Turbulence Modeling - Science topic

We will get the value of turbulent kinetic energy (k) from fluent or cfd post. this value can be substituted into 𝑘=0.5(𝑢′2+𝑣′2+𝑤′2).

with isotropic turbulence condition 𝑢′= 𝑣′ = 𝑤′

hence the initial equation turns out to be 𝑢′ = sqrt (2k/3)

This way, you can calculate the fluctuations. If you want to know the fluctuations 𝑣′ and 𝑤′, one must use rsm turbulence model.

For better understanding, check the "Turbulence Modelling for CFD" by Wilcox.

Overset meshes for incompressible flows: On preserving accuracy of underlying discretizations

AsheshSharmaa,∗, ShreyasAnanthana, JayanarayananSitaramanb, StephenThomasa, Michael A.Spraguea

aNational Renewable Energy Laboratory, Golden, CO, USA

bParallel Geometric Algorithms, LLC, Sunnyvale, CA, USA

a r t i c l e i n f o

a b s t r a c t

Article history:

Received 27 March 2020

Received in revised form 19 September 2020

Accepted 30 October 2020

Available online xxxx

Keywords:

Computational fluid dynamics

Unstructured meshes

Chimera grid

Overset

Additive Schwarz

Wind energy

This study on overset meshes for incompressible-flow simulations is motivated by accurate prediction of wind farm aerodynamics involving large motions and deformations of components with complex geometry. Using first-order hyperbolic and elliptic equation proxies for the incompressible Navier-Stokes (NS) equations, we investigate the influence of information exchange between overset meshes on numerical performance where the underlying discretization is second-order accurate. The first aspect of information exchange surrounds interpolation of solution where we examine Lagrange and point-cloud-based interpolation for creating constraint equations between overset meshes. To maintain overall second-order accuracy, higher-order interpolation is required for elliptic problems, but linear interpolation is sufficient for hyperbolic problems in first-order form. Higher-order point-cloud-based interpolation provides a pathway to maintaining accuracy in unstructured meshes, but at higher complexity. The second aspect of information exchange focuses on comparing the approaches of overset single system (OSS) and overset Additive Schwarz (OAS) for coupling the linear systems of the overlapping meshes. While the former involves a single linear system, in the latter the discrete linear systems are solved separately, and solving the global system is accomplished through outer iterations and sequential information exchange in a Jacobi fashion. For the test cases studied, accuracy for hyperbolic problems is maintained by performing two outer iterations, whereas many outer iterations are required for elliptic systems. The order-of-accuracy studies explored here are critical for verifying the overset-mesh coupling algorithms used in engineering simulations. Accuracy of these simulations themselves is, however, quantified using engineering quantities of interest such as drag, power, etc. Consequently, we conclude with numerical experiments using NS equations for incompressible flows where we show that linear interpolation and few outer iterations are sufficient for achieving asymptotic convergence of engineering quantities of interest.

©2020 Published by Elsevier Inc.

First question: where could have ChatGPT taken the relative information for training? Honestly, if you're going to use that, I can't see how you could not want to know about that.

Second question: why ChatGPT? This is not some complex information we can't extract from complicated data. It's just exactly there for you to learn or to search trough. Also, it could be better done by making a new net from scratch just about that, than using a LLM that really has nothing to do with this.

My general comment is that engineers and scientists should not look for answers in a tool that is not even tought to be what they want. ChatGPT just spits out words following a prompt and a statistical model. That is not different from a bad student that just goes by memory without understanding. ChatGPT is actually worst, as it never assumes it could not know something. You wanna ask that about turbulence models? Oh my...

I don’t use RANS formulation, I can just address some general questions:

1) use at least 3-4 nodes within y+<1

2) Are you comparing your solution to experimental measurements? Are you sure to be congruent with the experimental inflow conditions?

3) have you assessed you are able to reproduce the results for the classic test-case of the backward facing step at high Re number?

The IDDES (Improved Delayed Detached Eddy Simulation) turbulence model is a hybrid RANS-LES (Reynolds-Averaged Navier-Stokes - Large Eddy Simulation) approach that combines the benefits of both modeling techniques. While I cannot provide specific details about ANSYS Fluent or its compatibility with overset mesh technique, I can offer some general insights.

