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## CFD Blog Feeds

### Another Fine Meshtop

► The Connector Newsletter for 2017 Q1
23 Feb, 2017

The 2017 1st quarter issue of our The Connector newsletter is now available on our website at www.pointwise.com/theconnector/2017-Q1. This issue features articles on

► This Week in CFD
17 Feb, 2017

## Applications and Events

• CFD investigation of the flowfield in a swimmer’s wake. (Registration required.)
• The 2nd OpenFOAM French Users Meeting will be held on 21-22 March 2017 at the ISAT Engineering School. [Please pardon any factual errors due to my poor translation of the French language.]
• Boom Technology is using CFD as part of their effort to bring back supersonic transoceanic air travel.
• Keynote speakers from Boeing, Porsche, and more were announced for this summer’s NAFEMS World Congress.
• The Call for Papers is now open for this September’s International Meshing Roundtable in Barcelona. Full papers are due 30 May.

Increased combustion accuracy is coming in STAR-CCM+ v12.02. Be certain to watch the video at the link. Also read the article because if a kid thinks it’s real fire, it’s real fire. [Just like if a kid says you’re fat, you’re fat.] Image from Siemens PLM.

## Servers

• Tecplot’s SZL server, available in Tecplot 360 2017, provides remote data access via a client-server architecture. Learn more from the video at the link.
• Pointwise’s Glyph server, recently released as part of Pointwise V18.0 R2, provides the ability to use virtually any scripting language (e.g. Python, Perl) to execute Glyph commands via a client-server architecture. There’s a Glyph server webcast scheduled for 22 February.

Reduce fuel consumption by 11% using plasma? Read all about it from Symscape.

## News and More News

• Jacobs in Hunstville seeks to hire a CFD engineer.
• Inside HPC shares this video and slide deck on the topic of exascale computing using Fortran. [I recall 30 years ago being told that while we didn’t know what programming languages would look like 30 years in the future, we did know that they’d be called Fortran.]
• Digital Engineering’s article on Engineering the Software User Experience is a worthy read. In it they discuss four UI themes which I quote directly here:
• make the model the menu;
• reduce the initial barriers to entry;
• progressive disclosure; and
• customization is always an option.
• NVIDIA reported revenue of US$2.17 billion in the calendar quarter ending January 2017. Statements in the article about their financial performance indicates in AI and related applications. Nothing about CFD or CAE. • But there is something about CFD in DEVELOP3D’s article about NVIDIA’s new double precision Quadro GP100. • Envenio interviewed Resolved Analytics’ Stewart Bible about CFD, cloud computing, optimization, and more. • [I only recently discovered] CrunchYard, a cloud service provider targeting CFD. “Computational modeling will become a very important part of the personalization of medicine.” Hear this and more in a fascinating look at Dassault Systemes’ Living Heart Project. Image from Dassault Systems. ## Software In what probably should be my CFD application of the week, this screen shot is from a McDonald’s video showing their use of CFD to design the STRAW, an advanced straw for suctioning up their new chocolate shamrock shake. As first seen on Engadget. One month later and I would’ve chalked this up as April Fools fodder. At least they’re not taking themselves too seriously. ## More Reticulation Painter Kyle Sorenson focuses on “representing urban and metropolitan spaces through various forms of geometric abstraction.” The painting shown below caught my eye because it has been painted on birch wood creating just enough tension between the organic and inorganic. Kyle Sorenson, Reticulation, 2013. Image from artist’s website. See link above. Bonus: We talk a lot about topology in mesh generation. So I found the video Who (else) cares about topology? quite interesting. You may too. However, this clock composed of a triangular array of 15 circles is beyond my comprehension. P.S. No This Week in CFD next week because it’s time to visit the mouse again. ► Survey Results: Best 3-D Pan, Zoom, & Rotate 13 Feb, 2017 We asked “Which software product has the most intuitive 3-D pan, zoom, and rotate tools, in your opinion.?” The results are in and you like SOLIDWORKS the best. This highly unscientific poll (can you say “sample bias”?) yielded some interesting commentary too. • The best is “whatever I am used to.” This respondent must be a very practical person. And they also help prove the adage “Only a poor musician blames his instrument.” • 3D Connexion.” This wise user knows that a two-handed approach to 3-D software often works the best: traditional mouse for clicking in the GUI and a 3-D mouse from 3D Connexion for all the panning, zooming, and rotating of the 3-D model. This user probably also knows that Pointwise has supported 3D Connexions’ products for many years (see our supported hardware web page). • Make it configurable.” I agree, but this will be a case of do what I say not what I do. We hope to eventually make the 3-D controls fully user-configurable, but for now you have a handful of preference settings for mouse and keyboard controls. • Rotate.” I’m sorry, Sir, but this was not a popularity contest. (But I too probably would’ve chosen rotate over pan and zoom as my favorite.) • Minecraft.” I suppose I could log all my gaming time to “research.” • I think the default in Pointwise is pretty terrible.” And this, Madam, is why we ran the survey. Thank you to everyone who responded. Would you like to try the meshing software that tied for 2nd place for most intuitive pan, zoom, and rotate tools? Start your no-obligation evaluation of Pointwise today. ► This Week in CFD 10 Feb, 2017 ## Software • There are some very cool charting and graphing features coming in STAR-CCM+ v12. What they call Chart Highlighting includes hover effects, leader lines, multi-series editing, and more. • Tecplot’s SZL technology (for smaller files and faster performance) has been integrated into NASA’s FUN3D solver. • Esteco launched Volta, their enterprise product for access, sharing and reuse of simulation data in a multi-disciplinary environment. • I just recently learned about Aither CFD, an open source, multi-block, structured grid RANS solver. Column gas density from the “world’s highest resolution [10,048**3] simulation of turbulence ever done.” Image Federrath et al. Click here for paper. ## News Screen capture of a video on RealFlow’s website showing an example of their fluid simulation work – in this case a TV ad for Disney’s World of Color show. See link below. [Was there any doubt this is the example I’d use here?] ## Events • TFAWS 2017 (Thermal & Fluids Analysis Workshop) will be held 21-25 August in Hunstville. • The 3rd Gmsh Workshop will be held 29-31 March in Lanzarote. [Being a geography-challenged American I had to research Lanzarote to discover it’s the eastern-most Canary Island and is nicknamed the “island of eternal spring.”] • Website for the 12th OpenFOAM Workshop is now up. • A video of Dr. Peter Vincent’s presentation, Toward the Industrial Adoption of GPU Accelerated CFD, from last year’s GPU Technology Conference is now online. [Full disclosure: Pointwise is mentioned.] Screen capture from a video discussing the application of CFD to surfboard design. Read the article (which links to video). ## Applications Thermal simulation of an integrated circuit die. Image from Mentor Graphics. See link above. ## A CFDer’s Artist Art doesn’t get more directly applicable to CFD than this. Mark J. Stock’s work “explores the tension between the natural world and its simulated counterpart.” In particular, his video work entitled Smoke Fire Water (see image and link below) hits very close to home for me. In the artist’s own words: “Virtual fluids are nothing of the sort. To define a virtual fluid in 0s and 1s requires an underlying data structure (as does defining anything digitally). When stripped of all normal visual context, a fluid reveals this (computational) structure. These are the building blocks upon which virtual simulations of reality are based.” I highly recommend you explore the artist’s website. Mark J. Stock, Smoke Water Fire, 2008. This is a screen capture of the video. MUST WATCH. Bonus: The photo below is neither a simulation nor an experiment. Fluid Porcelain is just what it sounds like: a porcelain bowl designed and produced by Aylin Bilgic [sorry about the lack of diacriticals] to have the appearance of a fluid. I can’t imagine this being shipped to a buyer and arriving in one piece but if any one of you decides to make a purchase, send me a photo when you receive it. Fluid Porcelain by Aylin Bilgic. Image from Behance. See link above. ► Pointwise User Group Meeting Showcases Pointwise V18 6 Feb, 2017 From the 2016 Q4 issue of The Connector: Attendees at the Pointwise User Group Meeting 2016 were the first people to get hands-on training with the new Pointwise V18. In addition, they saw technical presentations on applications, scripting, higher-order meshing and future development plans. (more) ► This Week in CFD 3 Feb, 2017 ## All About MSC • The big news in the CAE business this week is MSC Software’s acquisition by Hexagon AB for US$834 million. MSC is one of the most storied names in the CAE business making its future very significant for the industry.
• From MSC Software themselves.
• Monica Schnitger thinks “it’s a perfect match.”  She also reports from MSC that “No change to MSC’s current product roadmap are planned.”
• From DEVELOP3D: MSC helps Hexagon achieve their “smart connected factory vision in discrete manufacturing industries such as automotive and aerospace”

## Software & Meshes

• ANSYS 18 was launched and includes ANSYS CFD Enterprise which itself includes all CFD solvers, several specialized solvers, geometry, meshing, and optimization. (See image below.)
• Some thoughts from SIMULIA on democratization of simulation.
• ANSYS also weighs in on democratization in ENGINEERING.com including 5 tips for choosing the right CFD software. #3 Are numerous training materials available live and online?
• SimScale shares news about recent updates including a mesh quality report.
• The EnSight blog features an article on mesh quality [one of my favorite topics].
• Very cool [but very brief] article from 3D Systems about 3D scanning the Apollo F-1 engine. [Click the link just to see the pictures.]

Screen capture from a video illustration of harmonic CFD analysis now available in ANSYS 18. See link above.

## Applications & Events

Optimized spillway design as simulated in FLOW-3D. Image from Flow Science. [Another beautiful CFD image.]

STAR-CCM+ simulation of blood flow. Image from a Siemens PLM article on use of simulation in medical clinical trials. [I fell in love with this image the moment I saw it.]

## Order from Chaos (via Oaths)

From the title of her work shown below (Before After Oaths Gray 4), I can only assume that Marjorie Welish has generated meshes. Because when I get a mesh like the right half of her painting there are a lot of oaths uttered before I can get it into the form on the left.

Marjorie Welish, Before After Oaths Gray 4, 2013

As originally seen on Art News, this painting is from the artist’s exhibition at ART 3 Gallery in Brooklyn. I urge my friends in the NYC area to visit before the exhibition closes on 05 February. Failing that, explore the artist’s website to see these works, her Mondrian-esque paintings, and more.

### F*** Yeah Fluid Dynamicstop

► If you place a droplet on a surface much hotter than its boiling...
24 Feb, 2017

If you place a droplet on a surface much hotter than its boiling point, that droplet will skitter and float almost frictionlessly across the surface on a thin layer of its own vapor. This is what is known as the Leidenfrost effect. But you don’t have to heat a surface to get this behavior. There’s also an aerodynamic Leidenfrost effect, shown above, when the surface is moving. As the surface moves, it drags a layer of air along with it, and that layer of air is capable of keeping droplets aloft indefinitely. The thickness of the air layer depends on speed; the faster the plate moves, the thicker the air layer underneath droplets. The aerodynamic forces generated are large enough to drive a droplet up an incline against the force of gravity (bottom image). (Image credit: animation - M. Saito et al., source; chronophotograph - A. Gautheir et al., pdf)

► Cephalopods, especially octopuses, are fascinating creatures. At...
23 Feb, 2017

Cephalopods, especially octopuses, are fascinating creatures. At sea level, an octopus can generate an impressive pressure differential of 1 to 2 atmospheres with each of its suckers. That incredible grip is possible thanks to fluid dynamics. An octopus’s sucker consists of two main parts: the ring-shaped infundibulum on the outer surface and the inner, cup-shaped acetabulum. When the infundibulum makes contact with a surface, it creates a water-tight seal. The octopus then contracts radial muscles along the acetabulum. This expands the inner chamber. The water trapped in the acetabulum now has to take up a greater volume, causing the pressure to drop and creating suction. To let go, the octopus simply relaxes the radial muscles or contracts circular ones to reduce the chamber volume and release the suction. (Video credit: Deep Look)

► When a vortex ring impacts a solid wall (or a mirrored vortex...
22 Feb, 2017

When a vortex ring impacts a solid wall (or a mirrored vortex ring), it expands and quickly breaks up. The animations above show something a little different: what happens when a vortex ring hits a water-air interface. As seen in the side view (top image), the vortex starts to expand, but its shear at the interface generates a stream of smaller vortices that disrupt the larger vortex. (They even look like a little string of Kelvin-Helmholtz vortices!) When viewed from above (bottom image), the vortex ring impact and breakdown look even more complicated. Mushroom-like structures get spat out the sides as those secondary vortices form, and the entire structure quickly breaks up into utter turbulence. There’s some remarkable visual similarities between this situation and some we’ve seen before, like a sphere meeting a wall and drop hitting a pool. (Image credit: A. Benusiglio et al., source)

► Australian photographer Warren Keelan captures spectacular...
21 Feb, 2017

Australian photographer Warren Keelan captures spectacular photos of waves just before and during the moment they break. Fluid dynamics is defined by motion – specifically the motion of substances that do not hold a single form - but one thing I love about wave photography is how crisp and solid water appears when frozen in time. In a way, it feels like a reminder that, even though we classify matter into different states, ultimately those states have a lot in common. (Image credit: W. Keelan; via Colossal)

► Despite its proximity, Venus remains largely mysterious, thanks...
20 Feb, 2017

Despite its proximity, Venus remains largely mysterious, thanks to its cloudy atmosphere and incredible harsh conditions. A recent study using data from the Japanese satellite Akatsuki revealed an enormous bow-shaped wave in the Venusian atmosphere. The wave appeared at an altitude of about 65 km and stretched more than 10,000 km long, across both the northern and southern hemispheres. Although surface winds on Venus are believed to be small due to its incredibly slow rotation, winds higher in the atmosphere are much faster – so it was strange to observe this wave sitting essentially stationary for five days of observation.

When the scientists mapped the location of wave relative to the surface, they found it was sitting over the Aphrodite Terra highlands, suggesting that this structure is a gravity wave generated by winds interacting with the topography. Similar, albeit smaller, gravity waves are often observed on Earth near mountains. The finding raises questions about our understanding of Venusian atmospheric dynamics and exactly how disturbances from surface winds could create enormous structures so high in the atmosphere. (Image credit: T. Fukuhara et al.; h/t to SciShow Space)

► What you see here is the formation of clouds and rain - but it’s...
17 Feb, 2017

What you see here is the formation of clouds and rain - but it’s not quite what you’re used to seeing outside. This is an experiment using a mixture of sulfur hexafluoride and helium to create clouds in a laboratory. Everything is contained in a cell between two transparent plates. Liquid sulfur hexafluoride takes up about half of the cell, and when the lower plate is heated, that liquid begins evaporating and rising in the bright regions. When it reaches the cooled top plate, the liquid condenses into droplets inside the dimples on the plate, eventually growing large enough to fall back as rain. The dark wisps you see are areas where cold sulfur hexafluoride is sinking, much like in the water clouds we are used to. Setups like this one allow scientists to study the effects of turbulence on cloud physics and the formation of droplets. (Image credit: E. Bodenschatz et al., source)

Boston-area folks! I’ll be taking part in the Improbable Research show Saturday evening at 8 pm at the Sheraton Boston. Come hear about the Boston Molasses Flood and other bizarre research!

### Symscapetop

► Plasma-Powered Drag Reduction for Trucks
13 Feb, 2017

What if you could reduce the drag of a tractor-trailer truck by 23% and therefore reduce fuel consumption by 11%? Further, what if the only modification to the trailer was to apply some decals and hook up an electric power source? According to Plasma Stream Technologies these savings are viable if the plasma flow control they are developing fulfills its promise.

Improve the Pressure Recovery Behind a Trailer using PlasmaGeometry courtesy of Nectar Design

► Rise and Fall of Winglets in MotoGP
2 Feb, 2017

MotoGP is the pinnacle of motorcycle racing, just like Formula 1 is to car racing, so it should come as no surprise that aerodynamics is a key element for success. The focus in MotoGP was on drag reduction through streamlined fairings, helmets, and outfits. Then in 2015 Ducati added winglets to their front fairings to produce downforce.

Winglets Come and Go in MotoGP

► Fluid Dynamics in 2016
9 Jan, 2017

In our third and final review post for 2016, catch up on general fluid dynamics topics that will ensure your future is so bright you'll have to wear shades in 2017.

McLaren 2010 MP4-25: F-duct carLicense: CC BY 2.0, Andrew Griffith

► Lessons in CFD from 2016
6 Jan, 2017

In our second review post for 2016, here are lessons and guidance to make your Computational Fluid Dynamics (CFD) more productive in 2017. "Those who do not learn [from] history are doomed to repeat it."

CFD Simulation Results Revealed by Transparency

► CFD Simulations of 2016
29 Dec, 2016

As 2016 draws to a close it is review time. Here are the Computational Fluid Dynamics (CFD) simulations that made 2016 so colorful, just in case you missed them the first time around.

CFD Simulation of Air Speed Surface Contours

1 Dec, 2016

Having previously determined how Santa makes his deliveries using a Reality Distortion Field, now we have also figured out how he selects the perfect gifts for each and every CFDer.

