if the filter width Delta is implicitly related to the grid size h, then yes, the computational grid measure the smallest resolved scale..
However, it is possible to use an explicit filtering in LES. In such a way, the filter width can be as larger as you want, for example 4 times the computational grid size or greater

Let me pic up this topic.
Recently, I experimented with a Rayleigh Taylor Instability using Fluents VOF method just for fun (see the picture on the left).
As it turned out, even with an undisturbed initial solution (a perfectly flat interface) I was able to trigger the instability. It is the roundoff errors that accumulate and produce the initial disturbance.
What is even more surprising in the first place is that for example reordering the grid and running the simulation again produced an entirely different solution.
So the roundoff errors that are random to some extent are usually enough to produce different solutions for two otherwise identical DNS.

well, this is a typical effect, even simply porting the code on a different computer o using different compliers you see different numerical transient...
However, it is really important assessing that the statistics are all equal, whatever the numerical transient is

thanks for the reply. To be explicit in my understanding, is there any recommended criterion on how to set the width of the filter? Must the size be for e.g., be in the inertial subrange?

for implicit-based filtering, the size is determined by the grid and numerical method, you can only set exactly the grid size, not the filter width...

were you solving for a steady solution?

Im rather confused. if i understood correctly, the production of different results has nothing to do with the equations to be solved (e.g. RANS, LES), but rather how the grid is structured and the precision handling of the computers on which the simulation is run.

in LES you compute statistics after a long-time integration is solved and several flow fields are sampled...
Difference in the statistics can be due to the grid, to the numerical model, to the SGS model, etc.

to compute transient (unsteady) solutions, how should one choose between URANS or LES? kindly explain in simple terms.

I have the same question. For one specific problem, I can use the same grid sizes for URANS and LES. Which one to choose, URANS or LES?

URANS and LES provides solutions that have different meaning! the URANS is statistically averaged, the LES is filtered in space but not in time (if you do not use specific time-filtering apporach).

Using URANS o LES depends on the problem and on what you want to compute...but LES requires more refined grids than URANS

Thanks for your reply.

From the definition, they indeed have the different meaning. But the final form of the averaged equations (URANS) and filtered equation (LES) are nearly identical, except that the form of the sub-grid parameterization. To me it just like one method with two different parameterization methods.

I've read this text many times, but the answer I want to know is how to decide to choose RANS or LES for one specific problem, but not "it depends". I'm also curious to know, for one specific LES study, if I do a URANS study with the same grid size, what' wrong?

Thank you.

the mathematical equations looks similar but they differ for the expression of the unresolved terms. They take into account different unresolved tensors.

URANS can be used when an external unsteady force is present, for example the compression/expansion flow in cylinder due to the piston motion.

comparing LES and URANS on the same computational grid can give very different results for many reasons...
just as example, if you use a very very refined grid, LES solution will automatically tend to DNS, URANS does not.

Without regarding computation time, can i say, one should always trust LES to give a better result?

Can you help me understand how fine the grid should be for LES cases?

the answer is...it depends!
you can find very bad LES simulations... (too coarse grid, to dissipative model, etc)

the guideline for a LES grid is to solve wall boundary by using at least 3-4 cells within y+<=1.
Unbounded flow regions can be meshed by grid sizes dx+,dy+,dz+ = O(10)-O(20)

thanks. im new to CFD and appreciate the reply.

why would LES be too dissipative? i thought LES resolves all large scales, and only small scales are modelled - unless you mean that the SGS model are also too dissipative in the first place.

refining the mesh to achieve a 3-4 cells representation of y+ = 1 would require a very fine mesh.

if im interested only in wake structures, which are far away from walls, do I have to resolve my grid to such a resolution?

Some SGS models are purely dissipative in nature (e.g., the Smagorinsky) and could strongly affect the energy transfer of large resolved scales.

Furthermore, if you have vortical structures in wakes but that interacts with a wall (that is are generated by stress or are dissipated within the B.L.) you need to have a refined grid at wall. Some wall model exists in LES that allows to apply special B.C. at y+>1 but the full success is debatable.

i have been rereading this statement many times.

since the size of the filter is implicity implied by the grid size, does it always mean that the mesh density of a LES case is always higher than URANS?

yes, generally LES grid is more computational extensive even for the fact that URANS could be used (depending on the problem) in 2D.

in a few posts back, u mentioned that

can you explain how you judged that URANS can also be suitable (i assume LES is just as suitable in the quoted case ; please correct me if wrong).

in my current problem, im only interested in the wake (and my domain is set so large that it will not interact with the walls). For LES, must I still be bothered with refining the grid then? Will URANS suffice?

your question is not so well-posed ...
let me explain better, you have a flow problem to solve and you have to choose the suitable tool.
Now you said to be interested in wake..that does not say nothing useful to me...what parameter you need from your solution? It is the type of information you need in the wake that can drive to the correct tool.
Therefore, if the wake is generated my a mixing of two or more corrent, you have a sort of evolving mixing layer that requires fine grid and correct time accuracy. You need to analyze special frequency analysis? that drives to LES as the time scales are solved directly.

Consider my suggestion, ask yourself what you really need in your problem and step-by-step decide the correct tool.

Some reading of a book such http://www.cambridge.org/us/academic...urbulent-flows

cna be useful