Today is International Women in Engineering Day. How about using the hashtag #INWED17 on Twitter to tell everyone about the women engineers in your organization?
CAESES has launched an online geometry tool so you can generate geometries for the MVRC challenge (design an Le Mans prototype car and simulate its performance using CFD). See image below. [Not being a car guy, I had to google the acronym LMP. My ignorance aside, I’d like to hear back from other folks who use this tool.]
Also, the call for presentations is now open for the CAESES Users’ Meeting (27-29 Sep 2017).
Engineering.com shares work done by Mentor Graphics to do a system-level CFD analysis using mixed 1-D and 3-D CFD simulations. (Interoperability of multi-dimensional modeling is one of the challenges cited by the ASSESS Initiative.)
How about Diabatix’s browser-based CFD modeling of liquid cooling? In which we read, “The automation of engineering tools has generally not kept up with designers’ needs.”
Screen shot of the CAESES geometry tool for the MVRC challenge. Image from CAESES. See link above.
As reported in Scientific American, CFD is being used to gain insight into how how 600 million year old organisms lived by reverse engineering them from their fossils. [I would’ve paid money for a CFD image in this article.]
How about using CFD for “anti-soiling“? [Not what I originally thought. PowerFLOW is being used to simulate the accumulation of dirt etc. on a car’s exterior to understand better how to keep external cameras and sensors clean and clear.]
And architects can use CFD (in the cloud or otherwise) to better understand how wind flows around their buildings, especially in an urban environment.
News & Events
I just like this award-winning photo of graphene powder mixing in alcohol. Read about it at FYFD.
Envenio has received an investment of $1.3 million. [Full disclosure: Envenio offers a cloud-based version of Pointwise to their customers for CFD mesh generation.]
The DoE awarded NVIDIA funding from the Exascale Computing Project to develop a next-generation supercomputer. (Exascale computing platforms are part of the NASA CFD Vision 2030 Study.)
Registration is now open for TFAWS, the Thermal & Fluids Analysis Workshop.
SimVascular claims to be “the only fully opensource software package providing a complete pipeline from medical image data segmentation to patient specific blood flow simulation and analysis.”
Let the debate begin/continue: is proprietary or open-source software better for users? Asked by our friends at Simscale. [My answer: it depends. My other answer: it’s not a religion.]
Alert reader Mike shared with me the following photograph he took of Gemma Danielle’s mural City of the Sun (2015). He and I were both in Denver recently for AIAA Aviation and we both apparently were on the Cherry Creek Trail but he saw it and I missed it.
You can read here where the artist says “This mural looks very fluid and full of motion, but actually it’s made up of thousands of straight lines.”
She can’t be any clearer; it is a CFD mesh.
Gemma Danielle, City of the Sun, 2015. Photo credit.
Working with one of the world’s top big wave board shapers, engineers at CRAFT Tech have applied computational fluid dynamics (CFD) within a design optimization process, employing a genetic algorithm to evolve the design of a big wave surfboard. In this article, we will explore the design framework that allowed over 100 design iterations to be explored, resulting in a higher speed, lower drag, big wave surfboard design. (more)
Meshing is strange and mysterious territory for me. As the Receptionist at Pointwise, I don’t often get involved with the software, but I can’t help but admire the artistry of the posters created by our team. They are a beautiful mix of art and engineering that has captured my imagination. I’ve had a deep love of the arts since I was a child. While I greatly enjoy several different mediums, my primary love is writing. Creating places and people with words is something I’m still passionate about.
I was born and raised in the Sierra Nevada foothills of Northern California. My hometown is positioned right on the edge of the American River canyon, a beautiful and inspiring place to drive, hike, camp, and (if you’re brave) swim. I spent a large portion of my life climbing around those hills and walking along the river, letting my imagination run wild.
Later on, I worked at the local recreation district, whose offices were perched right on the edge of the canyon. It was a small organization, and I learned to exercise every artistic and technical skill I could muster in order to assist the different departments. Some of my strongest skills were developed here; I not only helped customers, I also managed and maintained our giant software program, wrote and designed ads for publication, and became the building’s unofficial troubleshooter.
When the time came to uproot myself and move to Texas, I took the opportunity to look at my future and decide what I really wanted to do with my life. With the encouragement and support of my closest friends, I enrolled at Tarrant County College and plunged back into the academic world I hadn’t seen since high school. As it turned out, I loved school and performed better than I expected. I found out that I liked math and science, which was a surprise, and I continued to invest in my writing abilities.
I’m in the middle of my degree program now, trying to decide on my major – so many choices! In the meantime, I love working at Pointwise and putting my eclectic mix of skills to work wherever they’re needed. My software experience has started to blend well with my writing skills, as I assist our marketing department as a proofreader and editor. I keep moving forward, step by step, class by class, and I’m excited to see what the future holds for me.
Location: Fort Worth, TX
Current position: Receptionist
Current computer: … It’s a Dell. They gave it to me. Okay, okay, it has a fast Intel Core i5-2400 Processor and two absolutely vital monitors. It also possesses GHz and RAM of some variety. Have I mentioned the monitors?
One word that best describes how you work: Tenaciously
I have a nice mix of tidy and lived-in going on here. In general, since this is the first thing guests see, there isn’t a ton of personalization in my space. I have to sneak in my five-headed dragon and Legend of Zelda milk bottle.
What are you currently working on?
Among many other things, I’m currently helping with proofreading articles and reconciling our company’s assets.
What would you say is your specialty at Pointwise?
I’m still pretty new and learning aspects of my job as they come up. But so far I think my specialty has been editing and proofreading articles for marketing.
Any tips, tricks, or advice for our users?
Use the resources Pointwise provides! There is a ton of content to help out just about every skill level, even a basic beginner like me.
What project are you most proud of and why?
I would have to say starting my YouTube channel. It doesn’t get a lot of use now, and it never got particularly popular, but in a few short months I went from being completely inexperienced with recording devices and hating the sound of my own voice to speaking confidently, recording content, and editing my videos. Every now and then I think of picking the channel back up and making a fresh start, I just haven’t had the time.
Have you recently read any books or articles we should know about?
I don’t know how relevant this is, but recently I’ve been reading everything I can get my hands on about the American Revolution. My American History classes were some of my favorites, and I’m still entranced by how the constitution was written in secret – with the doors locked and the windows blocked, so no one could catch on to the fact that they weren’t doing what they were told. Right now I’m reading Ron Chernow’s biography on Alexander Hamilton, which is fascinating.
Do you plan on attending any conferences or workshops this year?
Not really. As the receptionist, conferences and workshops aren’t really in my sphere.
What do you do when you’re not surrounded by engineers making meshes?
Read, write, listen to music, and play games, to name a few things.
What is some of the best advice you’ve received?
“Don’t live your life to please other people.”
If you had to pick a place to have dinner, where would you go?
Old Town Pizza – a restaurant back in my hometown that made the best pizza in the world. I’ve found restaurants in Fort Worth to replace most of my other favorite places from home, but nothing has come close to matching OTP.
Not strictly CFD (although I’m willing to bet CFD was and will be involved), but DARPA selected Boeing’s design for their Experimental Spaceplane project. Image from DARPA.
This is interesting. Current petascale computers have a “mean time between failure” of about 8 hours and extrapolating that to exascale yields a MTBF of minutes. Therefore, your CFD solver had better be fault tolerant which is exactly what the ExaFLOW project is working on.
Was it trickery? How aerodynamics contributed to the attempt to break the 2-hour marathon time. Image from Siemens PLM. See link below to full article.
In what might be one of the most widely read articles involving a CFD application, the folks at Siemens PLM (nee CD-adapco) did a CFD analysis with STAR-CCM+ of the recent attempt that just missed breaking the marathon 2-hour time. See image above.
Monica Schnitger thinks it’s gonna be a good year for CAE. But also wishes vendors would report number of licenses sold in addition to revenue in order to get a better feel for the nascent and uneven transition to subscriptions.
Convergent Science shares applications of CONVERGE CFD to gas turbine type flowfields. This image of a DLR combustor is from convergecfd.com where you can read the full article.
Jobs
Flow Science has several job openings including CFD Engineer and Sales Engineer.
The Norwegian Institute of Science and Technology has an open PhD position in CFD as applied to biomass.
Edoardo Tresoldi was included in Forbes’ list of 30 influential European artists under the age of 30 and his installations created from wire mesh at architectural scale make it easy to see why.
As originally seen by me on Colossal, Tresoldi’s work is a paradoxical combination of geometry and formlessness, heft and ephemerality. From his biography, his use of mesh “transcends space time dimension and narrates a dialog between Art and World” which reveals itself in “the fade-out of physical limitations.” His work Lift (shown lit and viewed from below in the following image) really caught my eye.
Do not missed Tresoldi’s website where many of his mesh works are revealed.
Edoardo Tresoldi, Lift (a temporary installation in Huntington, UK). See link above.
Just Silly: Structured water? You mean I’ve been drinking unstructured water all this time?
P.S. There won’t be a This Week in CFD next Friday as I’ll be on my way to Denver to participate in the AIAA 1st Geometry and Mesh Generation Workshop being held the weekend prior to the AIAA Aviation Forum and Expo. There’s still time to register and attend for what’s certain to be more geometry modeling and meshing discussion you’ve ever had on a weekend.
The 2017 2nd quarter issue of our The Connector newsletter is now available on our website at www.pointwise.com/theconnector/2017-Q2. This issue features articles on
The Philosophy of Simulation Software Development was a headline certain to get my attention. In the article, MSC Software’s VP of product development writes that “product development is about the connectedness of different departments within an organization” and much more. [I swear I only recently told someone that all engineering occurs at the interfaces.]
NASA’s offer of cash prizes to speed up CFD code FUN3D is getting wide publicity, most recently in Aviation Week (subscription required). If any of you are going to enter this contest, let me know.
Read about how EXN/Aero was used to simulate the ONERA M6 wing at transonic conditions at a cost of $324.
Applications & News
Capvidia’s FlowVision CFD solver is being used to simulate blood flow in Dassault Systemes’ Living Heart Project.
You’ll soon be able to access the supercomputing power of a Cray in the cloud.
DEVELOP3D interviewed the CEO of SimSolid and discussed how they are able to simulate complex parts without meshing. [You have no idea how hard it was for me to type those last two words.]
Is the “Superman tuck” the most aerodynamically optimal for cycling? [It certainly can’t be the most comfortable.] Read more from Symscape.
Congratulations to Daat, makers of the Coolit CFD software, on the 25th anniversary of the company’s founding.
Although the full article requires a subscription, Aviation Week included a head-scratcher in the title Wind Tunnels Have Future in Digital Age, Europeans Say [emphasis mine]. I’m fairly certain that’s not a Euro-centric viewpoint.
Comparison of temperatures from infrared imaging (left) and FloTHERM XT simulation results (right) for a tablet computer. Image from ElectronicDesign.com. Read full article here.
ANSYS Fluent simulation of an impeller inside a reactor vessel. Image from a white paper by Xerox about scaling up CFD simulations. Read full article here.