### CFD Onlinetop

► How to solve this
23 Feb, 2017
Quote:
ssssssssssssssssssssssssssssssssssssssssssssssss
► How to solve this : openfoam define a zero value volscalarfield
23 Feb, 2017
Quote:
 Originally Posted by LUIS1717 Hi everyone, I'm trying to program a solver and its need a volscalarfield that initially must be zero everywhere in the mesh and I don't want to read it from any initial condition or to write it down. Anyone know how to do it? thanks in advance Luis
ddddddddddddddddddddddddddddddddddddd
► Incompressible LES tutorial (oodles) - time averaged statistics
23 Feb, 2017
ggggggggggggggggggggggggggggggg
Quote:
 Originally Posted by bernarde Good day I have completed the Incompressible LES tutorial (oodles pitzDaily). Viewing the results in paraFoam gives me the field values at discrete time steps throughout the simulation. However, I would like to view the average over say the last 100 steps, to view the statistics of the solution. Is there a way I can do this in paraFoam? Also, how is convergence judged in this example? Normally I judge conversion in a LES by looking at velocity at a probe point and observing the instance when this velocity starts fluctuating around a constant value. Any help on this greatly appreciated. Regards
► using fieldAverage library to average postprocessing
23 Feb, 2017
Maybe useful for postprocessing

Quote:
 Originally Posted by eelcovv Dear programming experts, I am stuggelling with the following: I want to obtain the average of the fields already create in the time directories (i.e., during postprocessing). During runtime, this can be done via the functions in the controlDict by adding libfieldFunctionObjects.so No I want to do the same as a postprocessing step. My starting point was the postChannel application, that loops over the fields and collaps the field data that is assemed to by homogeneous. This tools requires Umean to be present. I want to create the Umean fields by a similar postprocessing tool called for instance postAverage This new tool (if it has been developped yet, please tell me) should read the controlDict dictionary, and than read the averaging information I took out all the functionality of postChannel (the collapsing of the fields) and I changed the line Code:  // For each time step read all fields // forAll(timeDirs, timeI) while(runTime.loop()) with the while runTime.loop construction, the controlDict is read and therefore the library is included. However, if I run this I get the complaint From function Foam::fieldAverage::initialize() in file fieldAverage/fieldAverage/fieldAverage.C at line 102. Of course this makes sence, because I do not do an explicit creation of the fields. Now the simple solution would be: explicity create the field U in the postAverage. However, this would make the utility less generic. I want it automatically to create the field that are defined in controlDict function fieldAverage. My idea was to include the fieldobject and reuse the code for creating fieldAverageItem. Unfortunately, I can not add or reuse the library in an external code, and therefore I can not reuse this functionality. I am relatively new to C++, but I that the power of it lies in reusing code, therefore I am quite sure this must be possible (I moreover, is desirable) Hopefully there is an OpenFoam programming expert out there who could give me a hint how to do tackle this problem. Or, which would be even better: is there somebody of has written a postprocessing utility to do the averaging of the time steps over a certain time range? Any hints very much appreciated! Regards, Eelco
► Pressure units in incompressible solvers
23 Feb, 2017
Quote:
 Originally Posted by Per Hi I am quite new to OpenFOAM, and have some basic questions about units. From the tutorial cases I see that the units for pressure in incompressible solvers (e.g. simpleFoam) are m^2/s^2. Which make sense since pressure is constant. I guess I then have to scale (divide) my pressure initial and boundary conditions with rho in order to get a correct solution? My real question is: can I define my pressure units to be kg/ms^2 and define density rho in the transportProperties file and get the same result? I want to be able to do this in order to avoid having to scale my pressure. Thanks in advance for replies. Per
openfoam implements in incompressible solvers pressure as p/rho (kinematic pressure).
nu = mu / rho must be specified in /constant/transportproperties because Re= U * D / nu will determine the flow pattern in the no-dimensional sense.

Solving adimensional N-S and rescale with U_ref, D_ref chosen and rho (phsical property, dimensionally independant from U_ref and D_ref [demanded by Pi-theorem], only that rho do not adimensionalise rho. but in the derivation of Froude number [https://en.wikipedia.org/wiki/Froude...in_application cauchy momentum equation], we use a rho_ref to adimensional rho, rho is then used to get rid of pressure), we get the U_ref to adimensionalize U, D_ref/U_ref to adimensionalize t and rho*U_ref^2, rho*U_ref^2 for pressure, D_ref for variables of position (x y and z).

Substitution these to N-S and divide by U_ref^2/D_ref, we get the non-dimensionalized N-S. With only one no-dimensional nu/(U_ref*D_ref) number resides with viscous term (1/rho before pressure gradient is eliminated by choosing rho*U_ref^2 for pressure).

If we choosed p0 for p. There will be something before grad(p) which will be - p0/(rho*U_ref**2). And since these are reference parameter,
► Prgramming BC
23 Feb, 2017
The beginning of a openfoam programming

Quote:

### curiosityFluidstop

► Equations for Steady 1D Isentropic Flow
5 Dec, 2016

The equations used to describe steady 1D isentropic flow are derived from conservation of mass, momentum, and energy, as well as an equation of state (typically the ideal gas law).

These equations are typically described as ratios between the local static properties (p, T, $\rho$) and their stagnation property as a function of Mach number and the ratio of specific heats, $\gamma$. Recall that Mach number is the ratio between the velocity and the speed of sound, a.

These ratios are given here:

Temperature: $T_o/T = \left(1+\frac{\gamma -1}{2} M^2\right)$

Pressure: $P_o/P = \left(1+\frac{\gamma -1}{2} M^2\right)^{\frac{\gamma}{\gamma-1}}$

Density: $\rho_o/\rho = \left(1+\frac{\gamma -1}{2} M^2\right)^{\frac{1}{\gamma-1}}$

In addition to the relationships between static and stagnation properties, 1D nozzle flow offers an equation regarding the choked cross-sectional flow area (recall that the flow is choked when M=1.)

$A/A^* = \frac{1}{M}\left(\left(\frac{2}{\gamma+1}\right)\left(1+\frac{\gamma -1}{2} M^2\right)\right)^{\frac{\gamma+1}{2\left(\gamma-1\right)}}$

Some excellent references for these equations are:

• Gas Dynamics Vol. I – Zucrow and Hoffman – 1976
• Gas Dynamics – John and Keith – 2nd Ed. – 2006

► Establishing Grid Convergence
9 Sep, 2016

Establishing grid convergence is a necessity in any numerical study. It is essential to verify that the equations are being solved correctly and that the solution is insensitive to the grid resolution. Some great background and details can be found from NASA:

https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html

First, here is a summary the equations and steps discussed here (in case you don’t want to read the whole example):

1. Complete at least 3 simulations (Coarse, medium, fine) with a constant refinement ratio, r, between them (in our example we use r=2)
2. Choose a parameter indicative of grid convergence. In most cases, this should be the parameter you are studying. ie if you are studying drag, you would use drag.
3. Calculate the order of convergence, p, using:
• $p=\ln(\frac{(f_3-f_2)}{(f_2-f_1)}) / \ln(r)$
4. Perform a Richardson extrapolation to predict the value at h=0
• $f_{h=0}=f_{fine}+\frac{f_1-f_2}{r^2-1}$
5. Calculate grid convergence index (GCI) for the medium and fine refinement levels
• $GCI=\frac{F_s |e|}{r^p-1}$
6. Ensure that grids are in the asymptotic range of convergence by checking:
• $\frac{GCI_{2,3}}{r^p \times GCI_{1,2}} \approxeq 1$

So what is a grid convergence study? Well, the gist of it is that you refine the mesh several times and compare the solutions to estimate the error from discretization. There are several strategies to do this. However, I have always been a fan of the following method: Create a very fine grid and simulate the flow problem. Then reduce the grid density twice, creating a medium grid, and coarse grid.

There are several strategies to do this. However, I have always been a fan of the following method: Create a very fine grid and simulate the flow problem. Then reduce the grid density twice, creating a medium grid, and coarse grid. To keep the process simple, the ratio of refinement should be the same for each step. ie. if you reduce the grid spacing by 2 in each direction between the fine and medium grid, you should reduce it again by 2 between the medium and coarse grid. In the current example, I generated three grids for the cavity problem with a refinement ratio of 2:

• Fine grid: 80 cells in each direction – (6400 cells)
• Medium grid: 40 cells in each direction – (1600 cells)
• Coarse grid: 20 cells in each direction – (400 cells)

Velocity contour plots are shown in the following figures:

We can see from the figures that the quality of the simulation improves as the grid is refined. However, the point of a grid convergence study is to quantify this improvement and to provide insight into the actual quality of the fine grid.

The accuracy of the fine grid is then examined by calculating the effective order of convergence, performing a Richardson extrapolation, and calculating the grid convergence index. As well, (as stated in the article from NASA), it is helpful to ensure that you are in the asymptotic range of convergence.

What are we examining?

It is very important at the start of a CFD study to know what you are going to do with the result. This is because different parameters will converge differently. For example, if you are studying a higher order parameter such as local wall friction, your grid requirements will probably be more strict than if you are studying an integral (and hence lower order) parameter such as coefficient of drag. You only need to ensure that the property or parameter that you are studying is grid independent. For example, if you were studying the pressure increase across a shockwave, you would not check that wall friction somewhere else in the simulation was converged (unless you were studying wall friction as well).  If you want to be able to analyze any property in a simulation, both high and low order, then you should do a very rigorous grid convergence study of primitive, integrated and derived variables.

In our test case lets pretend that in our research or engineering project that we are interested in the centerline pressure and velocity. In particular, let’s say we are interested in the profiles of these variables along the centerline as well as their peak values. The centerline profiles of velocity and pressure are shown in the following figures:

Calculate the effective order of convergence

From our simulations, we have generated the following data for minimum pressure along the centerline, and maximum velocity along the centerline:

The order of convergence, p, is calculated with the following equation:

$p=\ln(\frac{(f_3-f_2)}{(f_2-f_1)}) / \ln(r)$

where r is the ratio of refinement, and f1 to f3 are the results from each grid level.

Using the data we found, p = 1.84 for the minimum pressure and p=1.81 for the maximum velocity.

Perform Richardson extrapolation of the results

Once we have an effective order, p, we can do a Richardson extrapolation. This is an estimate of the true value of the parameter we are examining based on our order of convergence. The extrapolation can be performed with the following equation:

$f_{h=0}=f_{fine}+\frac{f_1-f_2}{r^p-1}$

recall that r is the refinement ratio and is $h_2/h_1$ which in this case is 2.

Using this equation we get the Richardson extrapolated results:

• $P_{min}$ at h=0  -> -0.029941
• $V_{max}$ at h=o  -> 0.2954332

The results are plotted here:

Calculate the Grid Convergence Index (GCI)

Grid convergence index is a standardized way to report grid convergence quality. It is calculated at refinement steps. Thus we will calculate a GCI for steps from grids 3 to 2, and from 2 to 1.

The equation to compute grid convergence index is:

$GCI=\frac{F_s |e|}{r^p-1}$

where e is the error between the two grids and $F_s$ is an optional (but always recommended) safety factor.

Now we can calculate the grid convergence indices for the minimum pressure and maximum velocity.

Minimum pressure

• $GCI_{2,3} = 1.25 \times |\frac{-0.028836-(-0.025987)}{-0.028836}|/(2^{1.84}-1) \times 100 \% = 4.788 \%$
• $GCI_{1,2} = 1.25 \times |\frac{-0.029632-(-0.028836)}{-0.029632}|/(2^{1.84}-1) \times 100 \% = 1.302 \%$

Max velocity

• $GCI_{2,3} =1.25 \times | \frac{0.2892-0.27359}{0.2892}|/(2^{1.81}-1) \times 100 \% =2.69187 \%$
• $GCI_{1,2} = 1.25 \times |\frac{0.29365-0.2892}{0.29365}|/(2^{1.84}-1) \times 100 \% = 0.7559 \%$

Check that we are in the asymptotic range of convergence

It is also necessary to check that we are examing grid converegence within the asymptotic range of convergence. If we are not in the asymtotic range this means that we are not asymptotically approaching a converged answer and thus our solution is definitely not grid indipendent.

With three grids, this can be checked with the following relationship:

$\frac{GCI_{2,3}}{r^p \times GCI_{1,2}} \approxeq 1$

If we are in the asymptotic range then the left-hand side of the above equation should be approximately equal to 1.

In our example we get:

Minimum Pressure

$1.0276 \approxeq 1$

Minimum Velocity

$1.0154 \approxeq 1$

Applying Richardson extrapolation to a range of data

Alternatively to choosing a single value like minimum pressure or maximum velocity. Richardson extrapolation can be applied to a range of data. For example, we can use the equation for Richardson extrapolation to estimate the entire profile of pressure and velocity along the centerline at h=0.

This is shown here:

In this post, we used the cavity tutorial from OpenFOAM to do a simple grid convergence study. We established an order of convergence, performed Richardson extrapolation, calculated grid convergence indices (GCI) and checked for the asymptotic range of convergence.

As I said before, the NASA resource is very helpful and covers a similar example:

As well the papers by Roache are excellent reading for anybody doing numerical analysis in fluids:

► The Ahmed Body
7 Sep, 2016

The Ahmed body is a geometric shape first proposed by Ahmed and Ramm in 1984. The shape provides a model to study geometric effects on the wakes of ground vehicles (like cars).

Image highlights:

In this post, I will use simpleFoam to simulate the Ahmed body at a Reynolds number of 10^6 using the k-omega SST turbulence model. The geometry was meshed using cfMesh which I will briefly discuss as well. Here is a breakdown of this post:

1. Geometry Definition
2. Meshing with cfMesh
3. Boundary Conditions
4. Results

Note: I ran this case on my computer with 6 Intel – i7 (3.2 GHz) cores and 32 Gb of RAM. Throughout the simulation about 20-ish gigs of RAM were used.

## Geometry Definition

### STL Creation

The meshing utility cfMesh is similar to snappyHexMesh in that it depends on a geometry file of some type (.stl etc) to create the mesh. But it is different in that the entire domain must be part of the definition.

The ahmed body geometry can be found: http://www.cfd-online.com/Wiki/File:Ahmed.gif

For this simulation, I generated the geometry using SolidWorks. But this wasn’t for any particular reason other than that it was quick since I am familiar with it.

### Preparation for Meshing

Once you have an STL file, you could go straight ahead to meshing it with cfMesh. However, some simple preparations to the STL geometry can improve the quality of the mesh created, and make setting up the case easier.

In particular, when you create the STL file in SolidWorks (or your 3D modeller of choice)  it contains no information about the boundaries and patches. As well, cfMesh works best if the geometry is defined using a .fms or .ftr file format.

Use surfaceFeatureEdge utility to extract edge information and create a .ftr file. Firstly let’s extract edge and face information from our STL file. We also define an angle. This angle tells cfMesh that any angle change large than this (in our case I chose 20 degrees) is a feature edge that must be matched.

surfaceFeatureEdge volume.stl volume.ftr -angle 20

After we run this, the new file volume.ftr contains a bunch of face and patch information. 13 surfaces with feature edges were extracted. The first 6 (volume_0 to volume_5) are the boundaries of the simulation (inlet, outlet, ground, front, back, and top).

mergeSurfacePatches volume.ftr ahmed -patchNames '(volume_6 volume_7 volume_8 volume_9 volume_10 volume_11 volume_12)'

After running this command, volume.ftr now contains 7 patches. We are now ready to move on to setting up cfMesh.

## Meshing with cfMesh

### Set up meshDict file in the system folder

Similar to snappyHexMesh and blockMesh, cfMesh using a dictionary file to control its parameters; meshDict. In this dictionary file we will be modifying a few parameters.

Tell cfMesh what file is to be meshed:

surfaceFile "volume_transformed.ftr";

Set the default grid size:

maxCellSize 0.2;

Set up refinement zones:

We want to set up two refinement zones; a larger one to capture most of the flow further away from the body (including the far wake), and a smaller more refined one to capture the near wake and the flow very close to the Ahmed body.

objectRefinements
{
box1
{
cellSize 25e-3;
type box;
centre (2.4 0 6.5);
lengthX 1;
lengthY 1;
lengthZ 3.5;
}

box2
{
cellSize 5e-3;
type box;
centre (2.4 0 7);
lengthX 0.5;
lengthY 1;
lengthZ 2;
}

}

Set up boundaries to be renamed:

renameBoundary
{
newPatchNames
{
volume_0
{
newName ground;
type wall;
}
volume_1
{
newName back;
type wall;
}
volume_2
{
newName inlet;
type patch;
}
volume_3
{
newName front;
type wall;
}
volume_4
{
newName outlet;
type wall;
}
volume_5
{
newName top;
type patch;
}
ahmed
{
newName ahmed;
type wall;
}
}

Set up boundary layering:

We require boundary layer on both the Ahmed body, as well as the volume_0 patch. Recall that the Ahmed body is surface mounted!

boundaryLayers
{
patchBoundaryLayers
{
ahmed
{
nLayers 10;
thicknessRatio 1.1;
maxFirstLayerThickness 5e-3;
}
volume_0
{
nLayers 10;
thicknessRatio 1.05;
maxFirstLayerThickness 10e-3;
}

}
}

### Run cfMesh

We want to create a hex-dominant grid. This means that the 3D grid will consist primarily of hex cells. To achieve this we will use the cartesianMesh solver from cfMesh.

The results are shown below. The final mesh consisted of approximately 16.9 million cells. The majority of the cells were hexahedra (approximately 99%).

## Boundary Conditions for the Solver

For this case, we are going to run a steady-state RANS simulation using the kwSST model and the solver simpleFOAM. This is simply to demonstrate the running of the solver.

The boundary conditions used are summarized in the following table:

As you can see I have used wall functions for the wall boundary conditions. This is due to the very small cell requirements that would be required to resolve the boundary layer on the ground, as well as on the Ahmed body which is at a Reynolds number of one million.

## Simulation Results

The simulation took about a day and a half on 6 cores. Throughout the simulation, about 20 gb of RAM was used.

## Conclusions

In this post, we meshed and simulated a surface-mounted Ahmed body at a Reynolds number of one million. We meshed it using the open-source meshing add-on cfMesh. We then solved it as a steady-state RANS simulation using the kwSST turbulence model, and the simpleFOAM solver.

The results gave some nice figures and a qualitatively correct result! And it was pretty fun. cfMesh was extremely easy to use and required much less user input than its OpenCFD counterpart snappyHexMesh.

## Some references:

http://www.cfd-online.com/Wiki/Ahmed_body

Some papers studying the Ahmed body:

-See the reference on the above CFD Online page!

As usual please comment and let me know what you think!

Cheers,

curiosityFluids

► Oscillating Cylinder in Laminar Crossflow – pimpleDyMFoam
19 Jul, 2016

In this post I am going to simulate an oscillating cylinder in a cross-flow… just for fun… and to provide an additional tutorial case for those wishing to use some of the dynamic meshing features of OpenFOAM.

The case I am going to simulate is a cylinder in a Reynolds number 200 cross-flow (U=2 m/s, D=1 m, nu = 0.01 m^2/s), oscillating at a rate of 0.2 hz.

## Tutorial Files

Please let me know if the download does not work or if there is a problem running the tutorial files. Note: I ran this case in parallel on a relatively fast 6-core computer. It will take a long time if you try to run it as single core.

## Mesh Generation

For this simulation, I built a simple two dimensional O-grid around the cylinder which joined to a series of blocks making a rectangular domain. I built this using the native OpenFOAM meshing utility blockMesh (which I like a lot).