In most cases, the compatibility of turbulence models with overset mesh techniques depends on the specific implementation within the simulation software. Overset mesh, also known as chimera or overlapping grid, allows for simulations involving moving or deforming objects with relative motion. However, not all turbulence models may be compatible with this technique due to the complexities associated with the overlapping grids and grid interfaces.

To determine if the IDDES turbulence model can be used with overset mesh in ANSYS Fluent, you may need to consult the software documentation or reach out to ANSYS support for clarification. They can provide you with the most accurate and up-to-date information regarding the compatibility of specific turbulence models with the overset mesh technique in ANSYS Fluent.

Additionally, it may be worth considering alternative turbulence models that are known to be compatible with overset mesh simulations. Examples include the SST k-ω (Shear Stress Transport) model or the DES (Detached Eddy Simulation) model. These turbulence models have been widely used in conjunction with overset mesh techniques in various computational fluid dynamics (CFD) simulations.

Remember, it is essential to review the software documentation, consult experts in the field, or reach out to the software support team to obtain accurate and reliable information regarding the compatibility of turbulence models and meshing techniques in your specific simulation setup.

I am interested to learn if it is possible "Ram Chandra Pageni" since I am doing research in Space Science.

In computational fluid dynamics (CFD), the y+ value is used to determine the appropriate treatment of the near-wall region in turbulence modeling. It represents the non-dimensional distance of the first grid cell from the wall, normalized by the viscous length scale.

Different turbulence models have different requirements and guidelines for the appropriate range of y+ values. Here's a general overview of how y+ values are used with different types of turbulence models:

It's important to note that these are general guidelines, and the appropriate y+ range can vary based on the specific turbulence model and the flow conditions. Additionally, advancements in meshing techniques and solver capabilities have reduced the sensitivity to y+ values in some cases. It's always recommended to consult the documentation or literature specific to the turbulence model and software you are using for more accurate guidance on y+ values.

Ilya Lichadeev What I mean that it depends how you verify your numerical model (point-wise, using integral values, etc.), and which experimental data is used. For some setups it is not possible to get a perfect quantitative match, but it is enough if your model follows the trends observed in the experiment.

As you can see, the RANS solution produces an averaged field over the sphere also for laminar vortex shedding.

However, if you have a fully laminar flow, simply solve for it. If You think to have transitional conditions, Depending on you computational power, use LES or DNS.

Hi Naveen,

I have the same question; has your issue been resolved ?

Alin

Hong Xiang Yu

In general, for dynamic mesh simulations, models that involve additional terms in the turbulence kinetic energy equation, such as WALE (Wall-adapting Local Eddy-Viscosity) or dynamic Smagorinsky, may be more suitable. These models account for the effects of mesh deformation on the turbulence field and can improve the accuracy of the simulations.

WALE model includes additional terms in the eddy-viscosity calculation that account for the effects of rotation rate, strain rate, and dissipation rate. This model can be more accurate for complex flows with high strain rates and rotation rates, such as flows with vortices or swirling motions.

On the other hand, the Smagorinsky model is a popular choice for LES simulations in OpenFOAM and is widely used for a range of flows. This model uses a constant eddy viscosity coefficient that is proportional to the grid size, which means that it can be computationally efficient for large-scale simulations. However, it may not be as accurate for complex flows or when mesh deformation effects are significant.

When choosing an LES model for dynamic mesh simulations, it is important to consider the specific characteristics of the flow and the desired level of accuracy. It is also recommended to perform sensitivity studies to investigate the effect of different LES models on the results and to validate the simulations against experimental data or other established methods.

Possibly you should have a look at

Baumert, H. Z., Universal equations and constants of turbulent motion,

Physica Scripta, T155, 014,001 (12pp), doi:10.1088/0031-8949/2013/T155/014001, 2013.

Also have a look at the attachments.

Feel free to ask specific questions!