Algorithmic Art
One might think that it’s a weekly struggle to find mesh-related fine art but honestly, works that fascinate me pass through my inbox or web browser with great regularity. The most recent example is the work of Owen Schuh who wrote this about his algorithmic-centered work: “These functions bear the structure of life, but operate in the parallel world of the mind: a world of simulacra inhabited by numbers and abstract relationships.” To me, this rings true about mesh generation: its function is to provide structure on which the simulation of a fluid (life) can be performed yet it remains something completely abstract.
Shown below, Unfolding a Cube (onto a plane) looks like many meshes I’ve seen before the optimization steps are applied. I recommend you read Schuh’s statements on the Art 3 Gallery website (see link above).
Owen Schuh, Unfolding a Cube (onto a plane), 2017. Image from Art 3 Gallery. See link above.
Viewing fluids through a macro lens makes for an incredible playground. In “Galaxy Gates”, Thomas Blanchard and the artists of Oilhack explore a colorful and dynamic landscape of paint, oil, and glitter. The nucleation of holes and the breakdown of sheets to filaments and droplets plays a major role in the visuals. The surface layer is constantly peeling away to reveal what’s going on underneath. In many cases this initial motion settles into a field of oil-rimmed droplets floating like planets against a colorful galactic backdrop. Watch carefully in the second half of the video, and you can even catch a few instances of a stretched ligament of fluid breaking into a string of satellite drops, like at 1:51. Check out some of Blanchard’s previous work here and here. (Video credit: Oilhack and T. Blanchard; GIFs and h/t to Colossal)
Fluid flows are complex, complicated, and ever-changing. Researchers use many techniques to visualize parts of a flow, which can help make what’s happening clearer. One technique, shown above, uses oil and dye to visualize flow at the surface. The vertical, black, airfoil-shaped pieces are stators, stationary parts within a turbine that help direct flow. After painting the stator mount surface with a uniform layer of oil, the model can be placed in a wind tunnel (or turbine) and exposed to flow. Air moving around the stators drags some of the oil with it, creating the darker and lighter streaks seen here. Notice how the lines of oil turn sharply around the front of the stator and bunch up near its widest point. Those crowded flow lines tell researchers that the air moves quickly around this corner. (Image credit: D. Klaubert et al., source)
Diving can generate some remarkable splashes. Here researchers explore the splashes from a wedge-shaped impactor. At high speeds, they found that the splash sheet pushed out by the wedge curls back on itself and accelerates sharply downward to “slap” the water surface (top). Studying the air flow around the splash sheet reveals some of the dynamics driving the slap (bottom). The splash sheet quickly develops a kink that grows as the sheet expands. This creates a constriction that accelerates flow on the underside of the sheet. That higher velocity flow means a low pressure inside the constriction, which pulls the thin sheet down rapidly, making it slap the surface. For more, check out the full video. (Image and research credit: T. Xiao et al., source)
In the latest Veritasium video, Derek demonstrates how to see gas motions that are normally invisible using a schlieren photography set-up. Schlieren techniques have been important in fluid dynamics for well over a century, and Derek’s set-up is one of the two most common ways to set up the technique. (The other method uses two collimating mirrors instead of a single spherical or parabolic one.) As explained in the video, the schlieren optical set-up is sensitive to small changes in the refractive index, making density changes or differences in a gas visible. This makes it possible to distinguish gases of different temperatures or compositions and even lets you see shock waves in supersonic flows. (Video and image credit: Veritasium; submitted by Paul)
When we watch sands running through an hourglass, we think their flow rate is constant. In other words, the same number of grains falls through the neck at the beginning and the end. In many practical granular flows, like those through industrial hoppers (left), this is not the case. Instead, emptying those containers involves a surge near the end where the discharge rate is higher.
The surge is related to the interstitial fluid – the air, water, or other fluid in the space between the grains. On the right, you see an experiment in which brown grains submerged in green-dyed water are emptied. The dark layer is dyed water initially at the top of the grains. As the container drains, that dyed layer moves down more rapidly than the grains; this indicates that the interstitial fluid is actually being pumped by the draining of the grains. Researchers think this is an important factor affecting the final surge. (Image credits: hopper - T. Cizauskas; discharge graph - J. Koivisto and D. Durian, source; research credit: J. Koivisto and D. Durian; submitted by Marc A)
Graphene powder swirls in alcohol in this prize-winning photo from this year’s Engineering and Physical Sciences Research Council photography competition in the UK. The image was captured while producing graphene ink that can print circuits directly onto paper. According to the researcher’s description, this ink is forced through micrometer-sized capillaries at high pressure to rip the layers apart and produce a smooth, conductive ink in solution. In this photo, we seem to see more conventional mixing driven by the powder’s injection and the variations in surface tension due to the alcohol and its evaporation. The graphene leaves behind beautiful streaklines that highlight its path as it mixes. (Image credit: J. Macleod; via Discover)
Fossilized imprints of Parvancorina from over 500 million years ago have puzzled paleontologists for decades. What makes it difficult to infer their behavior is that Parvancorina have none of the familiar features we might expect of animals, e.g., limbs, mouth. In an attempt to shed some light on how Parvancorina might have interacted with their environment researchers have enlisted the help of Computational Fluid Dynamics (CFD).
CFD Water Flow Simulation over a Parvancorina: Forward directionIllustration only, not part of the study
The recent attempt to break the 2 hour marathon came very close at 2:00:24, with various aids that would be deemed illegal under current IAAF rules. The bold and obvious aerodynamic aid appeared to be a Tesla fitted with an oversized digital clock leading the runners by a few meters.
Early on in the dash to develop ever faster racecars in the 1970s, aerodynamics, and specifically downforce, proved a revelation. Following on quickly from the initial passive downforce initiatives were active aerodynamic solutions. Only providing downforce when needed (i.e., cornering and braking) then reverting to a low drag configuration was an ideal protocol, but short lived due to rule changes in most motor sports (including Formula 1), which banned active aerodynamics. A recent exception to the rule is the highly regulated Drag Reduction System now used in F1. However, road-legal cars are not governed by such regulations and so we have the gloriously unregulated Lamborghini Huracán Performante.
Active Aerodynamics on the Lamborghini Huracán Performante
Fluidic logic (fluidics) uses specially designed fluid paths to perform logic operations, such as AND, OR, and NOT gates. In electronics logical operations underpin all the digital devices that depend on CPUs for their brains. Using Computational Fluid Dynamics (CFD) we can quickly explore potential fluidic components.
Hi,
after initializing your case, go to Reports.....Discrete Phase...Sample and choose all the walls of your geometry in the left menu, and in the right menu select your injections, then click start (note that all this process should happen after initializing the case not before it) and start the calculation, when the calculation is completed go to the previous menu (sample) and just click stop, you will see some files with dpm suffix (wall.dpm) are saved in the folder that your case is saved, open them and you can see all the particles datas that are traped including time, coordinate and ...
Function component is defined in class GeometricField.H (but in class GeometricBoudnaryField). It takes a
Code:
const direction
and return a component typed GeometricField.
Well, since it is declared in GeometricBoudnaryField (not really class GeometricField). Let's see the mother class of GeometricField which is DimensionedField and she has a function component which does practically the same thing except that a DimensionedField is returned instead of GeometricField.
So, going back to the construction of object gradTx. I found one constructor corresponding to what is written above
Here he talked aboutmemory functionand a big memory footprint which will need special treatment in programming.
Quote:
Originally Posted by JonW
Hi there again,
I see what you mean. I am pretty sure that this is not in OF. You have to program it your self
I just send you the paper you asked, and it might be a use after all, for your problem.
If you look at Eq. (8) in the paper (see also eq 9.3 p. 210 in the phd thesis), you have a Gamma which I am using. What I am working on is fading memory. But you could let it have full memory (alpha = 1) and put I = shear rate (i.e. dot-gamma) and you would be calculating deformation. In the FORTRAN source code this is named Gamma (in viscous.f90).
Anyway, if you do use this approach, memory (as in RAM) can be a problem very quickly, but the integration trick in my phd thesis I mentioned above can solve this.
Here is a proof. So I give up the route to compile OpenFOAM by icc.
Quote:
Originally Posted by wyldckat
Hi Achim,
I wanted the output and error messages to be in the same file, so I could have an historic detailing of where/when the errors occurred. This way I have separate history lines...
Either way, my suspicion is that you edited the following code in "etc/config/settings.sh":
If you changed the lines with "WM_CC" and "WM_CXX" to use "icc", then undo those changes! Like I wrote before, Scotch will not build with Icc... at least not without substantial hacking - here's the proof: http://www.openfoam.com/mantisbt/view.php?id=323
By the way, knowing which Gcc version and Linux architecture would make it easier for me to help you. To know the architecture, run:
The following is taken from*CFD ISRAEL*blog (thoughts of a CFD blogger), addressing a branch of Scale Resolving Simulation known as 2nd generation URANS.
The specific SAS model is a*2-equation*unsteady RANS model, able to get very good quantitative results for many unsteady flows even on a relatively coarse mesh.
Here is an extremely simple simulation to set up that has a surprisingly beautiful output. In this post, we will simulation the classic Rayleigh–Bénard convection (see Wikipedia) in 3D using the buoyant solver, buoyantBoussinesqPimpleFoam.
buoyantBoussinesqPimpleFoam is a solver for buoyant, turbulent, incompressible flows. The incompressible part of the solver comes from the fact that it uses the Boussinesq approximation for buoyancy which is only suitable for flows with small density changes (aka incompressible). A good source for the equations in this solver is this blog post.
Simulation Set-up
The basic set-up for this case is simple: a hot bottom surface, a cold top surface, either cyclic or zero-gradient sides, and some initial conditions.
For this example, I used the properties of water, I set the bottom plate at 373 K (don’t ask me why… I know it’s close to boiling point of water), and the top plate at 273 K. For this case, we will not use any turbulent modeling and will simply use a laminar model (this simply means there is only molecular viscosity, there are no simplifications applied to the equations).
Geometry and Mesh
The geometry is shown below. As the geometry is so simple… I will not go over the blockMesh set up. The mesh discretization that I used was simplegrading (i.e. no inflation), with 200x200x50 cells.
Constant
For this case, we will simulate water. The transportProperties file should look like:
I don’t typically delete unused entries from dictionaries. This makes using previous simulations as templates much easier. Therefore note that the turbulent Prandtl number is in the dictionary but it is not used.
Selecting Reference Temperature TRef for buoyantBoussinesqPimpleFoam. To answer this recall that when the Boussinesq buoyancy approximation is used, the solver does not solve for the density. It solves the relative density using the linear function:
Therefore, I think it makes sense that we should choose a temperature for that is somewhere in the range of the simulation. Thus I chose Tref=300. Somewhere in the middle!
The g file tells the solver the acceleration due to gravity, as well as the direction:
dimensions [0 1 -2 0 0 0 0];
value (0 -9.81 0);
Boundary Conditions
In the “zero” folder, we need the following files: p, p_rgh, T, U, and alphat (this file needs to be present… however it is not used given the laminar simulationType.