If you are wondering about the blockMesh set up… I intend to a blockMesh tutorial post… not that it’s all that necessary since the OpenFOAM manual covers it pretty well.

## Case Set-up

### dynamicMeshDict

When running a dynamic mesh case a solver runs as part of the solution and solves for the new grid at each timestep. Several are available in OpenFOAM and I am not really trying to do a full post on that right now. So I’ll just tell you that in this case I decided to use the displacementLaplacian solver.

Along with the solver one must define the coefficients that go with the solver. For most of the solvers this means setting the diffusivity for the Laplace solver. Since I am relatively new to these types of solutions I did what we all do… and turned to the great CFD online forum! A discussion in this post (http://www.cfd-online.com/Forums/openfoam-meshing/97299-diffusivity-dynamicmeshdict.htm) made me think that a good starting point would be the inverseDistance model for the mesh diffusivity.

In the end my dynamicMeshDict (which belongs in the constant folder) looked like:

dynamicFvMesh dynamicMotionSolverFvMesh;

motionSolverLibs ( "libfvMotionSolvers.so" );

solver displacementLaplacian;

displacementLaplacianCoeffs
{
diffusivity inverseDistance 1(cylinder);
}

The solver produces the grid movements through time as the simulation is performed. Here is what the mesh looks like while it is moving! I added the stationary mesh and the moving mesh. Click on them to get a clearer picture

### Boundary Conditions

The set up for this case is simple. An inlet boundary (on the far left) where I specified the incoming velocity and pressure (both are uniform fixedValue). The top and bottom boundaries are slip boundaries (but could also be freestream depending on your preferred set-up style).

The cylinder itself requires some special treatment because of the moving mesh. It must obey the no-slip condition right? So do we set it as uniform (0 0 0) ? No! The cylinder is moving! Luckily there is a handy boundary condition for this in the U file:

 cylinder
{
type movingWallVelocity;
value uniform (0 0 0);
}

For a typical simulation using pimpleFoam a p and U file would be all that are required. However, since we are doing a moving mesh simulation there is another parameter that must be solved for and requires boundary conditions… pointDisplacement.

For the pointDisplacement boundary conditions, we know that all of the outer edges should NOT move. Therefore they are all fixed with a type of fixedValue and  a value of uniform (0 0 0).

The cylinder however is moving and requires a definition. In this simulation we are simulating and oscillating cylinder. Since we are using the displacement solver the type is oscillatingDisplacement. We input and omega (rad/s) and an amplitude (m) in the following way:

 cylinder
{
type oscillatingDisplacement;
omega 1.256;
amplitude (0 0.5 0); // Max piston stroke
value uniform (0 0 0);
}

## Results

Yay! Now its time to look at the results. Well since I am not doing this for any particular scientific study… let’s look at some pretty pictures!

Here is an animation of vorticity:

Looks pretty nice! I personally have a big nerd love for vortex shedding…. I don’t know why.

Obviously if you intend to do any scientific or engineering work with this type of problem you would need to think very carefully about the grid resolution, diffusivity model, Reynolds number, oscillation frequency  etc. All of these were arbitrarily selected here to facilitate the blog post and to provide a nice tutorial example!

## Conclusion

In this post I briefly covered the set-up of this type of dynamic meshing problem. The main difference for running a dynamic mesh case is that you require a dynamic mesh solver (you must specify in the dynamicMeshDict), and you also require boundary conditions for that solver.

Let me know if there are any problems with this blog post or with the tutorial files provided.

► Shocktube – rhoCentralFoam TVD Schemes Test
18 Jul, 2016

Careful selection of TVD interpolation schemes is important for solving high speed compressible flows. In cases with discontinuities such as shockwaves and contact surfaces, these schemes help keep the simulations free of spurious oscillations. In this post I will use the shock tube tutorial case to test some of the available schemes in OpenFOAM (specifically rhoCentralFoam).

I am going to test the following schemes: vanLeer, vanAlbada, and Gamma (0, 0.5 and 1). To test them, I will first simulate the tutorial case without changing anything. I will then examine how each of the limiters respond to an increase in grid density and also a decrease in CFL (Courant-Freidrichs-Levy) number … also the amazing Canadian Football League :).

Here is a summary of what I’ve found… then you can read through all the plots after!

1. In all cases the results at a cell count of 100 showed oscillatory and therefore unsatisfactory results. As you should already know, grid resolution is important.
2. At a CFL of 0.2, vanLeer in rhoCentralFoam was inconsistent. What I mean by this is when using vanLeer, as you refine your grid the results actually get worse! This is not desirable in a numerical scheme for obvious reasons.
3. At a CFL of 0.1 vanLeer showed good results. This indicates that one should probably avoid using vanLeer unless you are committed to using small timesteps (more computing time).
4. vanAlbada is consistent at both CFL numbers tested. This indicates that by using vanAlbada, it is possible that a larger CFL could be tolerated (assuming sufficient grid density is used).
5. Gamma 0 – Corresponds to central differencing and therefore we expect it to be the least stable (and not total variation diminishing). This is confirmed by the results which are poor in all cases! Decreasing the CFL number helps but it is clear that different schemes should be used.
6. Gamma 0.5 – Corresponds to hybrid central and upwind (TVD) differencing. The results are much better than Gamma 0.  In fact, the results are very similar to the results of vanLeer.
7. Gamma 1 – Corresponds to fully TVD linear upwind interpolation. This scheme should be the most stable.  However, because it is only first order, higher grid resolution is required to get the best results. This is the compromise that must be made between stability and accuracy when using TVD schemes.

In my opinion, the two best options are vanAlbada and Gamma 1. vanAlbada most likely provides the best mix between accuracy and stability (at least for the case examined here). Gamma 1 provides the best option if stability is the most important thing to you. However, because it is first order you will need more cells to get the most accurate solution.

Details of the test case and all of the results are listed below! Hopefully somebody finds this useful. If I have anything wrong please let me know! If you want me to try more schemes or anything different let me know as well!

Cheers,

curiosityFluids

## Shock-tube Case

If you aren’t familiar with the shock tube case, it is the simple one dimensional problem where one side (the driver side) begins at a high pressure. The other side (the driven side) is at a lower pressure. When the simulation begins, the discontinuous initial conditions send a shock wave down the tube. Following the shock wave is a contact surface. Here is an animation of the simulation:

## vanLeer

CFL=0.2

• 100, 1000, 10000 cells

### CFL=0.1

• 100, 1000, 10000 cell

### CFL=0.2

• 100, 1000, 10000 cells

### CFL=0.1

• 100, 1000, 10000 cells

## Gamma

• ### CFL=0.2

• 100, 1000, 10000 cells
• ### CFL=0.1

• 100, 1000, 10000 cells

• ### CFL=0.2

• 100, 1000, 10000 cells
• ### CFL=0.1

• 100, 1000, 10000 cells

• ### CFL=0.2

• 100, 1000, 10000 cells
• ### CFL=0.1

• 100, 1000, 10000 cells

► Converging-Diverging Nozzle v2- rhoCentralFoam
14 Jul, 2016

Recently I read a CFD Online forum post where the accuracy of rhoCentralFoam was called into question.

The test case was the simple example of a converging diverging nozzle flow. In fact, I have already done a blog post regarding this. However, I tackled the problem using a large reservoir. In fact, it would have been more prudent to just simulate the nozzle and attempt to match the example from the NASA cfd benchmarking website:

http://www.grc.nasa.gov/WWW/wind/valid/cdv/cdv.html

So this is what I’ve done!

The case file can be found here:

The geometry in the case is an axi-symmetric converging diverging nozzle defined by a cosine function. I built the geometry and mesh in PointWise (just for the sake of time… I mean, I already did a detailed nozzle post). The result is here:

Three cases are calculated for comparison on the nasa site given above:

1. Subsonic flow at exit (Pe/Po=0.89)
2. Supersonic flow with shock wave in expanding section (Pe/Po=0.75)
3. Supersonic flow at the exit (Pe/Po=0.16)

## Case 1- Subsonic

For the subsonic case, it is important to remember that the outlet pressure MUST be set. Why? Because for a given nozzle geometry and stagnation pressure there is more than one possible solution since the flow is not choked!

The two most important boundary conditions here are the inlet and outlet. The inlet is specified using the totalPressure boundary condition (in my set-up P0=10000 Pa). You can also specify the total temperature. However, this will not affect the mach number or pressure profiles, only the velocity, density and temperature profiles. I set the temperature to be fixed at 298 K. The nozzle itself is an adiabatic, slip wall. The front and back are wedge types. The outlet is set to zeroGradient for velocity and temperature and the pressure is fixed to 8900 Pa (P/Po=0.89)

The results for pressure distribution and Mach number are shown here. Remember that since we are comparing P/Po and Mach number, it didn’t matter that I used a different pressure and temperature than the test case. I also included gifs showing the unsteady part of the simulation at the start.

Clearly the results match! Woohoo! One down, two to go!

## Case 2- Supersonic with shock in diverging section

Tip for supersonic flow solution: Initialize the problem as a shock tube! Then the throat is choked right away in the simulation and convergence to the solution is faster!

In this case we must also specify the outlet pressure. Why? Because there are multiple solutions possible! Changing the back pressure changes the strength and location of the shock in the diverging section! So the setup is identical to the subsonic case for the inlet and nozzle. The only difference is that the outlet is set to 7500 Pa (P/Po=0.75).  In order to speed convergence, we can use the setFields utility to set the LHS of the nozzle to the stagnation pressure, and the RHS of the nozzle to the outlet pressure.

The results for pressure distribution and Mach number are shown here:

Hurray! Two for two!

## Case 3- Supersonic Flow

The fully supersonic case is in fact the most simple. Recall that a supersonic nozzle is actually an initial value problem! This is in contrast to the subsonic cases, or cases with shocks, where they are boundary value problems (the outlet pressure controls the solution). But in order to achieve the supersonic flow solution we just set the outlet pressure to be zeroGradient. Additionally, we again start the solution as a shock tube.

So you might ask, but we were given a specified outlet pressure! Well, we CAN set this outlet pressure. But this unnecissarily restricts our solution. Having a back pressure very close to, but not exactly the same as the actual supersonic outlet pressure could make things difficult. It is much easier to let the solution converge to where IT THINKS the right answer is. Then, how closely we match the outlet pressure is in fact an indicator of the quality of our solution!

Here are the results:

Three for three! rhoCentralFoam has passed the test.

As always comment and correct me where my mouth (or keyboard) has made error.

Cheers,

curiosityFluids

### Hanley Innovationstop

15 Feb, 2017

Have you ever wish for the power to solve your 3D aerodynamics analysis problems within your company just at the push of a button?  Stallion 3D gives you this very power using your MS Windows laptop or desktop computers. The software provides accurate CL, CD, & CM numbers directly from CAD geometries without the need for user-grid-generation and costly cloud computing.

Stallion 3D v 4 is the only MS windows software that enables you to solve turbulent compressible flows on your PC.  It utilizes the power that is hidden in your personal computer (64 bit & multi-cores technologies). The software simultaneously solves seven unsteady non-linear partial differential equations on your PC. Five of these equations (the Reynolds averaged Navier-Stokes, RANs) ensure conservation of mass, momentum and energy for a compressible fluid. Two additional equations captures the dynamics of a turbulent flow field.

Unlike other CFD software that require you to purchase a grid generation software (and spend days generating a grid), grid generation is automatic and is included within Stallion 3D.  Results are often obtained within a few hours after opening the software.

Do you need to analyze upwind and down wind sails?  Do you need data for wings and ship stabilizers at 10,  40, 80, 120 degrees angles and beyond? Do you need accurate lift, drag & temperature predictions at subsonic, transonic and supersonic flows? Stallion 3D can handle all flow speeds for any geometry all on your ordinary PC.

http://www.hanleyinnovations.com/stallion3d.html

Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

► Avoid Testing Pitfalls
24 Jan, 2017

The only way to know if your idea will work is to test it.  Rest assured, as a design engineer your idea and designs will be tested over and over again often in front of a crowd of people.

As an aerodynamics design engineer, Stallion 3D helps you to avoid the testing pitfalls that would otherwise keep you awake at night. An advantage of Stallion 3D is it enables you to test your designs on the privacy of your laptop or desktop before your company actually builds a prototype.  As someone who uses Stallion 3D for consulting, I find it very exciting to see my designs flying the way they were simulated in the software. Stallion 3D will assure that your creations are airworthy before they are tested in front of a crowd.

I developed Stallion 3D for engineers who have an innate love and aptitude for aerodynamics but who do not want to deal with the hassles of standard CFD programs.  Innovative technologies should always take a few steps out of an existing process to make the journey more efficient.  Stallion 3D enables you to skip the painful step of grid (mesh) generation. This reduces your workflow to just a few seconds to setup and run a 3D aerodynamics case.

Stallion 3D helps you to avoid the common testing pitfalls.
1. UAV instabilities and takeoff problems
2. Underwhelming range and endurance
3. Pitch-up instabilities
4. Incorrect control surface settings at launch and level flight
5. Not enough propulsive force (thrust) due to excess drag and weight.

Are the results of Stallion 3D accurate?  Please visit the following page to see the latest validations.
http://www.hanleyinnovations.com/stallion3d.html

Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.
► Flying Wing UAV: Design and Analysis
15 Jan, 2017

3DFoil is a design and analysis software for wings, hydrofoils, sails and other aerodynamic surfaces. It requires a computer running MS Windows 7,8 and 10.

I wrote the 3DFoil software several years ago using a vortex lattice approach. The vortex lattice method in the code is based on vortex rings (as opposed to the horse shoe vortex approach).  The vortex ring method allows for wing twist (geometric and aerodynamic) so a designer can fashion the wing for drag reduction and prevent tip stall by optimizing the amount of washout.  The approach also allows sweep (backwards & forwards) and multiple dihedral/anhedral angles.
Another feature that I designed into 3DFoil is the capability to predict profile drag and stall. This is done by analyzing the wing cross sections with a linear strength vortex panel method and an ordinary differential equation boundary layer solver.   The software utilize the solution of the boundary layer solver to predict the locations of the transition and separation points.

The following video shows how to use 3DFoil to design and analyze a flying wing UAV aircraft. 3DFoil's user interface is based on the multi-surface approach. In this method, the wing is designed using multiple tapered surface where the designer can specify airfoil shapes, sweep, dihedral angles and twist. With this approach, the designer can see the contribution to the lift, drag and moments for each surface.  Towards the end of the video, I show how the multi-surface approach is used to design effective winglets by comparing the profile drag and induced drag generated by the winglet surfaces. The video also shows how to find the longitudinal and lateral static stability of the wing.

The following steps are used to design and analyze the wing in 3DFoil:
1. Input the dimensions and sweep half of the wing (half span)
2. Input the dimensions and sweep of the winglet.
3. Join the winglet and main wing.
4. Generate the full aircraft using the mirror image insert function.
5. Find the lift drag and moments
6. Compute longitudinal and lateral stability
7. Look at the contributions of the surfaces.
8. Verify that the winglets provide drag reduction.

Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

► Corvette C7 Aerodynamics
7 Jan, 2017

The CAD file for the Corvette C7 aerodynamics study in Stallion 3D version 4 was obtained from Mustafa Asan revision on GrabCAD.  The file was converted from the STP format to the STL format required in Stallion 3D using OnShape.com.

Once the Corvette was imported into Stallion 3D, I applied ground effect and a speed of 75 miles per hour at zero angle of attack.  The flow setup took just seconds in Stallion 3D and grid generation was completely automatic.  The software allows the user to choose a grid size setting and I chose the option the produced a total of 345,552 cells in the computational domain.

I chose the Reynolds Averaged Navier-Stokes (RANS) equations solver for this example.  In Stallion 3D, the RANS equations are solve along with the k-e turbulence model.  A wall function approach is used at the boundaries.

The results were obtained after 10,950 iterations on a quad core laptop computer running at 2.0 GHz under MS Windows 10.

The results for the Corvette C7 model  are summarized below:

Lift Coefficient:  0.227
Friction Drag Coefficient: 0.0124
Pressure Drag Coefficient: 0.413
Total Drag Coefficient: 0.426

Stallion 3D HIST Solver:  Reynolds Averaged Navier-Stokes Equations
Turbulence Model: k-e
Number of Cells: 345,552
Grid: Built-in automatic grid generation

Run time: 7 hours

The coefficients were computed based on a frontal area of 2.4 square meters.

The following are images of the same solution from different views in Stallion 3D.  The streamlines are all initiated near the ground plane 2 meters ahead of the car.

Top View

Side View

Bottom View

Stallion 3D utilizes a new technology (Hanley Innovations Surface Treatment or HIST) that enables design engineers to quickly analyze their CAD models on an ordinary Window PC.  We call this SameDayCFD. This unique technology is my original work and was not derived from any existing software codes.  More information about Stallion 3D can be found at:

Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

► Hydrofoil Analysis
2 Jan, 2017

One factor that drive the effectiveness and efficiency of keels, rudders and hydrofoils is the airfoil shape.  If the airfoil shape is not suitable for the job then the design will not perform satisfactory.

For example, let us take a look at a high performance keel.  Here, an astute designer might investigate the use of a NACA 6-series airfoil for the cross-sectional shape.  The reason for using this airfoil shape for the keel is, of course, the delay in transition from a laminar to turbulent boundary layer along the chord of the airfoil.  In an ocean race, this is important because larger regions of laminar flows on the keel produces less overall friction drag or water resistance.  Less drag wins races.

But suppose the designer uses the same shape on the rudder, what do you think might happen?  A possible side effect of using the 6-series airfoil might be loss of control.  Why?  The answer has to do with early separation (vs angle of attack) of 6-series airfoils.

Knowing the behavior and use of airfoil shapes in the design of keels, rudders and hydrofoils is one advantage that the best marine designers utilize over their completion.  Another, is the importance of three dimensions or 3D in a design.  Yes, airfoil behavior is only the first part of a successful aero-hydrodynamic design.  The knowledge of the performance of the design in 3-dimensions equips the designer with the exact size (width and length) and shape of winning keels, rudders and hydrofoils.

Anyone can design a keel that is too large (an overwhelming amount of drag) or too small (not effective).  However, the subtleties of the best designed planform shape that makes it a winning appendage is a closely held secret that is only divulged by accurate 3-dimensional analysis.

