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April 5, 2014, 23:38 
CFD for separated flow

#1 
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Meimei Wang
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Hi,
It is well known that CFD won't work that well when the boundary layer separation happens. I'm wondering what is the main reason of that inaccuracy? Is the inaccuracy mainly from turbulence modeling? Or it is mainly because the discretization error expands when boundary layer separation introduces much larger gradient at the certain areas? If the main reason of the inaccuracy depends on the application area, let's take airfoil or aircraft wing stall simulation as an example.
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April 6, 2014, 10:20 

#2 
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Where is it wellknown?


April 6, 2014, 10:57 

#3 
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venki
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Try with SST model, it will predict better result


April 6, 2014, 23:54 

#4 
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Meimei Wang
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I read this paper from Airbus which states that boundary layer separation prediction is much more difficult than not separated flow for aircraft design.
http://www.mathematicsinindustry.com...5983110.pdf It states that 'the regime of ﬂow separation onset up to maximum lift conditions is still not modelled accurately enough, nonlinearities and turbulence modelling for separated ﬂows are still a major concern.' But I notice that the grids independence for boundary layer separation area is very difficult to reach. So I doubt whether turbulence modeling or discretization error is the main reason for inaccuracy of predicting separated flow with the current CFD technology.
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April 6, 2014, 23:56 

#5 
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Meimei Wang
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Certainly SST model will give much better results for separated flow than the other 2 equations model. Someone even suggests DES or LES model. But the inaccuracy is generally still much larger than boundary layer attached flow. What is the main origin of the inaccuracy?
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April 7, 2014, 03:45 

#6 
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venki
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Still you can reduce if you go higher order schemes


April 7, 2014, 03:47 

#7 
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venki
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turbulent structures


April 7, 2014, 03:48 

#8 
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Meimei Wang
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Could you theoretically explain that is why?
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April 7, 2014, 03:49 

#9 
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Meimei Wang
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What do you mean? Current turbulence modelings can't well capture the boundary layer separation turbulence structure?
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April 7, 2014, 04:44 

#10 
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venki
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Ya correct, you have very gud computation facility , try with DNS


April 7, 2014, 11:23 

#11 
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Joachim
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Hey!
Yes, you were initially right. The problem comes from turbulence modeling. As you may be aware of, you have a wide range of methods for modeling turbulent flows. The wide majority of them are based on the gradient hypothesis, which assumes that the Reynolds stresses are proportional to the mean strain rates (in 2D, you typically see bar{u'v'} = nu_T du/dy). This hypothesis is mainly responsible for the failure of the models in separated flow. The concept of eddy viscosity is pretty questionable. For example, you might notice that the Reynolds stresses come from the convective terms in the NavierStokes equations, but are modeled as viscous terms (eddy viscosity). People use these models because they are simple and provide reasonable estimates for nonseparated flows. If you want to get rid of this assumption, you have to go back to the Reynolds stress models, which are FAR more complex (lots and lots of additional equations and variables). The worst part is: the results are not even much better, since you still need to model a large number of terms in these additional equations! So basically, all models based on the gradient hypothesis are doomed. That includes most algebraic (mixing length...) and one and twoequation (kepsilon, kw,...) models. What people have typically done in the past is simply 'tweak' the models with empirical correlations to improve the predictions. If you look at the kepsilon model for example, you have at least 6 constants that were defined based on canonical flows (usually, like isotropic turbulence, etc). If you move away from these basic test cases, your predictions get worse and worse... Tweaking the coefficients to get a better agreement for one flow is not the best solution ever, but well... One solution could be to reduce the importance of the turbulence model. In order to do that, you can use LES for example, where only the small scales of turbulence are modeled. However, as you might be aware of, the computational cost increases significantly. Industry probably won't use LES on a daily routine before 2050 at least. DNS is even better, but we are still FAAAAR away from using it for engineering problems (2080?). I hope that helps, Joachim 

April 7, 2014, 17:15 

#12 
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Filippo Maria Denaro
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"it is well known" that turbulence modelling is the problem when using statistical approaches (RANS/URANS) ...
LES would totally change this statement, provided that the grid resolution is adequate (and you have the computational resources) 

April 8, 2014, 04:09 

#13  
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Meimei Wang
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Quote:
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April 8, 2014, 04:28 

#14 
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venki
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If you super computer you can try now itself


April 8, 2014, 04:31 

#15 
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Meimei Wang
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I can know a lot of things if I try. But the way to ask questions here is to save the trying time.
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April 8, 2014, 04:49 

#16 
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venki
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If your model periodic r symmetric model, Axis symmetric model, you can reduce your time.....


April 8, 2014, 08:01 

#17 
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Joachim
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Venkateshaero, I do not believe that using LES/DNS all the time is the right way to go. In my opinion, if a panel code is sufficient, why waste resources and time running more expensive methods? You need to know the exact limits of your model to determine which ones are applicable to your problem, and which ones are not. Anna Tian is doing the right thing when asking these questions.
DES will probably become the workhouse of industry within 10 years I guess. The concept of DES is elegant because of its simplicity. In order to understand it, you have to understand the differences between RANS and LES. In RANS, ALL scales of turbulence are modeled. Your code will only solve the equations for the mean flow. The turbulence models therefore represent the impact of the missing 'eddies' on this mean flow. The good thing is that you no longer need a super fine resolution. Only one that can accurately capture the mean flow. On the other hand, LES is based on Kolmogorov's hypothesis, which assumes that turbulence is universal at the small scales. Basically, if you have a flow behind a car or a plane, you will see big structures in the wake. These are geometry dependent. However, these large eddies will generate smaller eddies, which in turn will generate smaller eddies, etc (energy cascade). At one point, these eddies will become independent of the flow that initially generated them. This is a powerful concept, because it means that the small scale turbulence is the same for a flat plate, a car, a plane, etc. Therefore, it could be modeled pretty efficiently! That is what LES is doing: the problemdependent scales (large) are resolved as part of the simulation, and the small scales are modeled (using Smagorinsky or whatever LES model). In the case of LES, you apply a filter to differentiate the resolved and modeled scales. The local grid size is typically used to defined the filter width. All scales that are locally smaller than the grid size cannot be resolved, and are therefore modeled. This is the MAIN difference between a RANS and a LES code. In LES, your model will depend on the grid size. I am not talking about the accuracy of the model here, only its definition. If you compare Smagorinsky and any RANS model, you will realize they are very similar. However, LES models typically have a delta function somewhere, which represents the filter width (ie grid size). Here is the idea behind DES: Since we already have a model for the turbulent kinetic energy (Spalart Allmaras for example), why don't we just use it for the subgrid scales too? The SA model is therefore modified to turn into a subgrid scale model when the grid allows it. You can therefore have good resolution at the wall (without the crazy resolution required by LES) and still capture pretty accurately 3d unsteady separated features for example with LES like models. All that with a single, simple model. Pretty cool. However, you might realize that DES is not super physicbased. Usual LES SGS models are based on the filtered TKE equation and so on, whereas DES is just based on a modified version of the RANS TKE. You are using the same model for two different things. This is why some people are using another approach (hybrid RANS/LES), where you keep both the RANS and SGS models, and blend them to get the solution. DES has a few issues that still need to be resolved. However, you were right, it typically gives way better results than RANS models, without having the prohibitive cost of LES. Damn, I am writing too much. Good luck anyway! Joachim 

April 8, 2014, 09:04 

#18 
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venki
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You are correct,Thanks Jaochim.


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