T:
dimensions [0 0 0 1 0 0 0];
internalField uniform 273;
boundaryField
{
floor
{
type fixedValue;
value uniform 373;
}
ceiling
{
type fixedValue;
value uniform 273;
}
fixedWalls
{
type zeroGradient;
}
}
p_rgh:
dimensions [0 2 -2 0 0 0 0];
internalField uniform 0;
boundaryField
{
floor
{
type fixedFluxPressure;
rho rhok;
value uniform 0;
}
ceiling
{
type fixedFluxPressure;
rho rhok;
value uniform 0;
}
fixedWalls
{
type fixedFluxPressure;
rho rhok;
value uniform 0;
}
}
p:
dimensions [0 2 -2 0 0 0 0];
internalField uniform 0;
boundaryField
{
floor
{
type calculated;
value $internalField;
}
ceiling
{
type calculated;
value $internalField;
}
fixedWalls
{
type calculated;
value $internalField;
}
}
The results (as always) are the best part. But especially for this case since they are so nice to look at! I have made a couple animations of temperature fields and contours. Enjoy.
3D Temperature Contours
Temperature Field – Slice Through xy Plane
Conclusion
This case demonstrated the simple set up of a case using buoyantBoussinesqPimpleFoam. The case (Rayleigh-Bénard convection) was simulated in 3D on a fine grid.
Comments and questions are welcome! Keep in mind I set this case up very quickly.
This post is a simple demonstration of the timeVaryingFixedUniformValue boundary condition. This boundary condition allows a Dirichlet-type boundary condition to be varied in time. To demonstrate, we will modify the oscillating cylinder case.
Set-Up
Instead of using the oscillating boundary condition for point displacement. We will have the cylinder do two things:
Move in a circular motion
Move in a sinusoidal decay motion
The basics of this boundary condition are extremely simple. Keep in mind that although (here) we are modifying the pointDisplacement boundary condition for the cylinder, the basics of this BC would be the same if you were doing a time varying boundary condition for say pressure or velocity.
In the pointDisplacement file:
cylinder
{
type timeVaryingUniformFixedValue;
fileName "prescribedMotion";
outOfBounds clamp;
}
fileName points to the file where the time varying boundary condition is defined. Here we used a file called prescribedMotion however you can name it whatever you want. The outOfBounds variable dictates what the simulation should do if the simulation time progresses outside of the time domain defined in the file.
The additional file containing the desired motion prescribedMotion is formatted in the following way:
The first column is the time in seconds, and the vector defines the point displacement. In the present tutorial, these points were calculated in libreOffice and then exported into the text file. I arbitrarily made up the motions purely for the sake of making this blog post.
The circular motion was defined as:
and
Decaying sinusoidal motion was:
The rest of the set-up is identical to the set-up in the oscillating cylinder example. The solver pimpleDyMFoam is then run.
Results
Circular Motion
Sinusoidal Decay
Conclusions
This post demonstrated how a more complicated motion can be prescribed by using a little math and the timeVaryingUniformFixedValue boundary condition. Always like to hear questions and comments! Has anybody else done something like this?
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, ) and their stagnation property as a function of Mach number and the ratio of specific heats, . Recall that Mach number is the ratio between the velocity and the speed of sound, a.
These ratios are given here:
Temperature:
Pressure:
Density:
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.)
Some excellent references for these equations are:
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:
First, here is a summary the equations and steps discussed here (in case you don’t want to read the whole example):
Complete at least 3 simulations (Coarse, medium, fine) with a constant refinement ratio, r, between them (in our example we use r=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.
Calculate the order of convergence, p, using:
Perform a Richardson extrapolation to predict the value at h=0
Calculate grid convergence index (GCI) for the medium and fine refinement levels
Ensure that grids are in the asymptotic range of convergence by checking:
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:
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:
recall that r is the refinement ratio and is which in this case is 2.
Using this equation we get the Richardson extrapolated results:
at h=0 -> -0.029941
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:
where e is the error between the two grids and 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
Max velocity
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:
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
Minimum Velocity
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:
Conclusions and Additional References
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:
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:
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.
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.
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).
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!
We want to create a hex-dominantgrid. 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.
Cross-Section:
Streamlines and pressure on surface:
Vorticity surface in the near-wake
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.
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
The tutorial files for this case can be downloaded from here:
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).
Fig: Grid
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:
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:
Wake of Oscillating Cylinder
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.
Airfoil Digitizeris a software package for extracting airfoil data files from images. The software accepts images in the jpg, gif, bmp, png and tiff formats. Airfoil data can be exported as AutoCAD DXF files (line entities), UIUC airfoil database format and Hanley Innovations VisualFoil Format. The following tutorial show how to use Airfoil Digitizer to obtain hard to find airfoil ordinates from pictures.
More information about the software can be found at the following url:
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.
About Patrick Hanley, Ph.D. 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.
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.
About Patrick Hanley, Ph.D. 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.
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.
About Patrick Hanley, Ph.D. 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.
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:
About Patrick Hanley, Ph.D. 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.
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 .
About Patrick Hanley, Ph.D. 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.
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 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 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 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. 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 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.
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!
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.
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.
Please let us know your experience, good or bad. Good luck!
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.
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:
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:
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!
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).
Today we celebrate International Women in Engineering Day. Recently, I had the opportunity to meet with Dr. Shini Somara over coffee one afternoon. Shini is a broadcasting science and technology journalist who reports engineering and innovation on television and online, both in the UK and USA for networks such as the BBC and PBS. She began her career as a fluid dynamicist, having studied mechanical engineering at Brunel University and has always been passionate about making the invisible, visible! An interest that began with Bernoulli’s equation and has continued through her communication of science.
When we met, we started to share some common views about how to encourage more engineers into this field. Dr Somara is well placed to discuss this with her tireless work advocating STEM education, including providing free online education on physics, through a 48-episode series called Crash Course: Physics.
The conversation then moved onto the topic of gender gap in this area. I was quite shocked to find out from that women only make up 9% of Engineers in the UK from the Women in Engineering Society site she pointed me to — a statistic that she highlighted in her United Nations speech in February of this year, for International Day for Women and Girls in Science.
I had the opportunity to ask Dr Shini her thoughts and why she feels there is a shortage of female engineers. She explained:
There is no reason why women shouldn’t study engineering. In my experience, what has held most women back in this field, has been their own self-doubt. Girls drop out of engineering, largely due to a fear of failing. Failure is seldom recognised as being necessary for progress. Yet most solutions in engineering come from our understanding of failure.
Other outdated beliefs and inferior perceptions of women in engineering need to be overthrown too and instead replaced with role models. I believe role models should be a collection of men and women who are living examples of those that have followed their own passions, interests and talents, despite adversity. The skills of girls and women in engineering need to be stimulated, supported and encouraged, so that they can push through archaic attitudes, and so reach their own fullest potential within the industry.
We have been supporting IWIED since its inception and I feel strongly that we should continue this to make a real change. This is why our local office signed up for a UK event that celebrates International Women in Engineering Day. They will showcase a number of inspiring women from the engineering industry at the iconic London Transport Museum.
There is a tremendous amount of varied and exciting careers available in the engineering world and I had the opportunity to meet with several female engineers using ANSYS. They work on cool projects from designing vacuum cleaners, chocolate, shopping malls to football stadiums.
Catriona Stokes, Dyson
For Catriona Stokes from Dyson — great-great-niece of Sir Georges Stokes — I somehow think her destiny had already been written. Now, I’m not an engineer, but working alongside physicists every day the Navier-Stokes equation, which describes the motion of viscous fluid substances, is not as uncommon as you think.
Beccy Smith, Mondelēz
Most people would not associate chocolate with science and mathematics but Beccy Smith from Mondelēz uses equations almost every day.
Polina Osichansky, EM Infinity
I also met with Polina Osichansky who works for EM Infinity in Israel. She is an extremely gifted electronics engineer looking at complex problems faced with RF Systems, Antenna Design and Placement and found her passion for her work uplifting.
This all inspired me to put together a short video of these talented scientists and engineers and the importance this career is for them. Let them tell you in their own words what inspires them. In closing, I encourage everyone reading this to do their best to support the women in their lives who aspire to pursue the sciences.
This day, June 23rd, began as a UK initiative but has since grown to an International Day of recognizing the great women I have the opportunity to work side-by-side each day. ANSYS has many such engineers and we applaud their expertise. Cheers!
I was reminded just how complicated and expensive it is to develop a jet engine when I came across a video describing GE’s recent $26 million Cdn investment to upgrade its Winnipeg test facility. That is on top of even bigger investments by Rolls-Royce ($50 million) and GE ($40 million) and in recent years. Physical testing is not only expensive, it is time consuming and can lengthen design cycles.
Meanwhile, it has become easier than ever to simulate engine performance prior to any physical testing. Improved techniques like harmonic analysis, turbomachinery-specific workflows and better validation coupled with faster, more capable high performance & cloud computing are quickly expanding simulation so engineers can be confident in their designs before the first prototype is ever built. While physical testing is not going away anytime soon, ANSYS is working on digital prototyping with leading turbomachinery companies and helping them to cut it down to size.
That is why I’m so excited about the ASME TurboExpo / Power & Energy conference coming up next week in Charlotte, North Carolina. I can’t wait to immerse myself in the newest advancements and meet some of the 4000+ experts who will be attending from around the globe. It will truly be like drinking from a fire hose!
If you are attending, I invite you to please stop by the ANSYS booth 701 and talk me and our experts. Plus, we are going to be showing a new turbomachinery visualization tool that will knock your socks off. I can’t say more, you have to see it. Learn more about ANSYS at the TurboExpo here
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Here are just a few of the talks that you don’t want to miss:
Simulation Innovations Speed Turbomachinery Design and Analysis
Tuesday, June 27, 4:00 – 4:30
Andre Braune, Technical Account Manager, ANSYS
Use of Simulation in Additive Manufacturing
Wednesday, June 28, 1:45 – 2:15
Dave Conover, Corporate Fellow, ANSYS
Digital connectivity enhancing product life cycle value through prognostic health monitoring (PHM)
Wednesday, June 28, 4:00 – 4:30
Ahmad Haidari, Ph.D., Global Industry Director, ANSYS
Mode Shapes and Dominant Frequency Predictions in a Swirl Stabilized Premixed Air-Methane Combustor Using Modal Analysis and Large Eddy Simulation (LES)
Tushar Jadhav, Stefano Orsino, Saurabh Patwardhan and Pravin Nakod, ANSYS, Inc Learn more in this blog.
Unsteady Flow in a Centrifugal Compressor Stage Equipped with a Vaned Diffuser and Cavities
Christophe Corneloup & Dr. François Moyroud, GE Oil & Gas France
Dr. Mohand Younsi, & Dr. Antoine Baldacci, ANSYS, Inc. Learn more in this blog.
A Hybrid Flamelet Generated Manifold Model For Modeling Partially Premixed Turbulent Combustion Flames
Rakesh Yadav, Sandeep Jain, ANSYS Inc. Ashoke De, I.I.T Kanpur Learn more in this blog.