3DFoil/MultiSurface Aerodynamics is based on a novel numerical algorithm that couples the vortex lattice method (based on vortex rings) with a linear strength panel method.  Together, these two methods compute accurate values of the lift, drag (both profile and vortex) and moments.

3DFoil buit-it wing design tool makes it very easy for users to accurately design and analyze hydrofoil models.  The following video is a tutorial that shows the design and analysis of a L-foil.

3DFoil enables designers to test airfoil shapes, planform designs and stability (longitudinal and lateral).  The software is used to rapidly and accurately compute lift, drag, side force and lateral resistance for your designs.

3DFoil is the key to designing the most efficient and effective keels, rudders and hydrofoils.  The software has been used over the last decade to train engineering students at the US Coast Guard Academy .

Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

► Racing with Multiple Wings
31 Dec, 2016

The picture above shows a wing that I created using the built-in wing editor that comes with Stallion 3D.  The main wing is Selig S1223 airfoils while the remaining two airfoils are a NACA 4512 and a NACA 0012.  Since the wing is finite (2 meters in span with a 0.75 m chord), it produces less lift and more drag than is predicted by airfoil (2D) theory.  Accurate 3D predictions enables you to size engines/motors, fuel tanks and predict weight, range and endurance even before the vehicle is built.

Do you have the power to race with 3 Wings?

With more down force, you can corner faster and win races.  But what about power vs. drag on the straightaways?  Stallion 3D will  give you the wing arrangement to win every race. Find out the perfect wing settings to race on hot days, cold ones, wet ones and in windy weather.

Designing and analyzing 3D multi-element wings in Stallion 3D is exactly the same as in 3DFoil.  Stallion 3D has the identical wing creation tool that is available in MSA-3DFoil.  The big difference is that Stallion 3D analyzes the wing using the 3D Reynolds Averaged Navier-Stokes (RANS) equations and this enables you to predict lift, drag and down force for your multi-element designs with variable thickness, rotations and gaps for your race.  The following video show how to create and analyze a wing using the wing tool and the RANS solver.

Of course Stallion 3D can also import and analyze a CAD file in STL format created in any CAD software such as AutoCAD or Solidworks.  The software performs automatic grid generation directly on your CAD model.  It runs on your MS Windows (7,8 or 10) laptop or desktop computer.

Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

### FlowViz - Fluid Dynamicstop

► As this video says, ‘What does sound look like?’....
2 Aug, 2014

As this video says, ‘What does sound look like?’. This is one of those things that’s hard to visualise whether you’ve never thought to try, or you’re a hardened fluid dynamic nerd who’s seen hundreds of FlowViz videos. However this video shows it in unbelievable detail and clarity.

This is made possible due to the Schlieren flow viz technique, which allows us to see regions of air with different densities - as is so well explained during the video.

Something I find amazing is one of the simpler examples in this video - the speaker. Those waves spreading out are what our ears hear as sound, simply changing the frequency of the waves changes the note we here.

► A while ago you’ll have seen I posted some FlowViz of a...
24 Jul, 2014

A while ago you’ll have seen I posted some FlowViz of a special leading edge in one of our wind tunnels. It is one of the exhibits inspired by nature that was on show at the Royal Society’s Summer Science Exhibition. Well it has now gone all the way from theory to practice as McLaren used it on their rear wing this weekend.

What’s even better is that it fits in perfectly with this week’s look at stall and separation. Yes an F1 wing can stall just like the wings we’ve seen this week. The only difference is, that instead of losing lift, a car loses downforce. These wavy leading edges are probably aimed at stopping separation and stall more efficiently than by using vortex generators.

F1 teams use fluorescent paint like the picture HERE to see if their wings are stalling.

► As promised, how to avoid stall - well delay it at least. This...
23 Jul, 2014

As promised, how to avoid stall - well delay it at least. This video shows a fantastic real life example of using vortex generators, as visualised by the cotton tufts. Stall occurs when the flow over the wing separates.

Vortex generators work by mixing the fast moving air outside the boundary layer with the slow moving flow inside. This adds momentum to the Boundary layer and helps it remain attached. Adding these vortex generators post production can help improve an aircrafts performance.

These devices aren’t just used on planes but also on Formula 1 cars to manage the flow around the car.

► While searching for yesterday’s video I came across this...
22 Jul, 2014

While searching for yesterday’s video I came across this one. It’s not an unusual video but does demonstrate something that I still think is quite astounding - the air doesn’t always flow straight over the wing but can actually reverse it’s direction and flow towards the front of the wing.

This situation is called stall and occurs when the flow separates from the upper surface of the wing. (This can be seen well in yesterdays post at 3m05s & 4m49s onwards). This happens at either large angles of attack - like when a plane takes off - or when the plane’s air speed is too slow. At this point the plane loses a large amount of lift which is extremely undesirable as it may not be able to support its own weight.

In this video the first instance occurs at about 25s. The tufts attached to the wing allow us to see clearly the change in the flow direction.

There are ways to stop this happening however as we will see tomorrow!

► Back in the day they really knew how to make educational videos....
21 Jul, 2014

Back in the day they really knew how to make educational videos. I have previously shown a video explaining water waves and, after posting footage from our new FlowViz wind tunnel, I came across this gem.

A simple explanation of camber, flaps, stall, separation and slots for a basic aerofoil. There are a whole heap of these videos on youtube, check them out if you want to find out more!

► For those that can’t make it to the Royal Society Summer...
3 Jul, 2014

For those that can’t make it to the Royal Society Summer Exhibition of Science here’s a video to make up for it! This is what you can see in the wind tunnel on our stand. Despite being an old fashioned method of flowviz, it still gives great insight into the flow over a wing and it’s wake.

There is, a Karman Vortex street behind the wing at zero incidence and small angles of attack, clear separation as the incidence is increased and a large turbulent wake.

If you want to ask us any questions on this, like how the flow viz works, or anything else aero/fluid related we’ve got a Twitter Q&A session tomorrow from midday. Tweet to @aeflowcontrol with the hashtag #asksummerscience.

If you want to see some more of what we’re doing check out the links:  http://sse.royalsociety.org/2014/smart-wing-design/

### CFD and others...top

► 2016: What a Year!
3 Jan, 2017
2016 is undoubtedly the most extraordinary year for small-odds events. Take sports, for example:
• Leicester won the Premier League in England defying odds of 5000 to 1
• Cubs won World Series after 108 years waiting
In politics, I do not believe many people truly believed Britain would exit the EU, and Trump would become the next US president.

From a personal level, I also experienced an equally extraordinary event: the coup in Turkey.

The 9th International Conference on CFD (ICCFD9) took place on July 11-15, 2016 in the historic city of Istanbul. A terror attack on the Istanbul International airport occurred less than two weeks before ICCFD9 was to start. We were informed that ICCFD9 would still take place although many attendees cancelled their trips. We figured that two terror attacks at the same place within a month were quite unlikely, and decided to go to Istanbul to attend and support the conference.

Given the extraordinary circumstances, the conference organizers did a fine job in pulling the conference through. More than half of the attendees withdrew their papers. Backup papers were used to form two parallel sessions though three sessions were planned originally. We really enjoyed Istanbul with the beautiful natural attractions and friendly people.

Then on Friday evening, 12 hours before we were supposed to depart Istanbul, a military coup broke out. The government TV station was controlled by the rebels. However, the Turkish President managed to Facetime a private TV station, essentially turning around the event. Soon after, many people went to the bridge, the squares, and overpowered the rebels with bare fists.

A Tank outside my taxi

A beautiful night in Zurich

The trip back to the US was complicated by the fact that the FAA banned all direct flight from Turkey. I was lucky enough to find a new flight, with a stop in Zurich...

In 2016, I lost a very good friend, and CFD pioneer, Professor Jaw-Yen Yang. He suffered a horrific injury from tennis in early 2015. Many of his friends and colleagues gathered in Taipei on December 3-5 2016 to remember him.

This is a CFD blog after all, and so it is important to show at least one CFD picture. In a validation simulation [1] with our high-order solver, hpMusic, we achieved remarkable agreement with experimental heat transfer for a high-pressure turbine configuration. Here is a flow picture.

Computational Schlieren and iso-surfaces of Q-criterion

To close, I wish all of you a very happy 2017!

1. Laskowski GM, Kopriva J, Michelassi V, Shankaran S, Paliath U, Bhaskaran R, Wang Q, Talnikar C, Wang ZJ, Jia F. Future directions of high fidelity CFD for aerothermal turbomachinery research, analysis and design, AIAA-2016-3322.

20 Apr, 2016
The 64-bit version for the Linux operating system is now ready for you to download. Because of the complexities associated with various libraries, we experienced a delay of slightly more than a month. Here is the link again.

► Announcing meshCurve: A CAD-free Low Order to High-Order Mesh Converter
14 Mar, 2016
We are finally ready to release meshCurve to the world!

The description of meshCurve is provided in AIAA Paper No. 2015-2293. The primary developer is Jeremy Ims, who has been supported by NASA and NSF. Zhaowen Duan also made major contributions. By the way, Aerospace America also highlighted meshCurve in its 2015 annual review issue (on page 22). Many congratulations to Jeremy and Zhaowen on this major milestone!

The current version supports both the Mac OS X and Windows (64 bit) operating systems. The Linux version will be released soon.

Here is roughly how meshCurve works. The input is a linear mesh in the CGNS format. Then the user selects which boundary patches should be reconstructed to high-order. After that, geometrically important features are detected. The user can also manually select or delete features. Next the selected patches are reconstructed to add curvature. Finally the interior volume meshes are curved (if necessary). The output mesh is also stored in CGNS format.

We have tested the tool with meshes in the order of a million cells. But I still want to lower your expectation. So try it out yourself and let us know if you like it or hate it. Please do report bugs so that improvements can be made in the future.

Good luck!

Oh, did I mention the tool is completely free? Here is the meshCurve link again.

► An Update on the International Workshops on High-Order CFD Methods
9 Sep, 2015
The most recent workshop, the 3rd International Workshop on High-Order CFD Methods, took place on January 3-4, 2015 just before the 53rd AIAA Aerospace Sciences Meeting at the Gaylord Palms Resort and Convention Center in Kissimmee, Florida (Orlando). The workshop was co-chaired by H.T. Huynh of NASA Glenn Research Center and Norbert Kroll of DLR, and sponsored by NASA,  AIAA, DLR and the Army Research Office.

Participants came from all over the world, including students, researchers and practitioners from academia, industry and government labs. A wide variety of methods were covered by the attendees. The final agenda and other details from the Workshop are contained on the following NASA web site:

https://www.grc.nasa.gov/hiocfd/

There are still many unfinished businesses, including high-order mesh generation, robust error estimates and hp-adaptations, and efficient solution methods on extreme scale parallel computers. Please mark your calendar for the 4th Workshop which will take place in the breathtaking Greek island, Crete, on the 3rd and 4th of June 2016 just before the Eccomas / 6th European Conference on CFD (ECFD VI). ECCOMAS will feature a dedicated minisymposium, during which selected participants  will be able to present their results. The Workshop cases and other details are contained here:

http://how4.cenaero.be

Hope to see many of you in Greece!
► Happy Birthday to Academician Zhang Hanxin, China's CFD Pioneer
16 Aug, 2015
I did not know anything about CFD when I started college. My childhood dream was to be a fighter aircraft pilot. In the countryside where my parents taught schools (Jiangtou, Leiyang, Hunan), I often saw aircraft flying in the sky, and was fascinated by them. My fascination to aircraft grew even stronger because of a dream. When you are a young kid, the teachers' words are like the Bible. I still remember my physical education (PE) teacher telling us not to wear white clothes at night outside because enemy planes might see us from the sky, and even shoot us. That evening, I had the weirdest dream which I still remember to this day. In the dream, my best friend and I were playing and running in the rice field. Out of nowhere, a plane looking like a soucer appeared afar in the sky. Remembering what the teacher said during the day, I immediately gestured my friend to run for our lives, and we did. The soucer also spotted us, and started to chase and shoot at us. Suddenly, I was shot, and actually died in my own dream. The dream, however, continued with my friend running to my home, telling my parents that I was dead. I got to visualize what's happening lying there dead, without any pain...

I had to give up my pilot dream early in my childhood because I cannot handle spins and had a deep scar in my head. When I was admitted to National University of Defense Technology (NUDT, which, by the way, produced the fastest computer in the world right now according to top500.org), I chose the Department of Applied Mechanics, which had majors in rockets and missiles. I was put in the major of solid rocket engines. During my junior year, I decided to switch major to aerodynamics. Shortly after that, Academician Zhang Hanxin, who is a member of Chinese Academy of Sciences, visited NUDT, and introduced me to CFD, which I was immediately attracted to. In my final semester, I spent 3 months doing a senior design project at the China Aerodynamic Research and Development Center (CARDC) in Mianyang, Sichuan to study the artificial compressibility method developed by Professor Zhang. My direct project advisor was Mr. He Fangshang, who told me many stories about Academician Zhang. The favorite one was that Prof. Zhang remembered exactly which pages all the equations were on after he studied a book, for only once. The senior design project was a wonderful experience, which completely hooked me to CFD.

After obtaining my BS degree, I decided to pursue a MS degree at NUDT under the direction of Professor Zhang, who was also an adjunct professor of NUDT. I spent about three or four months in CARDC in 1986 to attend Professor Zhang's lectures on total variation diminishing (TVD) schemes before leaving for Glasgow University in Scotland to pursue my PhD degree sponsored by the Sino-British Friendship Scholarship Scheme. My PhD thesis title was "Total Variation Diminishing Schemes for Steady Flow Computations".

I am indebted to Academician Zhang for first introducing me to CFD, teaching me the basics, and inspiring me with his passion and dedication. His physics based approach to developing numerical methods has a long lasting impact on CFD development. Last month, a CFD workshop was organized in Mianyang, Sichuan, to celebrate Prof. Zhang's 80th birthday. It was a wonderful workshop with many excellent talks by top CFD researchers from all over the world.

Happy 80th Birthday and Best Wishes, Academician Zhang!
► Why is high order curved mesh important?
21 Sep, 2014
It is almost a year since my last blog! I really need to write something. Otherwise, whatever I wrote may become obsolete, soon.

Last week, the world held its breath to watch the independence referendum in Scotland. Because of personal history, I, too, followed the event with much interest. It is very remarkable that a very peaceful vote determined the fate and future of an entire nation. Yes or no, the Scots are to be congratulated on passionately exercising a sacred right  many other parts of the world can still only dream about.

Now back to high order CFD. Almost all finite volume (FV) CFD codes use linear control volumes (CV), cells or elements with straight edges. The geometry is approximated with linear triangles or quadrilaterals, and is thus "faceted". In a second order finite volume method, the solution is assumed linear within each CV, and discontinuous across the CV interfaces. This linear geometry representation is therefore compatible with the linear solution distribution without causing any significant accuracy loss as long as high curvature areas are properly resolved by the mesh.

As discussed earlier, high order methods are capable of achieving engineering accuracy on a much coarser mesh than the ones used in 2nd order FV methods. If a linear mesh is used in a high order simulation, the solution error caused by the linear boundary mesh may be 2nd order, thus destroying the high order accuracy. It is therefore critical to represent the geometry with quadratic or higher order elements supporting curved edges and faces. Both Gmsh and CGNS can handle high order elements.

Although high order elements have been used successfully in the finite element community, no commercial CFD mesh generators can produce high order meshes at present. The biggest challenge is the generation of highly clustered viscous meshes near high curvature regions. Additionally, high order meshes are usually much coarser than linear meshes. Robustness may become an issue in generating coarse meshes for complex geometries. I hope commercial mesh generators (Pointwise?) can conquer this challenge soon!

I believe high order mesh generation is now the biggest bottleneck in applying high order CFD in the design process (http://rsta.royalsocietypublishing.org/content/372/2022/20130318.abstract).

### ANSYS Blogtop

► Driving Toward the Future with AUTOSAR Compliant Software
23 Feb, 2017

Today’s automotive systems are more complex, smarter and more autonomous than ever before, featuring functionality that no one could have imagined 10 years ago. Advanced sensors and electronics control everything from a vehicle’s speed and position to its entertainment and communications technologies. Radar, cameras and other sophisticated electronics are increasingly being incorporated into consumer vehicles.

In fact, today, more than 60 percent of a car’s cost comes from its advanced electronics and software systems. Since many of the functions guided by electronic systems are mission-critical, it’s essential that all automotive systems work together with complete reliability. The tens of millions of lines of software code that control these systems must be flawless.

This presents a challenge for automotive systems engineers, since many of the technology systems and components are sourced from different suppliers. Engineers are challenged with the critical task of creating a robust, fail-safe system architecture, complete with controls that ensure the system’s consistent operation.

Most automotive systems engineering teams have been relying on manual processes, and generic tools such as Microsoft Excel™, to generate and verify this architecture. Because these tools are not created specifically for the task of automotive systems design, they do not support rapid or consistent engineering results. The associated manual processes can be extremely time-consuming and are subject to human error.

## Support for Automotive Industry AUTOSAR Standard

In 2003, a network of leaders in the worldwide automotive industry came together to create AUTOSAR (AUTomotive Open System ARchitecture) — a set of standards that define an open software architecture for automotive electronics.

AUTOSAR is a standard that’s gaining momentum worldwide and that promotes the standardization and reuse of automotive embedded software, electric and electronic components. AUTOSAR’s objectives are modularity and configurability. It defines a layered software architecture allowing integration of electronic control units (ECU) with configurability. And it enables function reuse from different suppliers. Standardized interfaces are defined between each layer, and standardized datatypes are defined. All entities connected to this AUTOSAR Runtime Environment (RTE) must comply to the AUTOSAR specification to ensure integration of the user specific software.

The AUTOSAR architecture consist of:

• Software components (SWC) for the application
• Basic software components for low-level services
• And a Runtime Environment

The ANSYS SCADE tools, and more specifically the brand new ANSYS SCADE Automotive Package, are used to design the software components, that are the application parts. From the ANSYS SCADE model, code can be generated in compliance with the AUTOSAR RTE required interfaces.