Best,

Helmut

Forget this k-eps model. While k is a so-called conserved variable, eps ist not. Eps is a colored mix of variables. I recommend for turbulence the k-Omega-model. It allows in my interpretation (2009) e.g. the derivation of the von-Karman constant as 1/sqrt(2*pi)~0.3989... as well as also other constants of this model. Cheers, Helmut

Here is a sample UDF code for the k-e-v2-f turbulence model in ANSYS Fluent:

This code defines a UDF for the k-e-v2-f turbulence model in ANSYS Fluent. The UDF computes the turbulent kinetic energy (k), the turbulent dissipation rate (eps), and the second moment of the velocity fluctuations (v2). It also computes the turbulent viscosity (mu_t) and the constants C1, C2, and C3, which are used to compute the wall function values of epsilon and f. The UDF then sets the values of k, epsilon, and f in the turbulent and laminar regions of the flow, and updates these values in the solver.

#include "udf.h"

DEFINE_K_EPSILON(k_e_v2_f, c, t, dS, eqn) { real k, eps, f; real v2, S; real mu; real rho; real Cmu, C1, C2, C3; real sigma_k, sigma_e, sigma_f; real f_wall, eps_wall;

k = C_K(c, t); eps = C_EPSILON(c, t);

v2 = 2.0k/3.0; S = sqrt(2.0k)/C_MU(c, t);

mu = C_MU(c, t); rho = C_R(c, t); Cmu = 0.09; mu_t = rhoCmukeps/(muS);

C1 = 1.44; C2 = 1.92; C3 = 1.0;

eps_wall = C2sqrt(k)pow(C_DIST(c, t), 2.0/3.0); f_wall = C3(1.0 - exp(-C1pow(C_DIST(c, t), 1.5)));

sigma_k = 1.0; sigma_e = 1.3; sigma_f = 1.0;

if (C_DIST(c, t) < 1.0e-10) { eps = eps_wall; f = f_wall; }

else { k = sigma_kmu_tfv2/(epsC_MU(c, t)); eps = sigma_emu_tfv2/(kC_MU(c, t)); }

f = sigma_fmu_tfv2/(kC_MU(c, t));

if (S < 1.0e-10) { k = 0.0; eps = 0.0; }

dS[eqn] = 1.0; C_K(c, t) = k; C_EPSILON(c, t) = eps; }

Hi,

In my reasearch project, I have completed recently; a research for a typical turbocharger compressor optimisation. It has been studied air turbulent flow simulated the K-Omega SST turbulence model. My numerical simulations has been validated and I wrote some papers. Please have a look into my papers, hopefully it can help.

Regards,

It is possible to calculate Taylor microscale and macroscale. Taylor microscale can be calculated using the fitting of the parabola at the origin. One may find details within this paper:

Chuychai, P., Weygand, J. M., Matthaeus, W. H., Dasso, S., Smith, C. W., & Kivelson, M. G. (2014). Technique for measuring and correcting the Taylor microscale. Journal of Geophysical Research: Space Physics, 119(6), 4256-4265. Available on:

I recommend you the previous links because you can easily copy and paste values from there, but you find theory, airfoil geometry and experimental data here: https://aeroknowledge77.files.wordpress.com/2011/09/58986488-theory-of-wing-sections-including-a-summary-of-airfoil-data.pdf

Also I recommend to search for experimental data here in RG.

k- epsilon model poorly resolves the viscous layer unline K-Omega model. Furthermore, k-ω model is good in resolving internal flows, separated flows and jets and flows with high-pressure gradient and also internal flows through curved geometries.

Dear Armin Hajighasem Kashani

trying to achieve a match with experimental measurements is not a simple task. In your formulation you should match the statistics of the experimental inflow and that is not straightforward also for a laminar flow problem as detailed in Sec.4 here

A further issue could be also due to the fact your grid resolution is not sufficient to resolve the BL. You should be able to set at least 3-4 nodes at y+<1 along the surface of the body.

Again, I strongly suggest to try your setting for the well studied problem of the backward facing step turbulent flow. You can find a lot of results in literature.