Stringent emission regulations force the gas turbine combustor community to come up with new designs. Lean Premixed combustion (LPM) is gaining popularity to meet the emission regulations. However, lean combustion process is prone to other issues like combustion instabilities and noise.
Self-excited combustion instabilities in a gas turbine play a vital role in the lifecycle of combustor, noise generation and pollutant formation. If the instabilities in the combustor dominate at natural modes, there are risks of resonance that can lead to bursting damage to the combustors. Therefore, it is necessary to understand the combustion dynamics performance of a given lean premixed combustor.
Our team is contributing our best to develop methodologies using our tools for modeling combustion dynamics with a great confidence. On behalf of our team, I will be presenting some of our most recent work, a paper entitled: Mode Shapes and Dominant Frequency Predictions in a Swirl Stabilized Premixed Air-Methane Combustor Using Modal Analysis and Large Eddy Simulations (LES) at the upcoming ASME Turbo Expo — Technical session 4-38 Combustion Dynamics: Modeling III, Friday, June 30th from 8-10 am in Charlotte.
In this paper, I will be talking about modal analysis carried out using ANSYS Mechanical to predict the longitudinal and the transverse modes in a swirl-stabilized premixed methane-air combustor. Large Eddy Simulations (LES) with Flamelet Generation Manifold (FGM) combustion model in ANSYS Fluent were used to identify the instabilities and their sources. A couple of images from the simulation results are shown below.
First longitudinal Mode at 1760Hz (ANSYS Mechanical)
Spatial and Temporal Evolution of Precessing Vortex Core (ANSYS Fluent)
I am quite excited to present and discuss more about these simulation results during the conference. If you will be at the Turbo Expo, please plan to attend this presentation. Also stop by ANSYS booth 701 our technical team would love to talk with you.
Semiconductors touch every aspect of our lives — from the computers that we work on to the automobiles we drive to the medical devices that keep us healthy. As these amazing chips become smaller and more packed with functionality (the latest NVIDIA graphics chip has 21 billion transistors!), designing and producing them becomes far more complicated. Yet increased demand for smaller, more powerful integrated circuits is increasing so companies can create the products of the future.
For more than a decade, the semiconductor industry has relied on ANSYS RedHawk for chip power integrity and reliability signoff — the verification a design must pass before being manufactured. These ANSYS-designed chips are inside everything from your smartphone to your washing machine. Similarly, many in the industry actively use Synopsys tools for physical implementation of their chips. Synopsys is the largest electronic design automation company in the world, with leading technologies, including physical implementation.
Designers have been using ANSYS and Synopsys tools in combination for years, but our integrated solution will enable customers to apply power integrity and reliability signoff solutions earlier in the design flow — empowering them to deliver innovative, high-performance and reliable products faster, while reducing power, area and cost. This is a win-win for our companies as well as the industry.
You may recall that we recently introduced RedHawk-SC, bringing RedHawk onto our ANSYS SeaScape architecture. RedHawk-SC gives users access to big data analytics and popular machine learning packages to reduce power while increasing performance and reliability of semiconductor designs.
We announced this partnership today at the Design Automation Conference (DAC), the premier semiconductor industry event, and so far the feedback has been outstanding. I truly believe this is a watershed event for the industry.
If you’re at DAC this week, please make sure you stop by booth 647 and speak with our experts.
I’m happy to announce that our team will once again be showcasing our industry-leading solutions at the 54th Annual Design Automation Conference (#54DAC) in Austin, TX. I invite you to stop by and meet with our domain experts in booth 647, from June 19-21, to learn how our industry-leading technology can help meet your SoC design challenges with production-proven solutions.
We’ll be hosting a series of workshops with technical presentations by our customers and partners, as well as ‘Best Practices’ sessions covering a wide range of important topics such as:
Thermal, EM and ESD Signoff for Advanced Designs
Workflows for ADAS, Mobile and HPC designs
RTL-driven System Power Profiling & Reduction
Next Generation SoC and AMS IP Power Noise Reliability Signoff
Chip-Package-System and Timing Closure
Join us in the ANSYS theater where we will feature presentations by our Foundry partners, along with some creative and informative entertainment, plus the opportunity for a chance to win a popular ANSYS stuffed toy dog, t-shirt, or cool raffle prizes!
I’m going to stop by ANSYS #54DAC booth 647 to win cool stuff & hear great speakers! Click To Tweet
Visit the ANSYS DAC page for a list of all our conference events at #54DAC including over 20 Designer/IP Track presentations and posters that will showcase ANSYS technology.
Don’t forget to register today for a Workshop or Best Practices technical session and stop by the booth to say hello and meet our experts. See you in Austin!
Transient blade row simulations in turbomachinery are needed either to improve the aerodynamic performance predictions or because the flow interaction we are trying to resolve and predict is unsteady in nature such as aeromechanical, aerothermodynamic or aeroacoustic interactions. Because the blade pitch is not similar between the rows of turbine or compressor, a transient blade row simulation will usually require the modeling of the full wheel (or full geometry). This constraint renders these simulations not practical and in many cases prohibitive as analysis or design tools.
In the past six releases, ANSYS CFX has introduced a range of transient blade row with pitch-change models — read past blogs on TBR advancements here: Aeromechanics and Performance, Blade Row Flow Modeling, Designing Superior Turbomachinery. These methods model the turbomachinery flow in one or few blade passages per row. Therefore, not only do they reduce the model size but the time required to obtain a solution with minimum loss of solution fidelity.
For a typical compressor fan stage, the time required to obtain a solution with the pitch-change method can be an order of magnitude faster than full wheel model. Therefore, the transient pitch-change methods provide viable analysis tools to turbomachinery engineer to explore the flow details and make an educated improvement to the design.
In order to use these transient pitch-change methods as design tools early in the design cycle, and not only as analysis tools in later stages of the design, further reduction in time to obtain a solution is required.
In ANSYS 18, CFX has introduced Harmonic Analysis (HA), a hybrid time-frequency domain solution method. This solution method provides rapid answers to transient periodic flows, such as the one in turbomachinery, with acceptable engineering accuracy. The first release of Harmonic Analysis tackles the blade flutter/aerodamping calculations flow problem — a fluid-structural interaction problem.
A typical blade flutter analysis modeled with pitch-change HA can be one order of magnitude faster than the same problem solved using transient with pitch-change solution method, and about two orders of magnitude faster than full-wheel transient simulations. The ability to obtain a fast solution with good engineering accuracy makes this solution method a viable design tool where repeated calculations are required to explore the design space.
Reconstructed pressure distribution on the 22 blades of NASA Rotor 67 for Nodal Diameter = 6 at some instance in time. The solution was obtained on two blade passages only using Fourier-Transformation pitch-change and the Harmonic Analysis solution method.
Aerodynamic damping for the entire nodal diameter range for Rotor 67 is now easily attainable with the efficient Harmonic Analysis solution method
ANSYS has been developing a comprehensive toolset for transient blade row analysis. At the end of June, I will attend the ASME Turbo Expo in Charlotte, NC, If you are attending this fantastic gathering, I would like to meet you. You can find me and my colleagues either in the ANSYS booth or in the technical sessions and we will be excited to tell you more about HA and ANSYS blade row flow modeling capabilities.
At Convergent Science, we got our start modeling internal combustion engines. Naturally, as we developed CONVERGE, we added tools and features with IC engine simulations in mind. But features like a detailed chemistry solver, automatic meshing with Adaptive Mesh Refinement, moving geometries, and low-dissipation numerics certainly aren’t limited to IC engines! These techniques are highly applicable to gas turbine engine simulations as well.
We have been studying gas turbine engines with CONVERGE for some time, with an eye toward solving the hard problems and bringing a truly predictive capability to the gas turbine industry. When we think of hard problems, we think of unsteady, highly nonlinear chemical processes. In a gas turbine context, this leads us to transient flame shape, emission formation, relight, lean blow off (LBO), and flashback. Two recent publications illustrate that CONVERGE can predict these critical operational phenomena.
In AIAA Paper 2016-4561, we demonstrated that CONVERGE can predict gas turbine relight ignition and flame propagation. We compared our simulated results to DLR experimental data from the CORIA-designed PRECCINSTA combustor. This up-fired, natural-gas-fueled, five-burner array is shown in Figure 1.
Figure 1: Numerical setup of the PRECCINSTA combustor rig in its five-burner configuration.
This was a fairly routine calculation by our standards–automated meshing of a very complex geometry, unsteady RANS turbulence modeling, an energy source term for a spark, solving chemistry with the SAGE detailed chemistry solver. The grid was not especially fine (about ten million cells), and we did not opt for the expense of an LES calculation. Yet Figure 2 demonstrates qualitatively representative relight dynamics. Figure 3 shows that injector timing is within experimental error bars.
Figure 2: Flame shape at three different instances in time. Spark event is at t = 0.0 ms. CONVERGE result at left, experiment1 on right.Figure 3: Ignition timing of injectors 1 through 5.
We conducted further simulations of two-burner and four-burner cases without changing the methodology. These results are shown in Figures 4 and 5. Once again, CONVERGE ignition times fall within experimental error bars.
Because we did not tune the model parameters, this is a predictive result for the critical capability of high altitude relight. Traditionally, CFD has not been used for relight design because no simulation suite could produce a predictive solution. But as tools improve, so do engineering best practices. Honeywell, a major gas turbine engine manufacturer, wrote an article outlining how CONVERGE CFD can be used to predict relight.
Figure 4: Ignition timing of the two-burner case.Figure 5: Ignition timing of the four-burner case.
In another recent AIAA paper (AIAA 2017-1059), we showed that CONVERGE can predict NOx and CO emissions in a pilot-stabilized power generation combustor. While NOx and CO are both environmentally important and tightly regulated emissions, they are also signals for operability. Gaseous-fueled power gas turbines typically use lean premixing strategies to reduce NOx. These Dry Low NOx (DLN) combustors often premix the main fuel to the edge of flammability and use a less premixed pilot to stabilize the flame. Minimizing pilot fuel minimizes NOx formation, but it reduces combustor stability and increases the risk of LBO and flashback. Emissions of CO typically rise just before LBO.
We studied the configuration of the scaled DLR combustor2, for which high-quality experimental data are available. Figure 6 shows this test rig and Figure 7 the internal geometry.
Figure 6: DLR Scaled-Rig Combustor[2]Figure 7: CFD model geometry
In this simulation, we again used CONVERGE’s detailed chemistry solver, but we used an LES turbulence model. We resolved a velocity field that matched the experimental data and predicted the NOx increase with increased pilot fuel split.
More noteworthy is that CONVERGE also predicts the so-called “knee” in CO formation, the rapid increase at low pilot ratios. Figure 8 shows these CO emissions predictions plotted against pilot ratio.
Figure 8: CO formation at several combustor pilot ratios.
The CO knee is a hallmark of incomplete combustion, and gas turbine design engineers treat it as a signal of incipient flame blowout and damaging combustor-section dynamics.