The detailed flow with ANSYS SCADE is as follows:

• It starts with the AUTOSAR application description for the software components to be implemented, using standard ARXML notation, by the system architecture teams
• The AUTOSAR model is imported into SCADE Architect, in which additional information related to the software architecture implementation can be added, by the software architecture teams
• AUTOSAR “runnables” are parts — software functions — of the SCADE model, that will be the running software, they are synchronized with SCADE Suite. As the synchronization is bi-directional, it ensures full consistency at any time between the software architecture and the implementation interfaces
• The embedded software model behavior is designed in SCADE Suite and the ISO 26262 certifiable code is automatically produced with SCADE Suite KCG code generator, by the software engineering teams
• Using that generated code, and additional AUTOSAR architecture level information, the AUTOSAR compliance wrapping code is also automatically generated by the SCADE Automotive Package, and used by the system/software integration teams to deploy on the AUTOSAR platform

Driving Toward the Future

Such a model-based approach to automotive system and embedded software design promises a range of benefits, including a significant increase in the productivity of engineering staff. As designers rely on an advanced tool to manage their code generation and verification tasks, new systems architectures can be launched much more rapidly.

Just as automotive electronics capabilities have advanced rapidly, the solutions used to design electronics systems should also reflect the latest thinking and best-in-class technology. Already proven effective in mission-critical applications such as aviation, nuclear power and rail transportation, ANSYS SCADE model-based solutions for generating control software code can now be applied in the global automotive industry. In the race to perfect autonomous vehicles, the new level of speed and efficiency enabled by such tools can help separate the leaders from the followers.

The post Driving Toward the Future with AUTOSAR Compliant Software appeared first on ANSYS.

► System Simulation for Model-driven Automotive Health Monitoring and Predictive Maintenance
22 Feb, 2017

Rather than just listing all the new capabilities for system simulation and analysis in the latest release of ANSYS Simplorer, I thought it would be interesting to share a cool example of how our systems capabilities have been applied to health monitoring of an automotive braking system. And along the way, I’ll highlight how the advancements in ANSYS 18 help our customers model and simulate systems such as these.

This example illustrates a physics-based system model intended to support health monitoring and predictive maintenance of automotive braking systems. And while this is an automotive example, our customers throughout different industries are developing similar capabilities to monitor and manage the performance of their products in operation — all in the name of improving safety, performance, and overall customer satisfaction.

Model-based approaches and physics simulation are powerful components of creating the digital twin of a physical asset in operation — a digital replica of the asset that is used to diagnose anomalies in performance and for predicting the state of health and remaining useful life of that asset. These insights can subsequently be used to optimize operational downtime, trigger preemptive maintenance, and mitigate costly failures.

Back to the example…the braking system model was created in ANSYS Simplorer, making use of a number of different modeling approaches and interfaces for assembling the essential dynamics and behaviors to simulate braking system performance during normal and abnormal operation.

The system-level model of an anti-lock brake system for predicting brake pad wear is modeled and simulated with Simplorer.

Reduced-order models (ROMs) are used throughout the braking system model, providing important source of detailed component behavior from 3-D physics simulation. The system model includes ROMs from ANSYS Maxwell for the electromagnetic behavior of the brake actuator and the magnetoresistive wheel speed sensor, and a ROM created from ANSYS Mechanical simulations that accurately model brake pad wear as a function of wheel speed and brake pressure.

System-level models for the braking actuator and wheel speed sensor are ROMs from electromagnetic simulations in ANSYS Maxwell.

Adding to the rich collection of ANSYS’ reduced-order modeling capabilities, ANSYS 18 includes the new Thermal Model Identification Toolkit, the Battery Design Toolkit (available from the ANSYS App Store), and the System Model Identification Toolkit, all used within Simplorer to create system models at higher levels of physics-based fidelity.

The hydraulic and pneumatic dynamics of the braking system were modeled using the new Modelica diagram editor in Simplorer 18, using the freely-available Modelica Standard Library as well as the Hydraulics Library and Pneumatics Library offered by Modelon.  We continue to expand support for Modelica within Simplorer, allowing users to combine their Modelica models with ROMs and other modeling formats, and to analyze them within Simplorer’s powerful simulation environment.

Detailed simulation in ANSYS Mechanical is used to produce a ROM that predicts brake pad wear during system analysis.

Finally, an antilock braking control algorithm was built with ANSYS SCADE and integrated into the Simplorer system model through the standard Functional Mock-up Interface (FMI).

Simplorer 18 features a graphical environment for creating Modelica models from a number of standard and commercial libraries.

In ANSYS 18, we have introduced support for FMI Co-simulation, allowing our users to integrate even more models from the continually growing list of FMI-compliant tools, and making it easier than ever to assemble complete system functionality within Simplorer.  Check out this short video for more highlights of how we’ve expanded ROM support and system modeling interoperability within Simplorer 18.

How is this system model used? As an example, we included the effect of a malfunctioning wheel speed sensor and simulated the resulting effect on system performance to observe the telltale signatures that would assist an automotive manufacturer in diagnosing a failing component. Further, we can use the system model to predict the amount of brake pad wear due to abnormal conditions in the system.

ANSYS SCADE is used to model control algorithms that are integrated into the system model using the FMI.

System simulation produces results to assess system performance under normal and abnormal conditions.

We could go into a lot more detail for this example…and we do.  We are presenting “A Simulation-Based Digital Twin for Model-Driven Health Monitoring and Predictive Maintenance of an Automotive Braking System” at the 12th International Modelica Conference in Prague, Czech Republic, on May 15-17, 2017. But for now, I hope this brief overview gives you a sense of how system simulation can be used to complement monitoring and diagnosis of actual products in operation, enabled through higher levels of physics-based model fidelity and flexibility.

Register for the ANSYS 18 System Simulation Webinar Today!

Learn more about what ANSYS 18 is delivering for system simulation and Simplorer in my ANSYS 18 Innovations Systems webinar on March 2.

The post System Simulation for Model-driven Automotive Health Monitoring and Predictive Maintenance appeared first on ANSYS.

► Solution Dependent Expressions for Fan Cooling Simulation with ANSYS AIM 18
21 Feb, 2017

In ANSYS AIM 18, design engineers have reason to be excited about increased functionality for fluids, structural, thermal and electromagnetics. While the foundational problem-solving functionality has existed since AIM 16, new functionality is being added in every release so AIM can better address niche applications. One such enhancement I’d like to bring to your attention is solution-dependent expressions for applications like fan cooling simulation. While this isn’t something I guarantee you’ll use in your everyday simulations, it is a powerful feature needed for certain calculations.

In most fluids studies, the general starting point in calculating flow through a pipe, for example, are the known quantities of inlet velocity and outlet static gauge pressure, or inlet and outlet pressure. These values are generally assumed and solver iterations will converge on an outlet velocity (or mass flow rate) among other calculated values.

Assigning inlet velocity and outlet static gauge pressure

However, these values are not known when considering things like fan cooling. In this case the mass flow rate and inlet pressure are dependent on a manufacturer’s fan curve (or “P-Q curve”) from testing. These fan curves show the relationship between the air volume and the static pressure resulting from loss due to the pressure applied to the inlet and the outlet of the fan.

ANSYS AIM can be used to determine the system resistance of fan cooling applications.

In previous releases of AIM this problem simply could not be solved. Design engineers were faced with two choices: either make faulty assumptions, or throw your hands in the air and rely upon a more experienced analyst to use more complex software, like ANSYS Fluent.

AIM’s Solution for Fan Cooling Optimization

But AIM 18 has a solution for this type of problem, and without sacrificing the ease of use for design engineers! It’s called “solution dependent expressions”. This new functionality essentially allows for an expression as a boundary condition, where the expression contains an output value.

Solution dependent expressions use an output variable to determine a boundary condition.

In this case, the outlet mass flow rate is used to determine the inlet pressure. In the previous example of fan cooled applications, the goal in the solution is to determine if the fan produces a mass flow rate that adequately cools the system. While the mass flow rate and pressure drop are unknown, the known values are supplier given test data points, typically shown through a graphical curve.

Static pressure curve data is usually supplied by fan manufacturers.

Using various data points along this curve and simple curve generation software like a spreadsheet, an equation can be generated that represents the P-Q curve. Once the resistance of a system is known — which AIM will intrinsically solve, then the operating pressure and flow rate are also known. Matching the data from AIM to the supplier’s curve, you’ll quickly know if a given fan will provide adequate cooling or will unnecessarily consume too much energy in cooling.

Velocity color map of a fan-cooled fuse box

Another example where solution dependent expressions are useful are in fluid storage tanks. Pressure increases with depth in the tank due to the weight of the fluid above. Thus, for a given tank, an expression for fluid pressure as a function of depth more accurately defines the boundary condition than a constant pressure on all interior walls.

To learn more about this functionality and other enhancements in ANSYS AIM 18, I invite you to take a look at the highlight page, register for an upcoming webinar, and try AIM in the cloud right now.

The post Solution Dependent Expressions for Fan Cooling Simulation with ANSYS AIM 18 appeared first on ANSYS.

► National Engineers Week – Dreaming of a Better World
20 Feb, 2017

In the United States, National Engineers Week is always the week in February which encompasses George Washington’s actual birthday, February 22. It is observed by more than 70 engineering, education, and cultural societies, and more than 50 corporations and government agencies. ~Wikipedia

When I graduated in 2005 with a Ph.D. in Engineering I did what many of us did at the time: flew to New York City to interview for Quant jobs. That is what was cool and sexy. Financial engineering, not engineering, was all the rage. How times have changed — for the better IMHO.

Today engineering students dream of designing life-saving devices, connecting the world with new transportation systems and innovating to lift people out of poverty. This did not happen simply because the “fashion of the week” changed. This happened because core innovation in telecommunication systems (Apple launched the iPhone in 2007), the IoT revolution (a term coined by Peter T. Lewis in a 1985 speech given at a U.S. Federal Communications Commission) is becoming a reality, sensors and MEMS are small and affordable to allow for the development of driverless cars, etc.

In honor of National Engineers Week, I want to share three stories of engineers pursing their dreams. A dream of making life better for people with chronic kidney disease, a dream of providing clean electricity for developing countries and a dream of creating a revolutionary, energy-efficient mode of transportation.

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Dr. Roy from UCSF works on developing the first surgically implantable artificial kidney. This would dramatically make life easier and better for many patient who have this disease. Dialysis is an great inconvenience and kidney transplants is limited because the number of available kidneys has remained stagnant for the past decade. I had the pleasure of listening to his presentation during our Innovation Conference last Fall in San Jose, CA. It was truly inspiring. You can see Professor Roy present the implantable artificial kidney in this video.

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Contours of water velocity with a new nozzle design

Ram Chandra Adhikari, a PhD student in Mechanical Engineering at the University of Calgary, is working on developing low-cost hydro turbines to deliver electricity in remote areas of developing countries. Even a small amount of reliable electricity makes a big difference for developing countries. It allows us to preserve and protect food sources from rotting too quickly, thus greatly improving standard of living. Ram used simulation to increase the efficiency of small hydro turbines that can be placed in Nepal. Read about Ram’s innovation in the blog he wrote entitled Improving Hydro Turbines for Developing Countries using ANSYS CFD.

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And for my final story recognizing National Engineers Week — last but not least of course. Take a look at the students participating in the SpaceX Hyperloop contest who dream of connecting people by developing a ultra high-speed transportation system. One hundred motivated and enthusiastic student teams are participating in the SpaceX contest. Watch this University of California-Irvine video explaining their journey designing a Hyperloop transportation system.

The post National Engineers Week – Dreaming of a Better World appeared first on ANSYS.

► Celebrating 20 Years of Solar Racing with ANSYS
17 Feb, 2017

On November 18, 2016, the Blue Sky Solar Racing team gathered at the MaRS Discovery District to celebrate our past achievements and to look forward to the future. We hosted a number of our industry sponsors, faculty supporters, and alumni who explored various displays on the team’s history including photos, trophies and artifacts from past cars. Four generations of cars were displayed at this event as well, including Cerulean (2007), Azure (2011), B-7 (2013) and Horizon (2015). It was an incredible way to celebrate the achievements of the past 20 years of Blue Sky Solar Racing with those who have been part of our journey.

Blue Sky Solar Racing originated in early 1996 as a design project by a couple of engineering students, the “Blue Sky Project,” and has since grown to be one of the largest student design teams in Canada. Through the hard work of students and the generous support of our sponsors, we have built 8 state-of-the-art solar cars and raced in 10 international competitions.

With a 20-year history of iteration-driven design, simulation has grown to play a key role in the Blue Sky Solar Racing team’s development cycle. As a student design team, we have limited prototyping resources. However, with the help of ANSYS we can be confident that our designs will work as expected. ANSYS has grown to play a key role in this process with each generation finding new ways to leverage the powerful tools provided.  ANSYS Advantage covered the story of our entry in the Australian 2013 Bridgestone World Challenge.

4 Generations of Solar Cars showcased

From CFD analysis with ANSYS Fluent to mechanical load analysis with Static Structural, rapid simulation enables us to quickly converge on an optimal design and make key decisions early in the process. In addition, ANSYS allows us to evaluate materials such as carbon, aluminum or Kevlar (to name a few) for key components such as wheel rims, suspension and chassis, saving considerable prototyping time.

Roll Cage

This year we were able to experiment with ANSYS Spaceclaim, a software program that was very effective for cleaning our CAD. Many meshing and manufacturing problems we had in the past were easily fixed using Spaceclaim. After two decades of realizing the value of simulation-aided design, the team has taken one step further and started running ANSYS on our in-house developed computing cluster. This has made it possible to accelerate the design process and design a car better than any before, a car that showcases the future of automotive technologies.

As the ninth generation of Blue Sky Solar Racing, we are excited to be working with the same mission to educate, innovate, and achieve excellence in worldwide competitions racing solar powered electric vehicles. Moving forward, we are focused on manufacturing and testing our ninth generation car for the Bridgestone World Solar Challenge in October 2017.

Thank you ANSYS for your support and we thank everyone who has helped us on our journey! Here’s to many more exciting projects and races ahead for Blue Sky Solar Racing!

Additional images we hope you’ll enjoy:

Wind Tunnel Testing at National Research Council Air Tunnel Facilities in Ottawa

American Solar Challenge 2016 – Horizon at Scott’s Bluff National Monument, NE

77 – Blue Sky Solar Racing’s team number, displayed with Nanoleaf Aurora Panels

Attendees look at past photos of the team

The post Celebrating 20 Years of Solar Racing with ANSYS appeared first on ANSYS.

► Chip-Package-System Workflow Breaks Down the Barriers in Electronics Design
16 Feb, 2017

Engineers are challenged to design modern electronic systems that operate at higher speeds with lower power with ever greater functionality in an ever shrinking footprint. These design challenges drive engineers to perform Chip-Package-System (CPS) co-design and analysis. However, the design flow is often unconnected, and design data is exchanged manually leading to slow design times and error prone design methodologies. ANSYS 18 breaks down the barriers between simulation domains and delivers a Chip-Package-System workflow that enables engineers to accomplish their work in a rapid and convenient way.

The ANSYS Chip-Package-System (CPS) design flow supports end-to-end simulation of electronic systems comprising printed circuit boards, IC packages, connectors and IC sockets. The automated flows reduce time-consuming manual setup and errors, and streamline the generation of system pass/fail metrics and system verification. This workflow is the foundation of the CPS solution, as it integrates IC models with package and printed circuit board simulation.

In ANSYS 18, we introduce a unique layout assembly capability that supports multiple IC packages, printed circuit boards and connectors in a single simulation. We’re advancing this capability by bringing transient circuit-based simulation directly into the layout, allowing engineers to perform system-level verification. We also extended ECAD-MCAD meshing efficiency and robustness, along with integration of simulation capabilities, into the HFSS 3-D Layout design flow.

Some of the most common challenges in performing complex system simulations are data sharing with IP protection, simulating large 3-D models and creating a schematic to wire models from different field solvers to better predict system time- and frequency-domain performance. All of these challenges are resolved via ANSYS 3-D Layout Assembly with ANSYS 3-D Components. ANSYS 3-D Components are self-contained HFSS models which may contain assemblies of model geometry, excitations, boundary conditions and parametric variables;  their IP is protected by encryption. Using 3-D Layout with integrated HFSS provides a layout assembly approach for connecting PCBs, ICs and arbitrary 3-D components.

With our new release, ANSYS HFSS 3-D Layout breaks the barrier between 3-D geometry and electrical layout geometry. Users can now simulate a complete system such as a laptop with CAD integration of connectors, modeled in 3-D, on a printed circuit board electrical layout. Complete system analysis using the Nexxim transient circuit engine and frequency domain field solver simulations from HFSS and SIwave, with their integrated driver and receiver models, can then be performed using an HFSS 3-D Layout assembly.

Maybe the biggest barrier in today’s high-speed digital designs is the effect of temperature. As power density within digital electronics products continues to increase, the ability to accurately predict electrothermal effects becomes paramount. Bridging the gap between the mechanical domain and the electrical domain requires a design flow that reduces learning time and can be easily adopted by industry.

ANSYS 18 delivers a CAD first approach to easily and accurately predict electrothermal results for both MCAD and ECAD geometries. We’re breaking the silos between electrical and mechanical engineering. Reliable electronics must meet requirements in both domains. ANSYS 18 can extract layers with a unique metal fraction mapping algorithm to automatically set up package/board designs for mechanical analysis.

For the first time, a design flow exists that can perform DC analysis, map Joule heating to a mechanical solver, and then produce temperature profiles with associated mechanical deformation and stress. It’s a chip-package-board solution that allows engineers to evaluate electrical, thermal and structural behavior. Reducing design time while improving accuracy has a significant impact on digital electronic products.

With ANSYS 18, signal integrity simulation has been turned inside out with electromagnetic field simulation. Instead of solving the circuits first and the electromagnetic physics later — the way it used to be done — modern simulation has placed physics-based solvers in the foreground, supported by circuit and system simulation.

Electromagnetic simulation of entire electronic systems can now be performed from a layout assembly. Advanced numerical methods, high-performance computing, new technologies for handling massive EDA data in an automated way, and multiphysics for determining thermal and stress effects have combined to make this powerful, advanced electronic design methodology possible. The barriers between IC, mechanical, thermal and electrical design have been significantly reduced.

