What LES should do for you?
I often read people writing about using "LES turbulence model", therefore I open this post since I am curious about your ideas of what LES is and what LES should do for you.
You are also probably aware of the paper http://iopscience.iop.org/13672630/6/1/035 
Working on it since 2008... still have to find out :rolleyes:
Jokes apart, i don't trust Unsteady RANS (or RANS, for that matter) 
I'd say provide better results than ranse (not necessarily the right ones, just better would be enough) in all the areas where ranse fail.

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I expect from LES just what its definition foresees, i.e., an underresolved, poorly modeled direct numerical simulation of NavierStokes equations. LES should provide, with a reasonable computational cost, the correct underlying physical processes (within modeling limits), should automatically distinguish between laminar and turbulent zones, catch unsteady phenomena, give accurate macroscopic results… in a few words, provide something which is as close as possible to reality. Considering the endless modeling, numerical, filtering, mathematical and even philosophical issues inspired by LES during the last decades (see ten questions by Pope, for instance), I think this is the minimum we must expect from it! 
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Can any body tell me, what is the URANS actually do? 
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So whats the cheaper way of getting an engineering problem solved? 
I know it is viable engineering approach. I actually cant understand the URANS philosophy. This approach may be good for the laminar transient flow, but not for the highly turbulent transient flows.

Just think of these two philosophies:
LES > filter G(xx'; Delta_x) URANS > filter G(tt'; Delta_t) Actually, an example would be useful, the cycle of a piston in engine. RANS will set a (hypotethically) statistical steadystate for all the cycles (1 cycle = 360 degree). URANS will give you the unsteady (statistical) behaviour during the 360 degree. Each time is representative of the average of N events at that time. 
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Does LES is useful only if RANS/URANS fails or you really need as much details as LES can provide?

LES for me is one thing above all: an intellectual challenge, and a good meeting place for fluid physicists, mathematicians, numerical analysis, high performance computing people....
plus it is damn fascinating and also frustrating :) seriously: For me, it is a great research tool to do research with and to do research on! 
good !
just to stimulate the discussion, I do a simple example... consider the "academic" testcase of the liddrive cavity at Re=1000. It is a laminar case. Then you run your code as it is on a uniform grid of 10 cells for side. You have a Re cell number =100 which means you are running the code in unresolved condition. For you, is that an LES? In other words, any time you use a CFD code in unresolved conditions, you are filtering something and implicitly doing an LES? 
well, my answer to this question is twofold:
1) yes, everytime you are using any type of discretization, you are doing an "unresolved DNS".... one might call that an implicit LES.... As soon as you are introducing a filter through approximation(by projection onto your approximation basis, e.g. phi=1 (the mean) in FV, dirac in FD, polynomials in FE) , you are no longer solving for u (the DNS), but for u(bar) (the filtered u). Plus you are adding approximation errors through your discretization of continuous term (like difference equations for derivatives), so, you what you are in reality dealing with is something like u(hat)_h. 2) Should every underresolved DNS be called an LES? No, I don't think so, because for me there's more to a LES than just picking a low resolution for a high frequency problem. But that's open to discussion, implicit filter implicit SGS LESpeople would disagree maybe :) Very interesting discussion! What are your thoughts about this? Cheers, all the best! 
I agree :)
I think that a cfd user must be aware of the fact the running any numerical simulation will allways produce an intrinsic filtering. The "label" of the simulation can be DNS or LES depending on the ratio between the Kolmogorov and the Nyquist frequency scale. I think that if this ratio is no longer than 10 we can still talk of real DNS. When this value exceeds 10 I use to call "unresolved DNS" or, better, "nomodel LES". In my experience, despite we know that is wrong, nomodel LES often produces better results that LES modelled with an eddy viscosity SGS model. So, further question arise, what an SGS model should do for you? 
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but I have never tested that, and is doesn't really matter, I guess, plus that's then often so close to the numerical double precision barrier of 10E16.... so it is just a theoretical point... Quote:
a) interaction of the numerical errors and SGS model... I have the feeling that this often makes the overall results worse than without a model. b) here's another idea, although it just a feeling, not a fact: eddy viscosity models (at least the Smagorinsks) rely on two things: 1) the equilibrium of dissipation and production, i.e. isotropic turbulence and 2) the alignment of resolved strain and stress. Both assumptions are invalid in many cases, BUT (here comes my feeling) if you use your discretization error as the model (or the Riemann solver /flux function in FV), then your discretization has the chance of introducing the anisotropy naturally... I'm not sure if I can express it in a good way, but I have the feeling that eddy viscosity models are more "isotropic" than your discretization dissipation. Quote:
For me, it should a) turn off when laminar b) allow backscatter c) remove the energy, of course, at the correct rate d) (maybe) even be clever enough to remove any aliasing errors I'm introducing through inexact nonlinear terms... e) not assume isotropy of strain and stress f) not destroy the time step g) not interfere with parallelization wow..... can think of more things, but have to get dinner now! until then! 
Very interesting discussion! :)
In my opinion the major drawback of many "classical" SGS models is that they have been derived within a formal mathematical framework which starts from the analytically filtered NavierStokes equations. As a consequence, such models imply a filter to the simulation rather than adapting to the real effective filter imposed by discretization and numerical scheme. One should rather start from the discretized form of the governing equations, but unfortunately the discretization operator is often unknown... however one interesting contribution in this vein was the one by Carati & al: http://journals.cambridge.org/action...line&aid=82969 Another interesting point is the one made by cfdnewbie about the alignment between resolved strain and SGS stress models, and the inherent anisotropy introduced by numerical schemes. In fact, in the book on ILES by Grinstein et al. the authors show via ModifiedEquation Analysis how some flux reconstruction schemes are equivalent to scalesimilarity SGS models, which are able to capture anisotropic effects. I also agree on what a SGS model should do, and would also add: catch correct nearwall dependence. 
I agree that the eddy viscosity assumption suffers of many limitations. However, nonisotropic eddy viscosity SGS provides somehow disappointing results, as well ...
In my experience the twoparameter dynamic mixed model is the more accurate, it provides good zeroorder statistics as in the nomodel LES but also quite good high order statistics. Deconvolutionbased modelling are also advisable for smooth filters. Now, my next question is: what do you intend for an LES?  a numerical approximation of a mathematical system of PDE obtained by filtering the NS in continuous form  a way to model all is unresolved by the discretized NS equations In the first case, in order to accurate represent the filtered solution, the ideal numerical method should minimize the local truncation error, that is exactly in opposite sense of the ILES approach. 
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or am I missing something? 
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