Critically, this operability limit is signaled by a change in CO levels of less than ten parts per million! We would not expect that a low-fidelity chemistry model (mixture fraction or tabular) could predict these dynamics. How could it? Perhaps such a model could be tuned to generate appropriate CO levels at each pilot ratio, but this would not be prediction. It would bepostdiction.
What’s the common theme? Gas turbine combustion can be predicted accurately with CONVERGE’s detailed chemistry solver. Chemical kinetics are the most critical physics to simulate in these flows, and computational resources spent on other increases in fidelity (e.g., LES) are wasted without it. With CONVERGE, we can accurately and confidently predict critical trace species and unsteady relight dynamics in gas turbine combustors.
How’s that for a hard problem?
Citations:
Barre, D., Esclapez, L., Cordier, M., Riber, E., Cuenot, B., Staffelbach, G., Renou, B., Vandel, A., Gicquel, L., and Cabot, G., “Flame Propagation in Aeronautical Swirled Multi-Burners: Experimental and Numerical Investigation,” Combustion and Flame, 161(9), 2387-2405, 2014. DOI: 10.1016/j.combustflame.2014.02.006
Lammel, O., Stohr, M., Kunte, P., Dem, C., Meier, W., and Aigner, M., “Experimental Analysis of Confined Jet Flames by Laser Measurement Techniques,” J. Eng. Gas Turbines Power 134(4), 2012. DOI: 10.1115/1.4004733
One of CONVERGE’s strengths has always been providing accurate results for complex transient problems with moving geometry. With the introduction of new features in CONVERGE v2.4, this strength extends to steady-state problems.
Two new features in CONVERGE v2.4 that accelerate your steady-state simulations are the completely redesigned steady-state solver and the new multiple reference frame (MRF) approach. These new features work particularly well together when applied to problems with moving geometry but for which you expect a steady-state result. Some examples of such problems are dynamic compressors, fans, and pumps.
The new steady-state solver in CONVERGE v2.4 leverages the steady-state monitor to track the convergence of flow quantities. The simulation begins with an automatically coarsened grid and loose solution tolerances for the governing equations, which allows fluid flow to rapidly propagate through the domain. CONVERGE progressively refines the grid and tightens solution tolerances to improve the solution accuracy as each monitored flow variable falls within a specified mean and standard deviation.
For simulations of devices with moving geometry (e.g., compressors/fans/pumps), the MRF approach reduces computational time with a very small impact on accuracy. The crux of this approach is to treat the moving portion of the geometry (e.g., the impeller for a pump) as a separate reference frame. CONVERGE transforms the flow quantities from the inertial frame to the moving frame to represent the geometry movement.
Figure 1. The fan geometry.
The centrifugal fan example case below compares several approaches to accelerating simulations in which we expect a steady-state result. This case is the ERCOFTAC centrifugal fan with vaned diffuser (Figure 1 below shows the fan geometry). The results shown below in Figures 2 and 3 compare mass flow rate at the outlet for three cases: steady-state solver with the MRF approach, transient solver with the MRF approach, and transient solver with moving geometry.
For mass flow initial conditions, the two MRF cases use a uniform value throughout the entire domain. The moving geometry case, however, employs a more accurate initial condition obtained from prior simulations on a coarse grid. Despite this more accurate initial condition, the moving geometry case takes several thousand more cycles to converge to an accurate steady-state.
Note that the two cases that employ the new v2.4 MRF approach converge much faster than the case that includes moving geometry. Furthermore, the case that uses the MRF approach and the steady-state solver converges the fastest of the three cases, and it takes fewer than 1,000 cycles. Figure 4 below compares the wall clock times for these solver configurations.
All three cases are within 3% of experimental results for mass flow rate and pressure rise.
Figure 2. Outlet mass flow rate convergence history.
When the simulation will yield a steady-state result, the MRF approach combined with the steady-state solver in CONVERGE v2.4 will rapidly obtain accurate results.
Figure 3. Outlet mass flow rate convergence history, zoomed.
Figure 4. Comparison of wall clock time for various solver approaches.
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 for zero-dimensional simulations. CONVERGE also contains a dynamic mechanism reduction method that reduces the mechanism during a three-dimensional 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 0.2% 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.
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.
Someone performs an experiment (typically not the CFD engineer).
The CFD engineer receives the experimental data (typically average pressure trace, apparent heat release rate, emission data, etc.).
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.
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!)
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 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.
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.
Challenging problems in the marine and maritime industry can be modeled with relative ease using FLOW-3D. These application areas include:
Coastal breakwater modeling
Wave hydrodynamics and impact analysis
Offshore platform dynamics
Ship Launches
Sloshing in LNG tanks
Floating barriers
Wave energy devices
These applications can be modeled accurately and effectively using FLOW-3D’sTruVOF free surface interface tracking technique and advanced multiphysics models. This technical webinar will provide an overview of the key features of FLOW-3D relevant to the marine and maritime industry.
Who should attend: practicing engineers working in the fields of offshore, marine and maritime engineering will all find value in this presentation. Additionally, engineers looking to harness CFD to design wave energy devices will find this presentation useful.
In the first blog in the FLOW-3D Cast v4.3 development series, I will discuss the implementation of Global Conditions for Active Simulation Control. Active Simulation Control was first introduced in FLOW-3D v11.1 and FLOW-3D Cast v4.1 to give users greater control over their simulations in terms of autonomous triggering of actions when appropriate conditions are met during the simulation run. In an earlier blog, all the rules and logic behind Active Simulation Control were discussed. These rules and logic work together to provide a flexible and powerful tool that allows users to accurately and efficiently simulate real-world design stages.
Some real-world applications of active simulation are:
Turning on a vent when the pressure at a probe exceeds a specified value
Triggering the motion of an object when fluid reaches a probe (e.g., increasing the plunger velocity from slow to fast in high pressure die casting when metal reaches the gates)
Controlling the addition of metal in gravity casting to stop pouring after metal reaches a particular height in the pour basin or in the sprue to avoid spilling
Introducing Global Conditions
Most often a condition (or a set of conditions) is shared by multiple events. For example, for a high pressure die cycling simulation, probes are placed at the gates. Once the metal reaches the gates (the condition), the following events are set to be triggered:
Increasing the plunger velocity from slow to fast
Decreasing the output interval for selected data
Decreasing the output interval for history data
To simplify this situation of having multiple events that share conditions, Global Conditions have been added to FLOW-3D Cast v4.3. Global Conditions only need to be defined once. Once a Global Condition is defined, it can be selected to trigger multiple events.
Using the high pressure die casting (HPDC) case as an example, the setup for Active Simulation Control is straightforward. First, the user defines a Global Condition that uses the probes at the gates to detect if the metal reaches the gates. Then for each individual event, the user specifies the use of Global Condition. If any conditions need to be changed later, then only the Global Condition definition can be modified. No changes are needed for each event definition. Using Global Conditions makes it easier to modify shared conditions and to keep the condition definitions consistent.
Pump Cover Simulation
This example illustrates an HPDC simulation of a pump cover. The initial plunger motion is computed to minimize air entrainment. Probes are defined in each of the four gates to monitor the arrival of metal. Once metal has reached all four gates, the fast shot phase is automatically initiated. The output frequency is also modified to capture the rapid filling sequence once the fast shot begins. In the animation, three views of the filling can be seen. In the lower left corner, the full geometry including the part, the runners and gates, and the shot sleeve are visible. A view of just the gates with the probes (red balls) is shown at the bottom. Plots at the top of the screen show the fraction of metal at the probes in the gates and the plunger velocity. Notice that the transition to fast shot automatically occurs when the metal reaches the gates as specified by Global Conditions for Active Simulation Control. A time dial – a unique feature of FlowSight – is shown on the lower right. The dial is useful for indicating the progression of time during fast shot. Once the fast shot begins, the output rate becomes very fast.
Global conditions greatly simplifies the definition of shared conditions by multiple events, reducing the chances of error and inconsistency in shared condition definitions, and easier modifications. The addition of Global Conditions to Active Simulation Control will improve the user experience, and make FLOW-3D Cast a better and more convenient CFD software.
In the next blog, I will discuss the new Heat Transfer Coefficient (HTC) calculator in FLOW-3D Cast v4.3.
Flow Science has been named one of New Mexico’s Flying 40. The award, given by Technology Ventures Corporation, recognizes New Mexico’s fastest-growing technology companies. An award ceremony was held on June 7, 2017 at the Albuquerque Botanical Gardens and Aquarium. Representing Flow Science at the event were John Ditter, VP of Software Engineering and Liping Xue, Senior Developer.
Superior productivity and speed, coupled with low heat input are resulting in laser welding processes replacing the more conventional welding methods. In addition, the laser technology has further enabled metal additive manufacturing processes for both powder bed based and metal deposition. This FLOW-3D webinar provides a comprehensive review of our modelling capabilities to simulate laser welding and additive manufacturing processes. We look at how process parameter optimization and relevant physical models play an important role in predicting porosity, surface finish and the subsequent microstructure evolution in welding and additive manufacturing processes.
Visit us in Booth #700 to discuss your simulation needs or arrange a meeting in advance of the show. TechConnect will be held in Washington, DC on May 14-17.
We hope that you can make it to the following presentations that showcase FLOW-3D results.
M1.374 Drop-on-Demand 3D Metal Printing
I.H. Karampelas, S. Vader, Z. Vader, V. Sukhotskiy, A. Verma, G. Garg, M. Tong, E.P. Furlani, University at Buffalo, US
Metal casting simulations are among the most challenging of CFD simulations due to the extreme complexity of flow and physics that occur during fill, solidification and cooling stages of any casting process. This technical webinar presents a broad review of FLOW-3D Cast‘s modeling capabilities, including detailed examples taken from many casting applications: LPDC, full HPDC cycles, centrifugal casting, lost foam, sand casting, continuous casting, tilt pour, and gravity pour in permanent molds. This webinar is now available to watch on-demand.
Tools for Advanced Packaging Design Follow Moore’s Law, Too!
Design Tools: Competitive Advantage
Cooling Power Electronics at Room Level
Medical Software Development – Where Safety Meets Security
Digital Data Storage: Part 1, Part 2, Part 3
Tools for Advanced Packaging Design Follow Moore’s Law, Too!
SemiWiki
High-density advanced packaging technology now requires a set of
Listening to Veloce Customers: Emulation is Thriving Automotive Features Prominently At DAC 2017 Huawei Delivers Outstandingly Accurate Models How Mentor Realized Concurrent Engineering for PCB Design Life on the Edge: CAD Design at the Musculoskeletal Transplant Foundation Listening to Veloce Customers: Emulation is Thriving SemiWiki Emulation is going through a fundamental shift in usage, scope
It was the year 2000 when Toyota Prius went on sale worldwide. How far we have come in the last 17 years: hybridization with Prius to Tesla-catalyzed battery electric vehicles to the launch of Chevy Bolt, first all-electric vehicle with >200 miles for <$40,000! What’s more exciting is that it is just the start. Mary Barra, General motors CEO, captured it perfectly when she said “The auto industry
To understand 1D-3D CFD and 3D-1D CFD, we have reviewed the history of attempts at both loose and close coupling of 1D and 3D CFD codes, the motivations of engineers who did it, and the benefits to be gained from these complementary engineering simulation technologies. Not least, because the umbrella term ‘1D/3D’ covers a number of different possible implementations with their own pros and cons. This paper deals with the use of 3D CFD component simulations embedded inside a 1D thermo-fluid system simulation tool – so-called 3D-1D CFD – and a new workflow conception that we call “Simulation Based Characterization” (SBC) of such system components for added accuracy.