Do you want to learn more about how ANSYS 18 has enhanced the Chip-Package-System workflow to address design challenges in power integrity, signal integrity, ESD, thermal and structural challenges? Discover how you can break down barriers in your own design flow. Join me on February 23rd when I will present an overview of the ANSYS 18 signal and power integrity suite during a 50-minute webinar. I look forward to discussing our advancements in simulation for high-speed digital design with you.

The post Chip-Package-System Workflow Breaks Down the Barriers in Electronics Design appeared first on ANSYS.

### Convergent Science Blogtop

► The Merits of Mechanism Reduction
13 Feb, 2017

Mount Everest grows about 4 mm a year1. Mostly, we accept that it’s 8850 m high. In this moving system, approximations help you get on with climbing the mountain rather than taking a tape measure to it.

Sometimes detail isn’t useful in the context you are in. Sometimes you’d rather use your resources on something other than on acquiring a detail you’re not going to use. You’ve got to have the option to choose what you spend your resources on. This is why I, an otherwise vocal proponent of getting a detailed and complete picture of combustion in the engine, would make an argument for mechanism reduction.

A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness.

Solutions to chemical kinetics are based on the mechanism that you’ve decided to use. While some of the information is critical, in many cases, you’d get good results without it all.

CONVERGE offers several methods to reduce your mechanism to maintain solution accuracy or to tune it compared to experimental data. CONVERGE has mechanism reduction not just for zero-dimensional and one-dimensional simulations but also for three-dimensional detailed chemistry solutions. CONVERGE even contains a dynamic mechanism reduction method that reduces the mechanism during a detailed chemistry simulation.

In this example, we reduced the LLNL Diesel Surrogate Detailed mechanism3 (2885 species, 11754 reactions) repeatedly using the CONVERGE mechanism reduction utility with varying error tolerances, which resulted in 32 mechanisms with different numbers of species (and reactions). We then ran zero-dimensional ignition delay simulations with these generated mechanisms. The difference between the ignition delay of the LLNL Diesel Surrogate Reduced mechanism4 and the original mechanism is larger than the difference between any of the CONVERGE-reduced mechanisms and the original.

More importantly, we saw a nearly linear decrease in the simulation wall clock time as the number of species was reduced. Keeping in mind that all the ignition delays were within 0.05% of the original, that’s a dramatic speedup. Now, on the scale of a single run, 0D simulations are fast. But if you want to run a couple thousand of these simulations for, say, genetic algorithm optimization, the computational time adds up pretty quickly.

For both reducing mechanisms for chemistry and curve fitting for plots, whether a simplified view is worth it comes down to the context. If we say a plot is nearly linear when the curve fits with an R2 of 0.9823, we are throwing away the 0.0177 that doesn’t fit. But it works to describe the system to the degree that’s needed in that moment. You need to have the ability to reduce that mechanism or fit that plot. So go out there. Take your ice pick. We’re not going to hobble you with a tape measure when you’ve got a mountain to climb.

1 http://www.nationalgeographic.com/features/99/everest/roof_start.html
2 Korzybski, A. Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics, Institute of General Semantics, 1933.
3 Pei, Y., Mehl, M., Liu, W., Lu, T., Pitz, W.J., and Som, S., “A Multi-Component Blend as a Diesel Fuel Surrogate for Compression Ignition Engine Applications,” Journal of Engineering for Gas Turbines and Power, GTP-15-1057, 2015.
4 Pei, Y., Mehl, M., Liu, W., Lu, T., Pitz, W.J., and Som, S., SME 2014 Internal Combustion Engine Division Fall Technical Conference, Volume 2: Instrumentation, Controls, and Hybrids; Numerical Simulation; Engine Design and Mechanical Development; Keynote Papers, Columbus, IN, USA, 2014.

► &lt;em&gt;Prediction&lt;/em&gt; or &lt;em&gt;Postdiction&lt;/em&gt;? In CFD, the Prefix Matters
23 Jan, 2017

If you’ve been following my blog series Toward Predictive Combustion, you know that at Convergent Science we are passionate about predictions. You’re probably thinking to yourself, “That’s obvious—you create CFD software!” But it’s actually less obvious than you might think. Much of the CFD that is performed today is still what I would call postdictive, even though it’s rarely referred to as such. According to Wikipedia, postdiction is the act of making a prediction about the past or explaining something after the fact. At Convergent Science, we use this term to explain how simplified models, extensive tuning, and offset errors are used to match CFD results to existing experimental data. This is a common process, especially for combustion simulations. As an example, here’s a typical postdictive procedure for internal combustion engine simulations.

1. Someone performs an experiment (typically not the CFD engineer).
2. The CFD engineer receives the experimental data (typically average pressure trace, apparent heat release rate, emission data, etc.).
3. The CFD engineer sets up the case, including the geometry, and runs the simulation. Uncertainties in the inputs can have a large influence on the simulation results, but it can be difficult to resolve these uncertainties. For example, for a diesel engine, how well known is the nozzle orifice diameter? The actual value doesn’t always match the nominal value. Is the CAD surface available for the intake and exhaust ports, or is a swirl ratio based on steady-state flowbench results used? How well known is the start of injection timing? What about wall temperatures? The CFD engineer often has to simplify the case (for example, neglect the intake by simulating only an engine sector) because of these uncertainties.
4. Likely the first simulation results don’t match the experimental data, and so the CFD engineer tunes the empirical spray and/or combustion models. By tuning I mean that the model constants are changed until the simulation results better match the experimental data. (As a side note, can we agree to quit calling these “constants”? Constant implies that they are never changed!)
5. The CFD engineer changes physical inputs (start of injection timing, EGR level, injection pressure, etc.) and hopes that simulations run with the tuned models capture trends in outputs such as emissions.

While there are advantages to this process (simulations typically run relatively quickly because of their coarse meshes, simplified domains, and empirical models), there are no guarantees that tuning will lead to simulation results that more closely match the experimental data. The final step in the process (identifying trends in outputs by running a series of simulations with the tuned models) doesn’t always work as well as desired. Moreover, the possibility of grid-dependent results from the coarse mesh often goes unchecked as the thought of recreating the grid is daunting.

In reviewing the above process, would you consider these simulation results predictions when a) it’s unclear if these simulations are using the correct physical inputs and b) the simulation results are predicated on the experimental data? I would argue that the simulations are postdictions because having the experimental results was critical to getting a “good” answer.

So, if this is a postdiction, then what’s a prediction? Imagine running a simulation with the exact physical inputs that would be used in the corresponding experiment, which has not been run. The simulation results predict the experimental results. A prediction is a forecast—an estimate of a future event—and it’s much more difficult to get right. It requires grid-convergent mesh settings (which are fairly straightforward with automatic and adaptive meshing), more of the domain (not just a sector) to be simulated, detailed combustion models, high order numerics, the inclusion of unsteady behavior, more physics, and typically much longer runtimes.

One of the key differences between postdiction and prediction is that in a prediction there should be much more confidence in the answer that the CFD is giving you. As a result, if the simulation results don’t align with the experimental data, you might be suspicious of the physical inputs rather than the physical models. This goes back to uncertainties in the physical inputs (“garbage in, garbage out”), which can be difficult to track down, but it’s well worth the effort.

So, which approach should you take for your next combustion simulation project? If you don’t have runtime constraints, a grid-converged mesh, detailed chemistry, and an LES turbulence model are your best bet for a predictive simulation. If you do have runtime constraints, a coarse mesh, an empirical combustion model, and a RANS-based turbulence model likely will get you a reasonable (postdictive) answer with a more affordable runtime. Keep in mind, however, that if tuning is required, the real runtime is the total time of all iterations simulated, not just the cost of a single calculation.

It is important to note that I’m not suggesting that predictive simulations never require tuning. Many state-of-the-art physical models still rely on some empiricism. On the flip slide, as long as you are aware of the errors, a postdictive approach can be successful for many types of CFD projects, and although many iterations may be required to tune the initial case, subsequent simulations may benefit from relatively short runtimes. The important thing is to be aware of what you’re running. Are your simulations predictive or postdictive? In CFD, the prefix matters.

► The Need for Detailed Soot Modeling in CFD
30 Nov, 2016

The United States Environmental Protection Agency (US EPA) has strict standards to control the emissions polluting the air. The latest tier 3 standards are set to start from 2017 and immediately require a lower sulfur content in gasoline. These new regulations will apply to most vehicles on the road, barring only some heavy-duty vehicles. As a consequence of the new tier of regulations, the US EPA expects a significant reduction in emissions by 2030, including almost an 8000-ton reduction in fine particulate matter levels (PM 2.5). These regulations will bring the US on par with California, Europe, and South Korea.

If we want to protect our environment and our lungs (or sell cars, for that matter), we must learn to adapt existing systems to work more efficiently and comply with regulatory bodies. CONVERGE CFD can simulate the fundamental physical and chemical processes involved in the various stages of soot formation (particle inception, coagulation, condensation, and soot surface reactions). You can use empirical and phenomenological models to quickly estimate emissions. For a more detailed and accurate simulation of soot formation, CONVERGE leverages the SAGE detailed chemistry solver in two detailed soot models. Note that the SAGE solver requires a detailed mechanism that includes PAH chemistry. To make things easier for you, CONVERGE contains a tool for mechanism merging that can combine two mechanisms.

Detailed soot modeling is applicable for a wide range of conditions and provides a host of data (including soot mass, number density, volume fraction, soot diameters, and surface area). Both of the CONVERGE detailed soot models are two-way coupled with the gas phase, meaning that soot formation affects gas phase chemistry and heat release, and vice versa. The two-way coupling means that your simulations results will provide a complete and accurate picture of soot formation as well as combustion.

The first detailed soot model, the Particulate Mimic (PM) model, uses the method of moments while the second, the Particulate Size Mimic (PSM) model, is based on sectional methods in which the solution is obtained by solving sections (i.e., bins) that contain particles of a similar size. The primary difference between the two is that the former assumes a particle size distribution function whereas the latter determines the particle size distribution in addition to the PM outputs.

CONVERGE Studio makes setting up these detailed models for complex soot formation and oxidation processes straightforward. To ensure that your PM and PSM simulations are as efficient as possible, CONVERGE includes acceleration strategies including multizone modeling and dynamic mechanism reduction. At the end of the day, the industry is moving toward a whole-system approach for simulating engines. No longer can we disconnect exhaust and emissions from combustion, since we don’t have the margin to accept the errors introduced by separate simulations. Even the US EPA considers the fuel and the vehicle a single integrated system. For accurate combustion and downstream simulation, it makes sense to use a single detailed chemistry solver to address the increasingly strict emissions challenges.

► Time-Savers in CONVERGE Studio
1 Nov, 2016

#### Local coordinates

Modern fuel-injected engines are geometrically complex, and such complexity makes every aspect of computational analysis more challenging. You might have passed off CAD duty to a colleague, and you can avoid discretizing the volume with CONVERGE’s automatic meshing and cut-cell capabilities, but there’s still the matter of injector configuration and setup. CONVERGE allows you to rapidly set up a local coordinate system (LCS) for each injector, avoiding the tedium and bookkeeping of manual coordinate transformations.

Consider the port fuel injected engine intake pictured here. The injectors (green) are not aligned with the global x, y, or z axes, nor are they aligned with each other. We will set up an LCS for each injector.

First, we use CONVERGE to calculate the spray axis by measuring the normal of the injector face and saving it to the Coordinate Cache. We also measure and save the centroid of the injector face. Then we go to Create > Coordinates and copy the cached normal vector, saving it as a new LCS.

Next we open the nozzle configuration editor and change the coordinate system from Global to your new LCS. The spray plume is now oriented along one of the axes of the LCS, and any edits or adjustments are clean and simple.

#### Surface refinement

CONVERGE has no trouble with surfaces that are discretized with very high aspect ratio triangles, but some other computational packages aren’t so forgiving. CONVERGE Studio provides a quick and easy tool to coarsen or refine a surface discretization, providing a triangulation that is nearly isotropic.

This process is as simple as selecting the triangles you want to replace and then going to Geometry > Create > Triangle. Select the Refine Triangles option and choose your target edge length. With but a moment’s work, you can create a nearly isotropic surface triangulation suitable for export to the most demanding third party software.

► Mathematical Acrobatics
31 Aug, 2016

The Swinging Sticks or Chaos Pendulum is the latest addition to the Convergent Science headquarters.

This kinetic sculpture embodies the essence of what makes CONVERGE CFD software unique: we can efficiently simulate chaos. Our CFD solver is mathematically and physically robust, and we allow the solver to predict where the mesh refinement is needed most.

Just like the Swinging Sticks, fluid flow in real-life, unsteady systems is complex and can be unpredictable. But just like the simplicity of the sculpture, CONVERGE provides you with a way to simulate chaos using a very simple set of input parameters.

A few weeks ago, this unrepeatable pattern concept was at the heart of a riveting company-wide discussion about cycle-to-cycle variation in unsteady systems. In a physical system, flow phenomena can vary from experiment to experiment, even when all of the initial conditions are identical, due to slight physical perturbations that grow and induce a larger effect on the flow as they propagate through the fluid domain. Some call this the butterfly effect.

Our discussion focused on how numerical perturbations can have this same effect on an unsteady system. These numerical perturbations can be caused by seemingly negligible rounding differences or changes to a random number seed. We are highly encouraged by our ability to predict this cycle-to-cycle variation in unsteady systems.

Despite how dry and technical “cycle-to-cycle variations in unsteady systems” sounds, the conversation was truly fascinating. Being in that room with a few dozen passionate PhDs, mechanical engineers, and CFD specialists was one of the most thought-provoking experiences I’ve ever had.

If your application requires you to predict complex and unsteady fluid flow behavior, we can help you. Email me at clayton.grow@convergecfd.com if you’d like to see what CONVERGE CFD can do for you.

► No Mesh, No Mess: Repairing Dirty Surfaces with Polygonica
23 May, 2016

For CONVERGE CFD simulations, you do not have to generate a volume mesh. The robust and efficient automatic mesh generation algorithm in CONVERGE does the work for you, accelerating your CFD workflow. Even though you do not have to generate a volume mesh, you may need to perform surface repair operations to ensure your CAD surface meets certain requirements. To make the surface repair process even easier, CONVERGE Studio v2.3 (the graphical pre-processor for CONVERGE) includes the Polygonica geometry libraries.

Diagnosis dock in CONVERGE Studio used to identify surface errors such as intersections and open edges. This surface has 2246 intersections and 40 open edges.

With an appropriate license, the Polygonica toolkit is integrated seamlessly into CONVERGE Studio v2.3. You have access to the Coarsen, Boolean, and Healing tools to quickly fix problems with a surface.

To demonstrate the efficiency of the surface repair process with CONVERGE Studio and Polygonica, let’s consider the following example. This geometry is of a two-stroke engine and is freely available via Solidworks Zen.

We use the Diagnosis dock in CONVERGE Studio to identify surface errors such as intersections and open edges. This surface has 2246 intersections and 40 open edges.

In the figure below, the error-free triangles are rendered in gray and the intersections and open edges are rendered in red. Many of the errors occur where the connecting rods meet the crankshaft.

The intersections, which may occur when creating an assembly from individual CAD parts, prevent the surface from meeting the requirements for CONVERGE. Manually repairing the surface would require a combination of moving, deleting, and recreating triangles, and would be time-intensive. Instead, we will leverage the Polygonica toolkit.

We can slightly coarsen the geometry with Polygonica to repair the intersections and open edges without a large reduction in surface fidelity. Coarsening a surface reduces the number of triangles based on the criteria that you specify. Polygonica’s powerful and efficient algorithms attempt to avoid intersections and open edges in the coarsened surface.

The original surface has approximately 295,000 triangles and we will reduce this number by about 5%. To use the Polygonica Coarsen tool, we open the Geometry dock in CONVERGE Studio. For the coarsening criteria, we set the minimum number of triangles to 280,000 and leave the other settings as the default options. Coarsen the entire surface.

In the above image, the left frame shows the original surface with the intersections in red. The right frame shows the coarsened surface with the intersections and open edges repaired.

After coarsening the surface, the Diagnosis dock indicates that there are no longer intersections or open edges. Once we address the remaining requirements for the surface, the surface is ready to simulate in CONVERGE–no meshing required.

Left: The spline shaft of the original geometry.
Right: The spline shaft after coarsening with Polygonica.

Polygonica makes repairing surface errors in CONVERGE Studio v2.3 much easier and further accelerates your CFD workflow.

Cut-plane view of the volume mesh as generated automatically by CONVERGE.

20 Feb, 2017

### Advanced microfluidic flow modeling using FLOW-3D

Mar 2, 2017M j, Y11:00am MST2017-03-02T18:00:00Zg:ia T1 hour

Convert to my timezone

Sorry, your location could not be determined.The CFD software FLOW-3D offers a unique blend of both free surface and internal flow modeling capabilities coupled with advanced multiphysics features that are particularly well suited to micro-scale fluid flow applications. This technical webinar will focus on presenting a broad overview of FLOW-3D’s modeling capabilities as applied to microfluidics, and will include an emphasis on particle sorting applications, including advanced electro, magneto and acousto-phoresis topics.

Keywords: surface tension, free surface, Lab-on-a-chip, micro-fluids, particle fluid interactions, sorting, acoustophoresis, bio-technology.

The post Advanced Microfluidic Flow Modeling appeared first on Flow Science.

► AFS/NADCA Ch. 30 Vendor’s Night
15 Feb, 2017

NADCA Chapter 30 and the American Foundry Society Southern California Chapter co-host the metal casting industry’s annual Vendor’s Night. California’s two largest metal casting chapters are coming together to recognize the leading vendors in the industry on March 9 at the Rio Hondo Golf Course in Downey, CA. Flow Science is proud to participate in this event for the third year in a row. Learn more about this event on the AFS/NADCA Ch. 30 website >

Flow Science is a NADCA and an AFS corporate member.

The post AFS/NADCA Ch. 30 Vendor’s Night appeared first on Flow Science.

► Water Resources: A comprehensive review of FLOW-3D’s modeling capabilities
9 Feb, 2017

FLOW-3D is a modeling tool that offers unique free surface modeling capabilities, as well as a vast array of advanced models that are tightly coupled to free surface hydraulics to include features such as scour and sediment dynamics, particulates, air entrainment, bubble dynamics, density flows, chemistry, and many more. Following a series of webinars that have focused on various aspects of civil infrastructure and environmental flow modeling, this presentation offers a comprehensive review of the modeling options that are currently available to water resource engineers using FLOW-3D.