If the size t is less than 8-12 distances to the bottom surface, the drag force will be determined, among other things, by the added masses of the liquid reflected from the bottom surface. If this is not important, then in the model you need to specify a distance of more than 12.

The number of wall layers is enough 5.

From above on a free surface, the wind has a speed and direction?

Hi

I tried to run an ANSYS FLUENT simulation with SST Model and it was hard to converge. The laminar model should converge, I'd suggest using a better quality mesh.

In some cases the SST will resolve to low/zero turbulence, but that is not always the case. The SST model will have turbulent fluid coming in the inlet, as set by your boundary condition. This would take time/distance to convert to laminar using the SST model. Check your Viscosity ratio in your results, see how turbulent the SST model is showing your flow to be. Laminar flows can have problems converging where they are actually turbulent flows, or where transient laminar flow structures exist. (This is assuming you have a good quality mesh and the simulation is correctly set up.) If you say the flow should be laminar then it is still quite possible that a laminar steady state simulation will not converge because of transient flow structures. This is very common in heat transfer simulations with natural convection - the natural convection tends to have transient laminar structures. If this is the case the only way to proceed is to do a transient laminar simulation. If you solve a laminar simulation with a turbulence model you are adding extra dissipation to the model. This dissipation is not real, it is a product of the turbulence model you are using, but it can have the effect of damping out these transient flow structures and apparently converging. The SST turbulence model is better than most turbulence models as when the turbulent kinetic energy goes to zero (it is exactly zero in a laminar flow, by definition) the turbulent viscosity also goes to zero, so SST does not add much dissipation to the model and you might get away with it. But k-epsilon based models are well known to have far too much dissipation in the low Reynolds number regimes because as k goes to zero the turbulent viscosity goes to a finite value, and this is false additional dissipation. This is why the k-e model is a bad choice for low Reynolds number flows, and you either need to modify it or use a k-omega based model like SST which does give zero turbulent viscosity at zero turbulent kinetic energy. However, if your flow is steady state laminar and you use the SST turbulence model the turbulence model is likely to give effectively zero turbulent viscosity, meaning that your answer probably will be reasonably accurate (only with a small amount of extra dissipation). If your flow is transient laminar and you use the SST turbulence model there is a good chance the turbulence model will generate too much dissipation and damp out the transient flow, which is wrong. You are likely to get a big error in this case. In this case using the SST turbulence model is wrong. A transient laminar model is correct. Note you will need to do time step, mesh and convergence criteria sensitivity studies to work out what you need for time step and mesh size, and convergence criteria.

Hopefully that explains things a bit.

I would not use k-epsilon for this kind of problem. It is most valid for free stream turbulence like atmospheric flows. Near the walls k-epsilon behaves quite poor according to my experience and I believe that is where your heat transfer happens.

But you can try it yourself of course.

In such cases, modifying the BC or mesh configuration may be beneficial.

Hi Somenath, It should be possible with ANSYS as I have made it possible in STARCCM+ for my flat plate simulations. I will let you know the procedure in STARCCM+ and probably the same steps can be followed in ANSYS too.

1. Make a line probe where vertical measurements can be taken at the location or position where you want to have U+ vs Y+ plot.

2. Go for an XY plot where Y-axis is U+ and X-axis is a log scale of Y+.

3. Now the input part to this plot should be the earlier line probe.

4. Most of the software has Y+ defined so you just need to recall the expression for Y+ from the software database and assign it on the x-axis.

5. U+ may not be defined in most software. Hence create a custom field scalar function defining U+. (You will get a standard expression for U+ from any Fluid Mechanics Text Book, I follow Frank M White and it has expressions for U+ too).

6. Finally assign this newly defined U+ to the Y-axis.

Hope you too will get the same on ANSYS.

In general, for internal flows with curvature and rotations, k-Omega SST model performs the best. While k-epsilon models are used widely, they are not able to capture the near-wall effects (performs weakly under adverse pressure gradients and along curvatures). While k-Omega-SST model uses a blending function and allows us to go to y+ ~ 1, without having the adverse problems like in k-epsilon. In essence, it functions like the k-Omega model near the walls, and like a k-epsilon model in the far-fields.