In the last 15 years, vehicle propulsion and powertrain technologies have seen significant innovations, driving the shift from IC engine vehicles to electric vehicles (EV). In this eBook Puneet Sinha considers the emerging trends in this industry: Electrification, drive-range, Formula E and fast-charging. Leading manufacturers including Mitsubishi, Toyota and Lotus discuss their experiences and how they are overcoming the challenges of pioneering in new technology.
For many years, TecIO has been a library of utility functions that allow you to output Tecplot binary files directly from your application. There are two versions of TecIO, one for serial output, and one for MPI-based parallel output.
The serial-output version can output either classic Tecplot binary *.plt (PLT) files or the new Tecplot subzone *.szplt (SZL) files.
The MPI-based parallel-output version can only output the new Tecplot SZL files.
Ever since we initially released the TecIO library we’ve had the intention of adding an API for reading binary data. The advantages are obvious. Reading capability would allow Tecplot binary files to be used as input for further analysis, or as a restart file for an unsteady or iterative analysis code.
Unfortunately, except for a short period several years ago, we never got around to adding read capability. TecIO was misnamed – it really should have been called TecO.
TecIO Reads SZL Binary Files
This has all changed! Starting with Tecplot 360 2017 Release 1, the TecIO library can be used to read data from Tecplot SZL binary files. For now, we don’t support reading of the classic PLT Tecplot binary format. If this is something you are interested in, please let me know ( email scottimlay@tecplot.com).
Our TecIO API’s are written to be compatible with C and FORTRAN, although it should work with any language that can call a C function. In all, there are more than a hundred functions in the TecIO read API. These functions allow you to read nearly any object that can be stored in a SZL file, but you can pick and choose which data you read.
How to Read Zone Data from a File
I will give a brief overview of how you would read zone data from a file, but will skip the APIs for reading geometries, text, custom label sets, strings, and auxiliary data. The APIs for reading these objects are described in chapter 5 of the Tecplot 360 Data Format Guide. The data format guide can be accessed from the Welcome screen on Tecplot 360 2017 or as a PDF file in the doc subfolder of the Tecplot home directory. This information is also available on our website.
There is an example application called rewriteszl in the $TEC360HOME/util/tecio/examples directory that will read a SZL file and write it back out again. Not a very useful utility, but it demonstrates the API.
If you look in the rewriteszl.cpp module, you will see the basic sequence of function calls.
Open a SZL file using tecFileReaderOpen(…)
Read the dataset title using tecDataSetGetTitle(…)
Read the number of variables using tecDataSetGetNumVars(…) and tecDataSetGetNumZones(…)
That is the basic file header data. Farther down in rewriteszl.cpp are the basic functions to actually read the field data. These include tecZoneVarGetNumValues(…), tecZoneVarGetFloatValues(…), and a host of others. In each case the function name gives a fairly good description of the functions purpose.
How to Obtain TecIO Libraries
There are now separate TecIO libraries for scalar and for MPI-based parallel output. Both libraries are included in TecIO library package.
Using MATLAB & TecIO to read/write Tecplot data file formats
30-minute Instructional Webinar
Tecplot 360 helps engineers get their work done quickly and reliably.
It’s not uncommon for engineers to use a dozen different applications on a given project. Meshing, flow solvers, a math analysis package such as MATLAB, custom in-house applications for specialized analysis, post-processing tools such as Tecplot, and let’s not forget everyone’s favorite spreadsheet, Microsoft Excel. Data flexibility in engineering tools is key to helping engineers manage their work day.
Update: During the webinar it we incorrectly indicated that no compiler was required. MATLAB’s loadlibrary() function requires a compiler. Information on platform specific compiler setup is available in the README.txt in the MATLAB-TecIO script download.
This 30-minute webinar provides a method for using MATLAB and TecIO for reading Tecplot files into MATLAB and for writing Tecplot files from MATLAB. This method allows you to take advantage of MATLAB’s wide array of analysis capabilities while still benefiting from Tecplot’s superior plotting power.
Incorporating TecIO into MATLAB allows direct access to read and write Tecplot files, enabling advanced workflows by taking advantage of the power of both products. In our example, a customer using this method was able to reliably post-process his data 3 times faster. The data was exported from Fun3D into Tecplot SZL file format.
“The ability to quickly read Tecplot files directly into MATLAB not only sped up my workflow, it also allowed me to use an existing output hook in FUN3D rather than having to request developer support to provide a custom output for my purpose.”
– Craig Streett, Senior Research Scientist in Computational Aerosciences Branch and NASA LaRC.
Q-Criterion is an important calculation used to identify vortices. In this video we’ll show you how to calculate Q-Criterion, plot the results, and compare the performance of Plot3D and SZL file formats for this work.
In this example, we are using the final time-step of a transient simulation of a wind turbine. An overset mesh was used and is composed of 5863 zones with a total of 263 million elements. The output is in Plot3D file format and the grid and solution files combine for a total of 21 GB.
To calculate Q-Criterion go to Analyze > Calculate Variables and select Q Criterion from the list. The Calculate Variables dialog has a unique feature called Calculate-on-Demand – if you check this toggle, the formula will simply be registered and the calculation will not occur until it is required. This can save a significant amount of time particularly for a data set such as this which has many zones, because the calculation will only be performed on the zones required for the desired plot.
Use an iso-surface, at a positive value near zero, to view the resulting Q-Criterion calculation. Some adjustment of the iso-surface value will be required to see the vortices. Increasing Q reduces the complexity of the iso-surface, but too high of a value makes an iso-surface that is too sparse. It is important to find a value that results in an iso-surface that is neither too dense nor too sparse.
When run in batch mode, the total time to load the data, calculate Q and generate an image with an iso-surface at Q=0.01, without Calculate-on-Demand, was 517 seconds for the Plot3D file and 409 seconds for the SZL file. Using Calculate-on-Demand was 27% faster, taking 372 seconds for the Plot3D file and 299 seconds for the SZL file. Additionally the SZL file is 35% smaller at only 13GB.
This concludes the tutorial on Q-Criterion. Thanks for watching!
Reservoir Simulation Visualization & Analysis with Tecplot RS
Oil and gas projects reservoir models are getting larger and more complex. And the need for effective analysis and communication is becoming more important.
When you finish running a reservoir simulation, you must be able to quickly validate your results, and make crucial decisions about your model. You will also need to communicate your predictions to colleagues and management.
Visualization and analysis tools are becoming crucial in the simulation workflow because they help you fully understand your model. A reservoir engineer’s time is valuable and with pressing deadlines, spending your time sifting through tones of simulation data and more time figuring out effective ways to present and communicate results, is a waste. Your time would be better spent making insightful decisions and discoveries.
You need a tool that can do three things. Organize incoming data, handle the data management process and show your results in a meaningful way. After you have full understanding of the reservoir behavior, you also need a tool that will help you communicate your results to colleagues and management.
After spending years consulting with the reservoir simulation community, we have a solution that makes the post reservoir simulation workflow significantly more efficient. Our tool, Tecplot RS, helps you manage, analyze large amounts of data, uncovering knowledge about reservoir model behavior, and communicate your results with professional images and animations.
By working closely with reservoir engineers, our team has carefully studied typical reservoir simulation workflows. Tecplot RS is specifically designed to streamline processes so that engineers get to their answers in just a few mouse clicks. Efficient methods for loading large data sets along with a simple and easy to use interface help you quickly access the views you need to comprehend your data.
As a full data visualization and analysis tool, Tecplot RS is compatible with many different sources of data. This industry compatibility makes it easy to standardize your results. It also facilitates cross team communication throughout the organization.
Important information extracted from your model can give you fresh insights about reservoir behavior, leading to new discoveries. Tecplot RS has built in statistical analysis tools that pull out detailed reservoir model characteristics. For example, integrating the total oil in place for a grid solution. High quality graphics allow you to view your reservoir from many different perspectives, and actually seeing what is going on inside your reservoir, optimizes your brains ability to make good decisions.
In the oil and gas industry today, affordability is a vital concern. The cost point of Tecplot RS can shift your company paradigm. Your company can now afford to supply a data visualization and analysis tool to everyone on the team. And with each team member accessing the same data, they can collaborate freely.
In summary, Tecplot RS is an all in one XY, 2D and 3D solution. This one tool helps your team easily organize data results, have confidence in model predictions, collaborate across teams and communicate results to management.
A short learning curve, full technical support and free online training, helps your team get up and running quickly.
We will continue to work with the reservoir simulation community to identify ways to improve post processing workflows. Your feedback will continue to help us carve out the future development path for Tecplot RS.
Learn more about Tecplot RS. We’re looking forward to working with you.
The SZL Server setup has been simplified and the performance enhanced in Tecplot 360 2017 Release 2.
SZL Server runs only on 64-bit Linux machines. Starting with the release of Tecplot 360 2017 R1, the shell-script installer is located in the “szlserver” folder of the Tecplot 360 installation. It can also be downloaded from our website.
To begin the installation of SZL Server on the desired machine, run the install shell script and specify the installation directory. Next, you will need to add the bin subdirectory of the SZL Server installation to your PATH environment variable. We do this by adding a line with the following syntax, pointing to the correct subdirectory, to our .profile file.
export PATH=~/szl/szl_server/bin:$PATH
This allows SZL Server to be called from the command line, which is needed to launch it after establishing a connection. SZL server does not run continuously, but is launched by Tecplot 360 to serve only a single data load.
To launch SZL Server from a client machine, open Tecplot 360 and select File > Load Remote Data. The default connection method, SSH Tunneling, requires SSH port forwarding to be enabled and an SSH key file for the client. All SSH authentication options are supported except Kerberos.
The benefit of the SSH Tunneled connection is that it is encrypted for security and can be used when running in batch mode.
Direct connection is unencrypted, but can also be used when running in batch mode and with speeds up to 11 times faster than SSH.
Manual connection provides added flexibility when connecting, but is not particularly useful for batch mode.
2-factor authentication may also be used for added security; however, this requires user interaction, making unattended batch mode operations not possible.
Performing multiple remote data loads by the client will result in multiple instances of the server on the server machine, and once the client disconnects, either by closing Tecplot 360 or by creating a new layout, any server connected to that client terminates.
The SZL Server installation is included with Tecplot 360 software, and as a separate install script on the SZL Server page of our website. SZL Server itself does not require a license, but access to it from Tecplot 360 requires a TecPLUS service subscription. Please email Tecplot Support if you have any questions, and thank you for watching.
New release has an enhanced Fluent loader, expanded PyTecplot APIs, improved SZL Server performance and integrated Q-Criterion calculation.