The post Water Resources: A comprehensive review of FLOW-3D’s modeling capabilities appeared first on Flow Science.

► FLOW-3D/MP v11.2 Released
6 Feb, 2017

We are pleased to announce the availability of FLOW-3D/MP v11.2, which is synced with the latest performance enhancements of FLOW-3D v11.2. This version includes major new features, significant advances in solver performance as well as added user convenience features and performance improvements in the user interface. Featured developments include the expanded Particle Model, the new Dynamic Droplet Model and interactive geometry creation. Learn more about FLOW-3D/MP v11.2 >

The post FLOW-3D/MP v11.2 Released appeared first on Flow Science.

► Computational Fluid Dynamics (CFD) Engineer
6 Feb, 2017

Flow Science has an open position for a Computational Fluid Dynamics (CFD) Engineer.

### Description

The focus of this position is on providing first-rate customer support, services, training and documentation for our commercial, multi-physics CFD software product, FLOW-3D.

### Experience

Strong fundamentals in fluid dynamics and heat transfer are required. Experience in the use of a commercial CFD code, either in industry or university settings, will be a very positive attribute. Experience with Windows and Linux is required. Fortran 90 and/or C, C++ is a plus, but not required.

### Education

The candidate should have a Master’s degree in mechanical engineering or physics.

### Attributes

The ideal candidate for this position will have excellent oral and written communication skills, excellent interpersonal skills, and the ability to work both independently and as part of a team.

### Principal duties

• Customer Support and Training
• Provide timely and thorough solutions to customers’ requests for technical support, via telephone, e-mail, and web sessions.
• Participate in training classes for clients, including developing and updating the training materials, presenting class lectures, and making occasional trips to users’ offices to provide onsite training.
• Assist in preparation and updating of users manuals.
• Assure continuous improvements to FLOW-3D by developing relevant test problems and procedures. Work with the various development groups to ensure that the needs of our customers are being met.
• Work with sales team to help discuss and demonstrate product capabilities to potential clients.
• Work with marketing team on various tasks to help provide material promoting our products.

### Benefits

Flow Science offers an exceptional benefits package to full-time employees including medical, dental, vision insurances, life and disability insurances, 401(k) and profit sharing plans with generous employer matching, and an incentive compensation plan that offers year-end bonus opportunity.

### Contact

Resumes may be submitted via mail (Attention: Human Resources, 683 Harkle Road, Santa Fe, NM 87505), fax (505-982-5551) or email (careers@flow3d.com).

Applicants must be available to attend an onsite interview in Santa Fe, New Mexico.

The post Computational Fluid Dynamics (CFD) Engineer appeared first on Flow Science.

► Job Opening: Software Engineer
3 Feb, 2017

Our software, FLOW-3D, is used by engineers, designers, and scientists at top manufacturers and institutions throughout the world to simulate and optimize product designs. Many of the products used in our daily lives, from many of the components in an automobile to the paper towels we use to dry our hands, have actually been designed or improved through the use of FLOW-3D.

### Open position

Flow Science, Inc., in Santa Fe, New Mexico, has a job opportunity for a Software Engineer.

### General description

As a Software Engineer, you would use your object oriented programming skills to create and maintain the user interface between our simulation software and the end user. You’ll have an opportunity to combine your creative skills with your analytic skills to contribute to a tool which is used by customers around the world.

### Required education, experience, and skills

• Bachelors degree or higher in computer science.
• Experience using object oriented programming techniques using C++ or other object oriented language

• Qt

### Attributes of a successful candidate

• Conceptual/creative thinking and problem solving skills
• Excellent oral and written communication skills
• Ability to work in a collaborative environment
• Ambitious and highly motivated

### Benefits

Flow Science offers an exceptional compensation and benefits package superior to those offered by most large companies. Flow Science employees receive a competitive base salary, employer paid medical, dental, vision coverage, life and disability insurances, relocation assistance, 401(k) and profit sharing plans with extremely generous employer matching, and an outstanding incentive compensation plan that offers year-end bonus.

### Contact

Resumes may be submitted via mail (Flow Science, Attention: Human Resources, 683 Harkle Road, Santa Fe, NM 87505), fax (505-982-5551) or e-mail careers@flow3d.com.

The post Job Opening: Software Engineer appeared first on Flow Science.

### Mentor Blogtop

► Blog Post: CFD in Sport: a Rejoinder
24 Feb, 2017
One of my abiding engineering passions over the years has been CFD in Sport. Ever since joining Fluent Europe Ltd in 1992 in the Sheffield, England office I have been hooked on the subject, be it Motor Racing, Americas Cup Yachting (with Team NZ and Alinghi for several race cycles), assorted Summer & Winter Olympic sports, or various ball sports. Nearly 25 years ago upon joining Fluent, I discovered
► On-demand Web Seminar: FloEFD V.16 - What's New
23 Feb, 2017

See what’s new in FloEFD v16.

22 Feb, 2017
There are a lot of great resources and technical papers on Mentor.com but here is a short list of general sources where you can see how a product works, download trial versions in our Virtual Labs to check it out for yourself, or sign up for in-person training. FloTHERM and Xpedition PCB: 7-minute video that shows how FloTHERM interfaces with Xpedition PCB design software so that the data transfer barrier
► Blog Post: Predict Operating Temperatures of Computer Components with CFD
21 Feb, 2017
The white paper “10 Tips for Predicting Component Temperatures…A High-Level ‘How To’ Guide” describes how to predict computer component temperatures using computational fluid dynamics (CFD). Physics-based reliability prediction can relate failure rates of electronic systems to the magnitude of temperature change over an operational cycle (power-on, power-off, power-on, etc.), and the rate of temperature
► Blog Post: How to Frontload CFD to Simplify LED Luminaire Development
16 Feb, 2017
The presentation “Thermal Simulation Simplifies LED Luminaire Development” explains how mechanical engineers can develop models and test the heat dispersion properties of an LED luminaire design early in the design phase by frontloading computational fluid dynamics (CFD) with FloEFD. CFD can be used to simulate and conduct thermal analysis on an LED device in virtual form, providing much more flexibility
► Blog Post: How to Use 1D CFD as a Digital Twin for Hardware-in-the-Loop Systems
14 Feb, 2017
Providing comprehensive and repeatable system-level testing for embedded systems is a major economic and technical challenge confronting systems and test engineers. One solution is to use a software simulation model as a digital twin with real hardware components to perform hardware-in-the-loop (HIL) simulation and testing. This technique is essential in evaluating and verifying systems that cannot

### Tecplot Blogtop

► Webinar: Increasing History Match Efficiency with Tecplot RS
23 Feb, 2017

Register and get a link to the recording.

Optimizing the history match workflow is crucial in helping a reservoir engineer validate their simulation model. With the growing amount of data that needs to be analyzed, the need for both quantitative (XY Plots) and qualitative (visualization) results are required.

Jim Gilman, director of engineering at iReservoir, will walk through a typical history match workflow by first doing a bubble pie chart analysis and then by diving into some raw comparisons.

Tecplot RS, a visualization and analysis tool, has unique features that assist the engineer in fully understanding their history match.

We look forward to seeing you at the Webinar!

► SZL Server Introduction
14 Feb, 2017

## Benefits of using Tecplot SZL Server.

This video introduces the capabilities and benefits of Tecplot’s SZL Server, a remote data access capability. SZL Server is included with Tecplot 360 2017 and later versions.

### Overview

SZL Server is a client server architecture that provides remote access to SZL data. It uses our proprietary SZL format to transfer only the data required to generate a plot.

Other client-server architectures require loading the entire dataset on the server side, which can be slow for large data sets.

In comparison, SZL Server may load and transfer as little as 1% of the total data, reducing RAM and CPU resources on the server side. In addition no graphical resources are required on the server side, as all rendering is performed locally, which also eliminates rendering latency.

To access a SZL Server from 360, select File > Load Remote Data…

From here you can enter the appropriate login credentials to launch the server, and provide a key file to securely connect.  Once connected, you can select a data set to pull from the list of available files.

For those concerned about data security, SZL server includes an option to use an SSH bridge, including two factor authentication, for secure data transfer.

SZL Server requires a TecPLUS maintenance subscription, and is included in the Tecplot 360 software package.

This concludes the introduction for SZL Server. SZL Server is included with Tecplot 360, or you can download it from our website.

To learn more, take a look at the Installation Guide or contact support@tecplot.com

► Tecplot SZL File Output from FUN3D
9 Feb, 2017

FUN3D version 13, released in September 2016, supports writing binary files in the new SZL format.

FUN3D version 13, released in September 2016, supports writing binary files in the new SZL format. I’ve written in previous blogs about the benefits of SZL technology for visualization of large data files. With SZL technology, common visualization tasks are up to two orders of magnitude faster and data files are compressed by up to 50%.

This change offers additional benefits to FUN3D/Tecplot users, especially for those running in parallel on a HPC system, for these reasons:

1. Tecplot data is now written in parallel to a single file. Previous versions of FUN3D supported parallel output in Tecplot format by writing a file for each MPI Rank, resulting in a large number of files per CFD case. Not only was this cumbersome to manage, but it unnecessarily used additional inodes, which are limited on many parallel file systems.
2. TecIO-MPI uses MPIIO to write the data in parallel, which offers performance benefits when writing to parallel file systems.

### How to obtain TecIO libraries

There are now separate TecIO libraries for scalar and MPI-based parallel output. Both libraries are included in our TecIO Library. Selecting the “Download TecIO source code for Windows, Linux, and Mac” downloads a “tar” file that contains the scalar and MPI versions of TecIO in separate folders: teciosrc and teciompisrc. To build these libraries, follow the steps outlined in the readme.txt file located in each folder.

### How to build FUN3D with TecIO

The FUN3D documentation was not completely updated for the new version of TecIO. For example, it refers to tecio64.a (a name no longer used – all tecio libraries are 64-bit) and makes no mention of the TecIO-MPI library. However, the configure command in section A.7.12 of the FUN3D Manual is still accurate. Simply configure using

--with-tecio=/path/to/tecio

where “/path/to/tecio” is the path to the appropriate version of TecIO: teciosrc for scalar or teciompisrc for MPI parallel.

### Performance Considerations

TecIO-MPI uses MPIIO to write data in parallel to a single file. The parallel efficiency of this process is dependent upon a number of factors, including the parallel file system. If your performance is lower than you expect, you can try modifying system parameters. One user discovered that, for his Lustre file system, increasing the number of stripes in the folder you are writing to improves performance.

► PyTecplot for Tecplot Scripting
6 Jan, 2017

Paul Ferlemann is an aeronautical engineer and researcher at the Hypersonic Airbreathing Propulsion Branch (HAPB) of the NASA Langley Research Center. The HAPB performs research to develop advanced technology for hypersonic airbreathing propulsion systems for aerospace vehicles. The focus is on airframe-integrated engine concepts having high performance over a wide range of flight Mach numbers.

### The Challenge

Paul was working with a complex geometry composed of 112 distinct zones. The desire was to show only exposed edges. For structured data, specifying which edges of each zone to display can be done by adjusting border styles in Tecplot 360. The challenge comes in knowing which borders to show for each zone. Manually adjusting the style for 112 zones would be tedious, error prone and time consuming – not to mention that this process must be repeatable for various geometries, many of which are far more complex, having easily 1000s of zones.

Figure 1: All edges displayed (left). Only exposed edges displayed (right)

### A Solution

Paul chose to use the Tecplot 360 macro language to automate this task using an edge detection algorithm. Then using the algorithm results, the macro adjusted the edge border settings for each zone to achieve the desired result. While this solution worked, the Tecplot 360 macro language is not optimized for the data access required to determine shared edges. As a result, the script was over 800 lines, was difficult to author and debug, and took 18 minutes to run on this relatively small geometry.

Says Paul “At the time (2011), using the Tecplot 360 macro language to automatically set border styles to only display exposed edges was the only option and seemed fairly straight-forward, at least conceptually. While the macro did save “human” time (typically executed before lunch or even over night for a large case), it was frustrating that it could not be efficiently used interactively.”

### The PyTecplot Solution

Engineers at Tecplot rewrote Paul’s edge detection algorithm using PyTecplot, a Python API in Tecplot 360 2017 and later releases. Python is a compact and expressive language, and as a result the final script was less than 200 lines of code and executes in only 7.3 seconds!

Again, Paul: “The time savings demonstrated for the same algorithm implemented by engineers at Tecplot using PyTecplot is very dramatic. I look forward to using PyTecplot for Tecplot scripting projects in the future.”

### Conclusion

Compared to the Tecplot macro language, PyTecplot is easier to author, easier to debug, and faster to execute. PyTecplot is available to Tecplot 360 licensees with a TecPLUS subscription. Watch the introductory video and read through the documentation on our PyTecplot web page.

► Tecplot’s SZL Server, a Client-Server Solution for Visual Analysis of Remote Data
6 Jan, 2017

Chris Nelson is the Chief Scientist for CFD at Innovative Technology Applications Company, LLC. Recently, one of his focuses has been on solving complex problems in jet noise simulation.

### The Challenge

Chris runs his CFD cases on the ITAC computer in Missouri and a host of other remote high performance computing (HPC) systems. The jet noise simulations were mostly run on DOD HPC systems. He generally works from home, so he connects to the remote computer using residential internet. Aeroacoustics requires highly resolved grids and the solution is unsteady, so the resulting set of data files is too large to transfer over the internet to his local computer. The case he used to test Tecplot’s new client-server capabilities had 21 million grid points and flowfield snapshots at each of the 100 time steps.

### The Solution

In the past, Chris used remote display with Tecplot to perform his visualization but, because of network latency, it often performed poorly with his internet connection. Chris says “some time back I spent an oversized chunk of time trying to get a movie generated on one of the HPC sites using Tecplot. The remote systems would run Tecplot and display (very slowly) the frame-by-frame creation of the animation…” He was also frustrated that the HPC system only had older versions of Tecplot that didn’t generate correct animation files, and that newer versions were not installed as quickly as he needed. Chris says, “client-server lets you take control of the version of Tecplot that you use.”

### Tecplot’s SZL Server Solution

A client-server version is available in Tecplot 360 2017 and later releases. The system uses Tecplot’s SZL (subzone load-on-demand) technology to transfer only those subzones (small pieces of volume data) from a server on the remote computer to Tecplot 360 on the local computer. In most cases, this dramatically reduces the amount of data sent over the network. For the jet noise example, only 1% of the volume data file must be transferred to generate Chris’ animation.

Chris was an early tester of Tecplot’s SZL Server and had this to say: “Doing things with SZL Server will be both faster to display and will also completely get around the issue of weird install glitches on the remote site (for the version set up out there).”

The SZL Server works only with SZL format (*.szplt) files, so Chris is converting his PLOT3D files to *.szplt files on the remote computer. Chris: “It’s a lot easier to do a file conversion up where all the data is and then run SZL Server than to have to download everything to a local system and run local Tecplot 360 or try to run Tecplot 360 in a fully remote mode on the server.”

### Summary

Tecplot’s SZL Server technology allows users to analyze and visualize large remote datasets. SZL Server is available to Tecplot 360 licensees with a TecPLUS subscription.

► Tecplot 360 2017 Release 1 Has Major New Capabilities Including a Python API, Analytics and Remote Data Access
5 Jan, 2017

## Biggest release in 3 years is packed with new features, improvements and enhancements.

BELLEVUE (Jan. 5, 2017) – Tecplot, Inc., developer of the leading data visualization and analysis software for engineers and scientists, today announced the general availability of Tecplot 360 2017 Release 1.

Three major new capabilities are included in the 2017 release of Tecplot 360, including:

Chorus is a design space exploration tool that helps engineers discover trends and anomalies in computational fluid dynamics (CFD), or computational physics studies and enables them to simultaneously gain insight into the underlying fluid-dynamic phenomena that cause these variations – all in a single environment. Tecplot Chorus, originally released in 2012 as a stand-alone product, will now only be available with Tecplot 360. Learn more about Chorus.

PyTecplot is Tecplot’s new Python API. Python is a popular scripting language used by scientists and engineers to automate workflows. PyTecplot features an easy-to-use object-oriented approach to create plots and alter data. PyTecplot integrates fully with other Python-compatible tools. Learn more about PyTecplot.

SZL Server is a lightweight application that is installed on an HPC, data server or remote file store. The server side leverages Tecplot’s proprietary SZL technology to transfer only the data required to generate a specific plot. Data transfer is fast and secure, and no graphics card is required on the server side. Learn more about SZL Server.

These new capabilities in Tecplot 360 are available only to customers who subscribe to Tecplot’s new, expanded maintenance plan, TecPLUS™. Customers who are currently on maintenance (SMS) will be automatically upgraded to TecPLUS™. A free trial of Tecplot 360 2017 R1, including all new capabilities, is available for download on the company’s website.

“After many months of hard work, we are thrilled to be releasing these new capabilities in Tecplot 360,” Tecplot President Tom Chan said. “Tecplot Chorus will empower our customers to quickly explore large data sets composed of multiple solutions. Our new Python API, PyTecplot, enables customers to automate workflows across their entire tool chain. SZL Server will help our customers spend less time transferring their data and more time analyzing their results.”

Chan continued, “The customers we’ve talked to are very excited about this release. They see huge opportunities to not only increase the efficiency of their work but also improve the accuracy of their designs.”

There are even more improvements in Tecplot 360 2017 R1:

• Variable combining enables variables in data files that have the same meaning but different names to be combined.
• Slice orientation can be set by selecting three points on the desired cutting plane or by interactively probing the three points.
• A new algorithm of isosurface generation provides better visual results and improved performance.
• Data files can be converted to the new SZL (.szplt extension) format in batch mode without consuming a Tecplot 360 license.
• TecIO can now read SZL files as well as write them.

Tecplot 360 saw a major overhaul in 2014 with the release of Tecplot 360, making it the fastest, most memory-efficient CFD post-processor available for desktop computers. When loading modern high-fidelity CFD solutions, benchmarks show 94% less memory usage, 6.75 times faster time to first image, and 50% smaller file sizes compared to earlier versions of Tecplot 360. Results vary depending on the size and type of data. CFD engineers are now able to load and analyze data once reserved for only the largest high-performance computing centers.