Hello Mr Jayathilake,

the kOmegaSSTt is suitable for this problem in my opinion, especially when computational limits are low. With adequate refinement in the critical regions you might achieve good results. I don't know which code you are using for your simulations but, if available, you can also try the kOmegaSSTSAS approach. This "Scale Adaptive Simulation" approach is able to resolve turbulent length scales more precisely. It falls back to kOmegaSST if the mesh is coarse. But if you apply some further refinement you should get a more detailed results. Timesteps should be limited to CFL<=1. Compared to LES or DES mesh and solution order requirements are still lower I guess.

Computational effort for this model is not really higher than for the kOmegaSST.

You may have a look e.g. at these publications of the model developer Mr. F. Menter:

and many more.

I hope this helps.

Best Regards

David

it may be caused by grid.

First - changing two things in a simulation at the same time is obvious not a good approach (mesh AND turbulence model).

Further I need to say, that first you would need to find out, where your simulation comes to a mesh-independente solution (this question can be anbswered with no regard to which turbulence model you are using, if properly implemented in the CFD solver), and only on a mesh level, where the CFD solution is independent from the mesh resolution you can start to make judgements about model errors or about which turbulence model is more suitable for your particular application.

In contrary, if your solution is not yet mesh independent, i.e. the mesh is still too coarse, you are looking on a not known mix of discretization errors and model errors. This can mislead you to a misjudgement about a particular physical model and on the next following refinded mesh the situation (with the two models in question) might be reversed in comparison to what you have observed on the coarser mesh.

This CFD methodology has been outlined almost 22 years ago in the so-called "CFD best practice Guidelines for Industrial CFD".

The report can be ordered on this page from ERCOFTAC.

The methodology says, that the following hierachy of CFD error quantification and elimination should be observed:

1) round-off error

2) iteration error (cut-off error with respect of stopping a solution process at a certain iteration number or by matchig a certain quantitative convergence criterion)

3) spatial and time discretization error

...and only afterwards you can investigate model errors of physical models.

Any attempt to investigate more than just one error source at the same time or to change the order of investigation of the errors from this error hierarchy potential will lead to erroneous CFD result.

Best regards,

Dr. Th. Frank.

WMLES or SBES model with a well thought (and validated) mesh resolution.

Regards,

Dr. Th. Frank.

PS : Your question did not involve any limitations on the computational time of the proposed turbulence modeling approach. Therefore a scale-resolving simulation would clearly deliver the best possible results (I'm not proposing a diret numerical simulation, since you asked for turbulence modeling approaches; DNS is resolving turbulence, but not modeling it).

Pradyumn Chiwhane I am posting here a previous answer I provided on submerged oscillating bodies in quiescent fluid. The total force exerted by the fluid on the cylinder, you should consider besides the drag the added mass force. Academic references are provided within the following research projects:

Pradyumn Chiwhane I am not used to performing calculations with CFX. Researchers in my group do.

If this is the total force exerted by the fluid on the cylinder, you should in my opinion deduce the added mass force to obtain the net Drag. You may consult academic references within the following research projects:

Academic resources on fluid Mechanics are provided on the project references:

SINGLE PHASE AND MULTIPHASE TURBULENT FLOWS (SMTF) IN NATURE AND ENGINEERING APPLICATIONS | Jamel Chahed | 3 publications | Research Project (researchgate.net)

The SST k-ω model is a hybrid of the best of k-ω and k-ε. This model uses the k-ω model near walls, and transitions to k-ε model in the open flow field. It’s popular in aerospace and turbo-machinery applications.

So SST is the most effective model for young computational fluid dynamists.

Flows with greater anisotropy in the turbulence stress tensor render standard one equation and two equation RANS models not great as they make assumptions about the turbulent shear stresses which turn out to be untrue for specific cases. This anisotropy is commonly present in flows with large separations etc. Reynolds stress models remedy this by solving transport equations for each component of the stress tensor but these too have limitations and haven't proven to be of much advantage. If high accuracy is desired, LES or DNS are the only way. But for most engineering flows of interest, one equation and two-equation eddy viscosity models provide sufficient engineering accuracy with results in a reasonable time.