BELLEVUE, Wash. (April 26, 2017) – Tecplot, Inc., developer of the leading visual data analysis software for engineers and scientists, today announced the general availability of Tecplot 360 2017 Release 2.
Tecplot is committed to improving the power, performance, usability and reliability of its industry-leading flagship data visualization product. Many improvements in Tecplot 360 2017 Release 2 add up to major benefits for Tecplot customers.
Highlights of this release include:
Enhanced Fluent Loader
Significantly improved performance and reduced memory requirements for data with shared grids (typically time-dependent solutions).
Fluent Additional Quantities can now be loaded.
Variables in symmetry zones can now be loaded.
Fluent variable name map updated to give more variables meaningful names.
Increased responsiveness and progress feedback while reading Fluent data.
In our test using a transient solution with 30 time steps of about 1 million data points each, time to first image fell from just over 74 seconds to under 4 seconds (a reduction of 95%), and memory use was cut by 93% (both compared to Tecplot 360 2017 R1).
Expanded PyTecplot APIs
PyTecplot, Tecplot’s Python API, has expanded to cover Interpolate, 2D and 3D vectors, Streamlines, View adjustments, Contour and line legends, 3D orientation axis, Auxiliary data, Node map, face map, and face neighbor information, Solution time for transient data and Slice extraction.
Improved SZL Server Performance
SZL Server, Tecplot’s client-server module, supports two-factor authentication and password authentication for SSH connections. Performance for data sets with a large number of zones has also been improved. In our test involving a 681-million-cell simulation with 81 time steps accessed from Tecplot SZL Server over a SSH-tunneled connection, the time needed to open the file and display all time steps in sequence was cut in half, from 1,737 seconds to 834 (compared to Tecplot 360 2017 R1).
Integrated Q-Criterion Calculation
Q-Criterion, a method for vortex identification, is now built into Tecplot 360’s CFD Analyzer. This integration makes the calculations faster, more memory efficient and easier to use. In our test using a Plot3D data set with 5,800 zones and 263 million cells, calculating Q-Criterion using Calculate Variables was more than 6 times (84%) faster than a macro we provide for the calculation (from nearly 47 minutes to 7 minutes and 20 seconds). Using a SZL file format further reduced the time required by 22%.
TecIO Writes Individual Zones in Excess of 2 Billion Nodes
The TecIO library is provided to allow third-party applications to read and write Tecplot file format. The library now includes an easier-to-use API for writing SZL files. The new release of TecIO supports 64-bit indexing which allows individual zones to exceed two billion nodes! The new API is also more flexible in the order in which it accepts data, which can help lower the amount of memory needed to write files.
“If you still think that Tecplot can’t handle big data, take a look at the new TecIO API” said Scott Fowler, Tecplot Product Manager, “TecIO now supports 64-bit indexing, which allows the writing of individual zones in excess of 2 Billion (yes, with a ‘B’) nodes!”
Chorus has Improved Ability to Investigate Trends
Chorus, included in Tecplot 360 2017 for exploring large data sets composed of multiple solutions or experiments, has improved the ability to learn more about data trends by allowing the use of variables to define the line color and symbol shape.
Tecplot 360 is the fastest, most memory-efficient CFD post-processor available for desktop computers. 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. 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.
In January 2017, three powerful modules were integrated into Tecplot 360:
Tecplot Chorus, an analytics tool for exploring large data sets composed of multiple solutions or experiments.
PyTecplot, a Python API for automating workflows.
SZL Server, a client-server module for accessing data remotely.
Tecplot, an operating company of Toronto-based Constellation Software, Inc. (CSI), is the leading developer of Visual Data Analysis software for engineers and scientists.
CSI is a public company listed on the Toronto Stock Exchange (TSX:CSU). CSI 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 visualization.
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.
News broke this morning that Dassault Systèmes has acquired AITAC BV, a Dutch company that uses the DS suite of products to design superyachts, cruise liners, frigates, tugboats and other smaller ships. As a complement to its services offerings, AITAC devised Smart Drawings, a software application that automates drawing creation from a master 3D model created in CATIA applications. If you’re familiar with the marine world, you’ll know that the industry is driven by classification society and other standards; Smart Drawings automates drawing creation, to speed up and cut the cost of drawing production. That’s smart; much of this work doesn’t create extra value (other than, of course, getting certification) and it’s more likely to come easy when the documents are exactly as required.
DS will integrate Smart Drawings into the “Designed for Sea” and “Optimized Production for Sea” 3DEXPERIENCEs. Alain Houard, DS’ Vice President, Marine & Offshore Industry, said that “AITAC’s long-term experience, expertise and team of naval architects and engineers will help us to extend our marine and offshore portfolio’s capabilities and support customer deployment projects.” In addition to the software, DS says that the deal also gives them “40 percent of AITAC’s marine and offshore engineering office in Croatia, AITAC d.o.o., a provider of naval architecture and engineering services to major shipbuilders.” (It appears that AITAC retains its operations in Germany and France as well as 60% of the Croatian team, and its PLM Solutions and Consultancy based in Netherlands.)
The details of Smart Drawings may be interesting to only me, a naval architect who once worked for the American Bureau of Shipping, but you should find the deal curious, too. DS has been selling in the shipbuilding world for decades, partnering in the late 1990s (early 2000s?) with Intergraph for a major US military contract and showcasing how its submarine and aircraft carrier clients –as well as the far more numerous but smaller commercial and leisure boat clients– use CATIA in design. All of these vessels have to be approved of by regulators and that is typically still done by 2D drawing. Automating this process makes so much sense. What is curious to me is the staff: does DS plan to pursue project work from the Croatian office? Will they bid on new ship design contracts? And will DS use this as a model for other industries? In other words, does DS want to follow the model of CAE-ish companies and build a services capability? That’s a terrific way to get close to customers, partnering with them on complex engineering challenges — but it’s also risky, in that those resources aren’t cheap and need to be kept near 100% utilization.
Of course, this is a tiny acquisition for a tiny piece of DS’ overall business. How tiny? Shipbuilding isn’t even called out specifically in DS’ end-industries-by-number, so I’d guess it’s less that 5% of total revenue, and probably much less. DS does say that its “fastest growing industries in 2016 included … Marine & Offshore in Diversification Industries”, but it was listed last in a category that also includes consumer packaged goods, AEC and EPU (energy, process and utilities).
Bottom line: I like DS’ diversification push into new industries. I like automating tasks that add little value and that can extend the importance of the 3D asset. And I’m very interested to see what DS does next with its team of naval architects and engineers.
This acquisition was closed on 1 June; financial details were not disclosed. But I would imagine no material impact on DS’ top or bottom lines. (And, yes, AITAC is CATIA spelled backwards.
I know I’m way behind on event reports but need to let you know about this:
Earlier this week, OpenText, the document management and collaboration vendor, announced that it is acquiring Covisint Corporation, a cloud platform for automotive and Internet of Things (IoT) applications. Why does this matter? Because it’s another sign that there’s huge interest from all sides of the corporation in IoT, though not many clear indications of how it’s all going to shake out.
So, why Covisint? There’s a wealth of data stored in OpenText (just as there is in ERP, PLM and all of the other databases hanging around but not connected) that can be used to make better operational decisions. OpenText plans to use Covisint to build a cloud-based IoT platform, initially targeted at the automotive industry. You may recognize the name: Covisint, was started back in 2000 as an online marketplace by Ford, GM and what was then known as DaimlerChrysler. Like lot of the markets created during the Internet bubble, it struggled to generate revenue and most of us lost track of it. A quick SEC search says that it was sold to Compuware in 2004 and spun off via an IPO in 2013.
Today, Covisint is a provider of cloud-based services for projects like connected cars. This includes something called identity management, a crucial piece of technology that secures what Covisint calls the identity lifecycle: managing authorizations “to provision, revoke and audit access to multiple enterprise applications”. The point: providing convenient access without compromising security. That’s critical in all IoT implementations, and OpenText hope to parlay Covisint’s roots in automotive into other industries in which it operates.
And, why now?Covisint has been struggling and investors have called on it to explore ways to build shareholder value, including letting itself be acquired. Covisint reported total revenue of US $70 million last year, down 8% from the prior year. OpenText’s purchase price totals US$103 million, quite a bit lower than some investors had been expecting but still a decent 20% premium over the recent share price.
OpenText intends to fund the acquisition with cash on hand. The transaction is expected to close in the third quarter of 2017 and is subject to customary closing conditions, including approval by shareholders of Covisint. I’ve seen rumblings on line that some are hoping for a higher offer, but it’s not clear that one is in the offing.
I think this acquisition tell us a lot about the IoT. It’s a wild wild west, with no clear winners in sight. Will OpenText win the battle for connected cars? Remains to be seen. But even if it doesn’t, Covisint and its identity management software just gave it entry into a lot of conversations it couldn’t have had last week.
Altair continues to expand its portfolio, yesterday adding MODELiiS, a French supplier of electronic design automation (EDA) software for circuit design and simulation. MODELiiS spun out of an entity called EASii IC, which develops Application-Specific Integrated Circuits (ASICs) and other electronics for clients; MODELIiiS is a set of digital and analog system modeling tools developed over the last 15 years and commercialized in 2015.
EDA is totally not my area of expertise, so I don’t really know other than to be impressed when MODELiiS throws around terms like “SPICE parser and elaborator”, but Altair CEO Jim Scapa says that MODELiiS expands Altair’s EDA capabilities “to deliver the most relevant, optimal, simulation-driven design solutions for IoT. Simulation and optimization are fundamental to the design of communication and controls for everything from personal devices to autonomous vehicles.”
As far as I can tell, MODELiiS is tiny and so would not add materially to Altair’s revenue or sales/service reach. But it sounds like a technology that can be used to create electronics-aware models of complex systems, for modeling in Altair’s HyperMath, LABVIEW, Modelica, MATLAB or similar tools.
Over the last few weeks, CAE companies have released earnings that highlight the growing importance of CAE technology in many companies’ product development processes, and the impact these technologies are having on the enterprises that use them. First, key highlights from each company and then some general thoughts.
ANSYS remains the biggest CAE player, and uses its size and breadth of offering to play at the highest levels in its accounts. The company reported total revenue of $254 million, an increase of 12% as reported and up 13% in constant currency (cc). License revenue was up 13% to $142 million while maintenance and services revenue was up 12% to $111 million. On a non-GAAP basis, lease revenue was up 15% to $94 million while perpetuals were up 9% to $48 million. Non-GAAP maintenance revenue was $104 million, up 11%. ANSYS reported good progress in leveraging its large portfolio of solutions in enterprise sales and cross-selling, which led to 31 customers placing orders over $1 million, including 5 customers with orders over $5 million. That’s up from 22 and one, respectively, Q1 2016. For Q2, ANSYS expects GAAP revenue to be between $254 million and $263 million and, for the year, revenue in the range of $1.029 billion to $1.057 billion.