Tecplot 360 users with current TecPLUS maintenance can upgrade to Tecplot 360 2017 Release 1 at no additional cost. Update Your Software or try Tecplot 360 2017 R1 for free.

Special pricing is available for Academic users upon request. Tecplot for Academics.

The software’s industry-leading speed – both computational and rendering — is achieved through Tecplot’s proprietary SZL technology, which is a combination of deferred data loading, exhaustive parallelization, and many other code optimizations. Learn more about SZL Technology.

Tecplot, an operating company of Toronto-based Constellation Software, Inc. (CSI), is the leading developer of Visual Data Analysis (VDA) software for engineers and scientists.

CSI is a public company listed on the Toronto Stock Exchange (TSX:CSU) that acquires, manages and builds software businesses that provide mission-critical solutions in specific vertical markets.

Tecplot’s products allow customers using desktop computers and laptops to quickly analyze and understand information hidden in complex data, and communicate their results to others via brilliant images and compelling animations. The company’s products are used by more than 47,000 technical professionals around the world.

Since its founding in 1981, Tecplot has consistently delivered category-leading innovation to the engineering and scientific communities. Examples of this never-ending innovation include Tecplot 360 for lightning-fast analysis of CFD simulation and test data, and Tecplot RS for oil & gas reservoir simulation.

Tecplot has been awarded numerous Small Business Innovation Research contracts from DOD, NASA, DARPA, and the National Science Foundation. In 2012, the company was named a Red Herring Top 100 Americas Award winner.

Tecplot, which is headquartered in Bellevue, Wash., has been named one of Seattle Business magazine’s “100 Best Companies to Work For” four times in the past five years.

### Schnitger Corporation, CAE Markettop

► Autodesk sells Moldflow’s US lab to Beaumont
24 Feb, 2017

Unless you’re a plastics guy, you may not know this, but one of Moldflow’s key differentiators is its material testing laboratories. Plastics are tricky, with fibers and layers and temperatures and shrinkage rates … designing a mold that can fill and cool to create the correct part relies on serious science to fully understand the material properties of possible polymer alternatives. Enter the materials labs, which test polymers in a number of real-world settings, using consistent methods and reporting. These tests inform Moldflow’s material database, which is then used in simulations.

Last week, Beaumont Technologies Inc. said that it had acquired Moldflow’s North American material characterization lab, and will partner with Autodesk to continue to provide material characterizations for Moldflow’s injection molding simulation tools. (Autodesk retains ownership of the Australian labs; more on that below.)  Beaumont has long partnered with Moldflow and Autodesk, providing its own material testing, training and consulting since the late 1990s.

Autodesk’s Greg Fallon, VP Simulation, said that the “partnership between Autodesk and Beaumont Technologies will lead to increased testing capacity and enhanced services through access to additional expertise in polymers, injection molding processes and dedicated local resources … With Beaumont handling the majority of our training and material characterization activities, Autodesk will focus on the development and validation of new process solvers, as well as characterization methodologies. This partnership is part of Autodesk’s growth strategy. The result will be a net increase in global characterization capacity, improved throughput and responsiveness as well as adding additional subject matter experts into our community.”

Why does this matter? Even if you’re not a plastics guy today? Because you likely will be or at least will need to consider plastics in your engineering process sometime soon. Plastics are molded, yes, but also used in a lot of 3D printing processes for fixtures, test parts and, increasingly, production parts. The science behind the materials becomes even more important as we try to simulate that production process and the parts it makes — and, from all accounts, we’re nowhere near ready to call that job, done. The Moldflow lab in Kilsyth, Australia, will continue to supply Autodesk customers throughout Asia and other global markets, and support on-going research and development of plastics modeling and simulation offerings. That may be even more strategic today, as Autodesk and its customers further explore the boundaries of 3D printing.

Terms of the deal were not given but it appears to be completed.

The post Autodesk sells Moldflow’s US lab to Beaumont appeared first on Schnitger Corporation.

► ANSYS realigns to go from $1B to$2B
23 Feb, 2017

ANSYS held its first earnings call under the leadership of new CEO Ajei Gopal today to discuss Q4 and full-year 2016 results and to offer investors a glimpse into where Mr. Gopal believes he can take ANSYS. One clear takeaway from the call: the goals are ambitious, as he told investors that he’s building a company that can grow from $1 billion to$2 billion in revenue. He gave no timetable, and the goals for 2017 are modest compared to that end-game, but make no mistake: that’s the ultimate objective. He’s going to lay out more of the details in an investor meeting in September but we can already start to see the outlines: more efficient sales people and processes, selling what the customer wants in the way the customer wants to buy it (meaning, stay flexible on licensing), an even broader product line, a combination of organic and acquired growth … But also a pruning of current resources so that ANSYS can reallocate resources to areas that it believes will contribute to higher growth in the future. That means a global, across-all-parts-of-the-company 4% reduction in headcount that’s to be completed by the end of Q2. As CFO Maria Shields told investors, ANSYS plans to focus on “product opportunity areas like additive manufacturing, digital twin, IoT, and even some of our core products.” But she also plans to invest in sales and other corporate infrastructure that will be needed as she and Mr. Gopal “scale the business from $1 billion to$2 billion”. I should also mention that ANSYS announced the resignation of Walid Abu-Hadba, Chief Product Officer, effective May 2017.

it all sounds a bit dire, but Q4 and 2016 actually were pretty good. You can get all of the details at ANSYS, but here are the factoids that struck me:

• Q4 GAAP revenue was $270.6 million, up 8% as reported while 2017 GAAP revenue was$988.5 million, up 5% as reported. Put those two together, and one sees that growth picked up in Q4, a trend ANSYS says continues into Q1 2017.
• Software license revenue in Q4 was $161.5 million, up 8%. Maintenance and services revenue was$109.2 million, up 7%.
• Lease license revenue grew 9% in Q4, a tiny bit faster than overall license revenue — meaning, like other PLMish players, ANSYS is seeing growing interest for leases
• Contribution continues to tilt towards direct (75% on Q4, 76% in F2016) but Mr. Gopal said that channel growth is a focus in Europe, so this may swing back a bit in fiscal 2017 and beyond.
• ANSYS reported signing 37 customers with orders of more than $1 million in Q4 (up from 34 a year ago), including seven customers with orders in excess of$10 million each. The company said that eight of these were enterprise agreements (bringing the total to 18 for 2016); these are part of ANSYS’ program to look at accounts more strategically, with a view towards “capturing pent-up demand for long-term enterprise vendor consolidation, particularly in North America.” The company describes these contracts as typically including “products from multiple product lines … and typically include training and other engineering services.” In other words, they’re helping customers consolidate on ANSYS solutions, potentially displacing competitors or in-house solutions, and offering services to make it all work.

For 2017, ANSYS basically reaffirmed earlier guidance, only making minor adjustments for currency fluctuations since November. For Q1, ANSYS sees revenue between $237 million and$246 million (which would be growth of 5% to 9% over last year) and, for 2017, ANSYS sees revenue of $1.010 billion to$1.045 billion, or up around 4%.

So a decent question is, how are you going to get to $2 billion in revenue at a growth rate of 4% per year? You can’t, at least not under one CEO’s watch. We can expect Mr. Gopal to announce acquisitions, and probably some really big ones, as he told analysts: “I’ve had some experience in driving M&A and I’ve seen the transformative opportunity that can be created by doing the right M&A. But I want to be thoughtful and make sure that we do the right M&A. As you know, the wrong deal can have a very negative impact on an organization. So absolutely, M&A is something that we continue to look at.” What other levers can Mr. Gopal pull, besides adding more products to sell? More sales resources to sell them — more focus on channel in Europe, more direct resources globally. Greater sales productivity. More users, both in enterprise accounts that already use some of the ANSYS portfolio, and in new accounts that ANSYS perhaps can’t reach now via a cloud offering that removes the compute power barrier to entry. ANSYS has cloud offerings (and continues work on a multi-tenant version); more details coming soon according to Mr. Gopal. A slight hitch in this growth path is the transition to leases –lower initial revenue from a lease than a perpetual could see revenue shrink from some customers– but let’s see what Mr. Gopal and his new VP of Sales come up with to get to$2 billion.

The post ANSYS realigns to go from $1B to$2B appeared first on Schnitger Corporation.

► MuM sees subs growth resuming in H2 2017
16 Feb, 2017

Earlier this week, Germany’s Mensch und Maschine Software announced preliminary results for 2016 — and they were solid, even with the perpetual to subscription transition it and its customers are working through. Total revenue in 2016 was €167 million, up 4%; M+M software revenue was €46 million, up 13%. The VAR business contributed €121 million, up 2%, helped by promotions run by Autodesk earlier in the year.

Towards the end of the year, MuM says, the proprietary software business dominated activities. The Open Mind CAM brand continues to drive that business. which contributes roughly 24% of group revenue but 42% of the group gross margin — in other words, it’s a nice, profitable business. CEO Adi Drotleff commented in the press release that MuM saw ‘[m]ore than 14% organic growth in proprietary software and service [which] is really exceptional.”

MuM also issued guidance for the next few years that shows how it, at least, sees Autodesk’s move to subscriptions playing out in the VAR ecosystem.

• For 2017, the “rental transition still retarding” sales, leading to a “weaker Q1”. Mr. Drotleff told German media that conditions should improve in the second, leading to growth for the year as a whole. “From August, when Autodesk is finally moving to the rental model, growth will pick up, as Autodesk’s business is back on track. On the whole, however, we see 2017 as being at the bottom of our current growth path.”
• By 2018, then, MuM expects to see a “positive rental impact” and a return to “stronger growth” for the business as a while of 11% to 12% (per the German interview)
• And, by 2020, MuM expects that the EBITDA contribution from both segments “should be equal”. MuM has long said that it sees M+M Software’s EBITDA leveling off at 25% (it’s at 23% in 2016); the VAR segment has the long-term potential to hit an EBITDA margin target of over 10% (was 4.2% in 2016).

Think about that trend line. We’ve got Autodesk-related software revenue declining through 2017 (as a result of the smaller-than-perpetual payments from existing clients) while revenue from the proprietary products grows. In 2018, MuM expects all software brands to be selling strongly again so that, by 2020, both parts of the business are contributing similar amounts to the bottom line.

Let’s talk EBITDA, earnings before interest, taxes, depreciation and amortization – in other words, the profitability of the business without factoring in taxes and financing decisions. In order for the two businesses to contribute equally to EBITDA, the VAR business will have to be large by 2018, with good margins, to equal the contribution of M+M Software. How does MuM get there? By supplementing and then replacing the low-margin software resale business with an increasing proportion of high-margin service business. This is everything from software user support and training to implementation to custom software development. And it’s going to have to be significant by 2020 to hit that target.

Let’s not underestimate how difficult this reinvention is. Many PLMish companies have struggled to develop profitable service businesses and we’ve seen them periodically outsource then insource then outsource again in an attempt to strike a good balance of customer responsiveness and software-company profitability. Investor expectations for MuM are different, since it’s supposed to have a strong services business, but that statement about equal EBITDA contribution is intriguing …

MuM will release final 2016 figures on 13 March 2017 (and cautions that the final audited 2016 figures might differ from the preliminary figures).

The post MuM sees subs growth resuming in H2 2017 appeared first on Schnitger Corporation.

► Autodesk’s Carl Bass to step down as CEO
7 Feb, 2017

Autodesk today announced that Carl Bass will step down as CEO effective February 8. The company also said that the board has started a CEO search and has “formed an Interim Office of the Chief Executive to oversee the company’s day-to-day operations”. I have no inside knowledge, but it seems that this was motivated by an agreement with an activist shareholder, who also insisted on other changes to the board of directors. I could be wrong, though, since the press release says that the company began discussing a possible CEO transition 18 months ago; I’m not sure when Sachem and other shareholders began to push on Autodesk to speed up the subscription transition.

The Interim Office of the Chief Executive will be headed by Amar Hanspal, SVP and chief product officer, and Andrew Anagnost, SVP and chief marketing officer, who will be interim co-chief executive officers.

Mr. Bass himself said in the press release, “It’s been my honor to lead Autodesk through this exciting period of growth and change. I’m very proud of everything we accomplished – from both a business and technology perspective. Our cloud and subscription business is well underway. The company’s strategy is working, the management team is strong and it’s the right time for me to step aside. Autodesk is poised for even greater success as it enters this next phase. I’m looking forward to my next adventure but will continue working with the company through my role as a board member and am committed to ensuring that the cloud and subscription business model will continue to be successful.”

The announcement also says that “Mr. Bass will remain on staff as a special advisor to the company in support of the transition to a new CEO. He will continue to sit on the Autodesk board of directors and will be nominated for reelection at the 2017 annual meeting of shareholders.”

If this is meant to reassure investors, it’s not working. At 9:30, when the New York Stock Exchange is just opening, Autodesk’s share price is down 1%.

More on this when there’s time – but it’s a real bummer. Mr. Bass is always interesting, incredibly smart and quick to see patterns that could lead to fascinating new developments. Of course, Mr. Hanspal and Mr. Anagnost are not dummies – but we’ll miss Mr. Bass’ “say anything and take no prisoners” approach. And his stories about his machine shop.

The post Autodesk’s Carl Bass to step down as CEO appeared first on Schnitger Corporation.

► Hexagon adds MSC to its stable, for $834 million 2 Feb, 2017 Now we know. The sale of MSC Software has been rumored for a while but always seemed to mention financial buyers. It turns out that a strategic buyer has stepped up — and I think it’s a perfect match. First, the background: Hexagon AB, for those who don’t know, today provides solutions that range from manufacturing (via VERO CAM solutions and Hexagon metrology tools) to 911, utility and other public safety and security solutions (from the former Intergraph subsidiary), laser scanning powerhouse Leica, to Intergraph Process, Power and Marine, a major supplier of solutions for design, engineering, fabrication and operations — in other words, lots of verticals, lots of technology, world-wide. MSC’s capabilities are a great addition. Well known in discrete manufacturing, MSC’s Nastran, Adams, Apex, Patran et al. help designers and engineers virtually test their products, optimizing them before a prototype is ever built and, in some cases, avoiding testing altogether. MSC has always had a presence in the process industries, but I don’t think it’s ever been a focus; that’s now likely to change With this gives Hexagon is a way to bridge the CAD world (which it doesn’t really have in manufacturing) with the manufacturing, via CAE. In the plant world, of course, it opens up a world of possibility to more tightly build CAE workflows into the design process. Hexagon already has some unique capabilities here, in tank/vessel design, acquired with Coade, but this is a new level of CAE for PP&M customers, one that they’re likely currently getting elsewhere. Hexagon CEO Ola Rollén said in a press release that “MSC represents a game changer in our mission to deliver actionable manufacturing intelligence, taking us another step closer to realizing our smart connected factory vision in discrete manufacturing industries such as automotive and aerospace. We can now leverage the data our MI division [Manufacturing Intelligence, the coordinate measuring machine/metrology part of Hexagon –Ed.] is generating to improve design choices and processes upstream in the workflow. The acquisition will also open up new markets and touchpoints for MSC via our PPM division.” The details: • The purchase price is$834 million, on a cash and debt free basis (Enterprise Value)
• That’s an EV multiple of roughly 3.5x, since Hexagon says that MSC had revenue of $230 million • Shutting down some reports, Hexagon says that MSC had “strong profitability and a high percentage of recurring revenue” in 2016 • Hexagon will fund the transaction via bank facilities • The closing is expected in April, pending regulatory approvals As I said, I like this. I think it’s better when the companies in our universe to be owned by entities that see their strategic value — it usually means a higher rate of investment, more public-facing and engaging management, and lots of integration opportunities. I’m excited to see what happens next. UPDATE: MSC CEO Dominic Gallello shared with me the letter sent to MSC customers about the transaction. It includes the following very important statement: “Hexagon considers MSC’s management team, along with all MSC employees, to be an extremely valuable asset. No personnel changes are anticipated as a result of this transaction. Additionally, Hexagon is a strong believer in MSC’s product roadmap and growth plans. No change to MSC’s current product roadmap are planned.” Later, in the FAQ, Mr. Gallello writes, “Hexagon has no plans to eliminate or sell [off] any products or business units.” The post Hexagon adds MSC to its stable, for$834 million appeared first on Schnitger Corporation.

► Quickie: Siemens AG mentions $100m PL order from GM amid solid FQ1 1 Feb, 2017 We’re getting ready to roll on earnings season in our PLMish universe. Tomorrow, Dassault Systèmes. But first, Siemens AG reported results earlier for the quarter ended December 31 and, while I don’t generally write about it since the PLM business is such a small part of the whole, Siemens’ growing focus on software and digitalization warranted a quick listen to the earnings call. First, some cautious notes: The company says it anticipates “increasing headwinds for macroeconomic growth and investment sentiment in our markets due to the complex geopolitical environment.” Interpret the reasoning how you will — but it means that the company now expects “modest growth in revenue, net of effects from currency translation and portfolio transactions.” In FQ1, total revenue was up 1% to €19.1 billion but orders fell 14%, leading to the foward-looking caution. The Financial Times says Siemens is Europe’s biggest industrial conglomerate so statements like this are bound to have a ripple effect, leading to caution from many other players, too. But here’s the kicker: CEO Joe Kaeser told investors that the “PL software business showed a clear comparable order growth”. That in itself is good, BUT he went on to say: “Siemens PL signed a multi-year contract with a value greater than US$100 million with General Motors. This new agreement validates the strength of our partnership helping GM to leverage digitalization.”

Somehow I missed this news – and it’s a big deal. GM decided back in 2011 to extend its contract with Siemens PLM Software, after a hard-fought battle with DS. No value was ever formally announced, so to hear that a follow-on deal is this large is … a big endorsement. [UPDATE: No details forthcoming. This is as much as GM is willing to have Siemens say.]

Mr. Kaeser also said that the shareholder vote on the proposed acquisition of Mentor Graphics will take place tomorrow (Feb 2);  he “expect[s] a positive outcome”. We’ll know tomorrow.

The post Quickie: Siemens AG mentions \$100m PL order from GM amid solid FQ1 appeared first on Schnitger Corporation.

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