ESI Group reported yesterday that revenue in the calendar first quarter was basically flat year over year, with revenue of €27 million. In cc and on an organic basis, revenue was down 3%. License revenue was up 3% to €20 million, even as new license revenue was 20% due to “the very challenging base effect which included exceptional growth of 43.5% in Q1 2016”. ESI doesn’t offer guidance, and the Financial Times website today seems to be a bit befuddled, so we don’t have a forecast to share. In general, though ESI’s revenue is heavily weighted towards Q4 with the company picking up steam as the year progresses.
Finally, Exa announced results last night that show a slight overall revenue decline in Q1: Total revenue was $16.6 million, down 1% as reported but flat in cc, with license revenue of $14 million, up 3% (up 4% cc) and project revenue of $2 million, down 22% as reported (down 21% cc). Exa’s revenue tilts heavily towards the automotive industry, and uncertainty in the US about regulatory changes to emissions and efficiency requirements dampened buying, as expected, said CEO Steve Remondi. This “temporary pause” affected both license and project revenue, as customers wait to see what develops in regs, NAFTA and other initiatives. Exa forecasts Q2 revenue in the range of $17.1 million to $18.1 million and fiscal 2018 revenue in the range of $76 million to $80 million.
So, what did we learn? That CAE is increasingly important strategically –a car or truck that doesn’t meet forthcoming regs won’t be successful and that things screech to a halt when that end-game isn’t clearly defined. But it’s also clear that diverse suppliers, like ANSYS, are better able to balance uncertainties in one market with success in another. Does this mean all CAE will consolidate? I sure hope not; some of the best innovations happen when small, hungry companies bring their ideas to the market.
Does ANSYS’ growth in Q1 come at the expense of Exa and ESI? Probably not. ANSYS has many levers to pull, while the smaller companies are more affected by a handful of deals that do or don’t happen in any time period. What’s positive across the board is that license revenue was up for all three vendors. More people using CAE means more seeing the benefit, showing off their blue to red plots, using the results to make business decisions — and CAE will continue to spiral upwards. We also need to remember that we’re in a transition phase, when many companies are hearing the rumblings about cloud and subscriptions and are considering what these technological and business changes might mean for them. It would be truly helpful if CAE companies would report number of users or licenses or tokens or keys sold, so that we could see another measure of growth besides revenue, but I don’t see that happening any time soon.
Every year’s first quarter is a mixed bag. It’s the end of the financial year in Japan, traditionally a big market for CAE. But it’s also the first quarter of the new fiscal year for most companies in North America and Europe, so budgets there need to get the group through a whole year. We can’t read too much into 2017, just yet. As ANSYS CEO Ajai Gopal said, “simulation is the most important solution companies have to help them address product complexity and accelerate time to market.” I think it’s going to be a good year for CAE.
AVEVA just announced that, as expected, total revenue was up as reported but down in constant currencies, as the British Pound is weakened over Brexit uncertainties. In general, the results seem are due to slumping demand from companies that would normally use AVEVA’s PDMS, E3D, AVEVA Net and other products on oil and gas projects, many of which are on hold until that business improves. Some details:
Total revenue for the year ended March 31 was £216 million, up 7% as reported and down 4% in constant currencies (cc)
Recurring revenue holds steady at 77% of total revenues, but the mix shifted slightly, as rentals decreased 5% while maintenance was flat. The announcement said that rentals were down because of lower demand in Latin America (mostly Brazil) and lower activity in oil and gas. Initial license fees (new license) revenue was down 3% while training and services revenue was down 14%, reflecting lower activity
By region, revenue from Asia was down 6% cc as Marine continues to struggle in South Korea and India; China and South East Asia were flat, performance in Japan was “strong”. Revenue from the Americas was up 4% cc, led by strong performance in North America, while Latin America continues to be weak. Revenue from EMEA were down 4% cc
Much more after the earnings call. Now, PTC Liveworx.
I was just at SAPPHIRE, SAP’s massive user conference. Quite a few very kind attendees explained to me how they couldn’t do their finance, HR or supply chain management jobs without SAP’s solutions — but I went to learn about Leonardo, SAP’s IoT platform, and especially about how SAP is looping its Fedem acquisition into IoT. More on that, below.
First, though: SAP isn’t at all what I thought it was. It’s way bigger than I realized. CEO Bill McDermott (whose slide is the title image, above, with him on stage) said that SAP manages 76% of the WORLD’s transaction revenue, $39 TRILLION of the global private sector GDP, 83% of business to business transaction revenue, $22 TRILLION in consumer purchases around the world –joking that Black Friday shopping couldn’t happen without SAP– and helping to manage $70 TRILLION in customers’ banking assets. SAP’s revenue was €22 billion in 2016, so its global impact has a serious multiplier effect.
Next, I thought SAP was a back-office-y, CEO/CFO-y set of technologies that are part of the price paid to carry out the real business of a company. Not so. The C-level and department heads attending SAPPHIRE told me that they use SAP to fine-tune their businesses, managing assets (including money but also real estate, inventory, people, supply chains, recipes/bills of material, and so on) to meet their strategic goals. Since I last tuned into SAP, it has gone from managing transactions like matching an invoice to a received item in inventory to using that information to create analytics: which suppliers deliver late? How can we optimize our inventory policies to reduce carrying costs but still meet availability goals?
Finally, I spoke to more business leaders at SAPPHIRE than IT department types. There were absolutely IT types at the event (and a solid chunk of the 30K attendees were implementation consultants) but SAP increasingly offers much more than tools. Mr. McDermott’s vision is to harness all of the data already warehoused in SAP systems plus analytics to reinvent many traditional corporate functions, like accounting. If a machine can learn how to match invoices and supplies, what could those accountants be doing? Another thing I hadn’t realized was how conservative this crowd is; thinking through how roles might change in the corporate back-office because of technologies like machine learning was both invigoration and frightening to this crowd. They’re excited about the possibilities but worried about how this will affect long-time employees. It seemed, almost, as if the wave of automation that hit manufacturing in the last 15 years is breaking over the business parts of the enterprise today — and that’s making people nervous.
That’s all fascinating but I went to SAPPHIRE hear about Leonardo, SAP’s offering for IoT. Leonardo debuted late last year as a platform to improve operations and maintenance via connected machines, doing proactive monitoring to improve asset utilization. The usual. Last Tuesday, SAP changed its game — and it was all over SAPPHIRE. Every wall, every SAP presentation, most of the signage in the enormous Orlando Convention Center urged attendees to check out Leonardo. So what is it? Not entirely clear, but here’s what I understand: Leonardo is machine learning, artificial intelligence, the data stored in SAP HANA (its database architecture) or other sources, sensor inputs, analytics, IoT and blockchain, some of all of which can be used to create intelligence and, if appropriate. automate a reaction. This could have been better articulated, but I expect the message to evolve.
To some extent, Leonardo is a set of tools looking for a problem to solve. What might those problems be, outside the predictive maintenance realm? To help answer this question, SAP connected me to Rafael Costa of Stara, a Brazilian company that manufactures of tractors, seed spreaders and other farm equipment for the local market and for export. Mr. Costa said that Stara saw the opportunity to help its customers, especially the large agricultural concerns that feed Brazil, run their farms more profitably. His team figured out how to use GPS to guide the equipment (not in itself, new) and sensors on the equipment to measure planting and spraying, how much overlap there is between passes — and tie this to the farm’s SAP to monitor efficiency and effectiveness (new). It ties the spot price of soy beans to the efficiency of a harvester. Or the exact amount of fertilizer that was spread on each plant in a field to the overall crop yield. Precision farming takes the tractor to the cloud. I’ll write more about this soon.
But back to the real reason I went to SAPPHIRE: Fedem. You may remember that Fedem was, in the 1990s and 2000s, a multibody dynamics product that tried to compete against MSC’s Adams — at that time, the 100-lb gorilla. it was tough, especially in North America; the company foundered, went through changes in ownership and management … Then, last year, it was snapped up by SAP as part of an overall vision for the digital twin. Fedem debuted this week on stage at SAPPHIRE, as a component of SAP Leonardo:
Today, Fedem (a product yet to be renamed in the SAPness of it all) is a part of a solution to monitor and analyze the real-time behavior of structures under dynamic load. The picture above is from the SAPPHIRE keynote, showing the presenter in a VR headset interacting with the digital twin of a wind turbine, Fedem’s first Leonardo application. It’s a combination of the CAD model of the asset, real-time sensor data from the pylon and Fedem’s super-fast finite element analysis. The idea is that this digital twin app can be used to monitor the asset, authorize in-person inspections, and decide whether it is more economic to run the turbine in a particular wind state or feather the blades to lessen wear. The secret sauce: since the FEA is based on sensor data, it can also imagine “virtual sensors” placed at points of stress, like the bolt in red on the lower part of the pylon. The response surface shows stress at the bolt under various wind conditions and can be used, along with other data in the SAP universe, to determine the location and availability of inspectors, spare parts, installers, energy demand and pricing, and so on. (I think the VR component was to up the coolness factor in the on-stage demo. I’ve never seen a demo from the SAP Fedem team that uses it; a regular browser is perfectly sufficient.)
SAP co-founder and current chairman of the board Hasso Plattner told attendees that “we’re still washing our data by hand. What could we do if we automate?” SAPPHIRE was all about that automation, whether in asset operation, predictive maintenance, precision farming or analyzing sick days trends. For the conservative attendees, it seemed to be like sitting at the top of a roller-coaster: you know you’re going, you’re a little bit afraid and more than a little excited. More soon.
Note: SAP graciously covered some of the expenses associated with my participation in the event but did not in any way influence the content of this post. The title image is a photo I took during Mr. McDermott’s keynote; the Leonaro/Fedem/IoT image is a screen grab of the replay of the keynote session. You can see replays here: http://events.sap.com/sapandasug/en/home
Working with one of the world’s top big wave board shapers, engineers at CRAFT Tech have applied computational fluid dynamics (CFD) within a design optimization process, employing a genetic algorithm to evolve the design of a big wave surfboard. In this article, we will explore the design framework that allowed over 100 design iterations to be explored, resulting in a higher speed, lower drag, big wave surfboard design. (
that is somewhere in the range of the simulation. Thus I chose Tref=300. Somewhere in the middle!
and 
) and their stagnation property as a function of Mach number and the ratio of specific heats, 
. Recall that Mach number is the ratio between the velocity and the speed of sound, a.
which in this case is 2.
at h=0 -> -0.029941
at h=o -> 0.2954332
is an optional (but always recommended) safety factor.
Today we celebrate International Women in Engineering Day. Recently, I had the opportunity to meet with Dr. Shini Somara over coffee one afternoon. Shini is a broadcasting science and technology journalist who reports engineering and innovation on television and online, both in the UK and USA for networks such as the BBC and PBS. She began her career as a fluid dynamicist, having studied mechanical engineering at Brunel University and has always been passionate about making the invisible, visible! An interest that began with Bernoulli’s equation and has continued through her communication of science. 
I’m happy to announce that our team will once again be showcasing our industry-leading solutions at the 54th Annual Design Automation Conference (#54DAC) in Austin, TX. I invite you to stop by and meet with our domain experts in booth 647, from June 19-21, to learn how our industry-leading technology can help meet your SoC design challenges with production-proven solutions.
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.
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 
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.
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