Resources, and academic tools on turbulent single-phase and multiphase flows are availble on the project:

Mohamed Bounouib please follow the links attached herewith and follow the steps for the flow simulation in an axial pump. That will be easier for you

Regards

Ahmed Gharib Yosry Thanks for your assistance. i tried to reverse the boundary conditions, replace the inlet and outlet with each other. but the torque doesn't goes down the optimum value.

In the COMSOL documentation the no-slip BC is indicated to be (u,v)=(0,0).

What you described is slip condition. You can consult the COMSOL team for more clarification. You can also run a test simulation to see how the code performs.

Dear Fellows,

We are all choosing a Turbulence Model for our flows. We are already creating decision chart in our heads. So, why not put these decision steps together and create a real chart? Please do not think so complicated. At the end of the chart there won't be only one Turbulence Model for relevant case, there will be list (at end of different decision steps same model could be listed too). After that you can do some literature search to select the best model in the list.

Please consider that.

Best regards,

Güven

Güven Nergiz please follow the following links below :

i hope it will help you.

The Reynolds Stress Model is the most complete turbulence model with regards to representing turbulent flow.

I would add that the meaning of y+ is very very simple. It is nothing but that the local Reynolds number measured along a normal-to-wall direction. That is, y+=0 is the wall position and then the value increases according to y+=(u_tau/vi)*y.

The Reynolds number is not high, and accuracy can mean saving of lives. Do not use a turbulence model, perform a direct simulation (DNS) resolving all temporal and spatial scales with an appropriate function for the viscosity (shear thinning character). This can be done with any numerical code solving the Navier-Stokes equations, and the numerical costs will be affordable.

I think it may be due to BCs. Can you please your problem in detail, so that I may help you in diverg. Issue

Dear Vinay,

I would recommend you to follow the video lecturer on Computational Fluid Dynamics by Prof. Suman Chakraborty according to me it is one of the best.

You can also follow Prof. Fillipo Maria Denaro's technical report/ppt on CFD, available in the ResearchGate.. He is one the famous researcher in computational physics and CFD.

Dear Prof. Th. Frank

Thank You very much for your explanation and suggestions on my error.

Hi Nehal,

If you have refined the mesh and used the appropriate turbulence model, then I suggest you to please check the convergence of the result.

Hi,

this is not an easy to setup simulation, also it may look like this on the first glance.

Why?

1) Vortices leading to air entrainment are rather small flow structures of thin diameter and having substantial velocity gradients over the diameter of the vortex. This velocity gradients need to be fairly good resolved in order to resolve the low pressure occuring in the center of the vortex leading to the air entrainement. For that a very fine mesh resolution is required.

2) Usually such vortices - if they are not artificially and by geometric design measures stabilized - are not stationary in space. So the vortex is moving around / precessing and that makes it even harder to establish a well resolved numerical mesh with fine mesh resolution at the vortex center. It might be suitable to use adaptive mesh refinement, but without a coarsening algorithm there is the danger, that the geometrical space more and more fills with refined mesh cells, making the simulation rather very expensive.

3) Standard 2-equation turbulence models are by default not capable of capturing the strong velocity gradients in flows with strong streamline curvature / rotation. A SST k-omega-based turbulence model with curvature correction is at least recommended.

4) It has to be made sure, that the turbulence conditions at the free surface between liquid and air are capturing the physical valid conditions. Usually a fine mesh resolution at the free surface is required for that in addition with turbulence model damping terms at the free surface, i.e. the turbulent kinetic energy at the free surface needs to be dampended to zero or small values.

Regards,

Dr. Th. Frank.

I try to address better the question. If the flow problem admits a statistically steady state you start from an initial condition and run until you flow variables are statistically steady, that is you see them oscillating in time around a constant value. From that moment the solution is physically correlated.

Conversely, if you have a problem that has an unsteady behavior also in the mean values, you need to simulate at least the largest time period of your problem.

This approach is typical for DNS/LES formulations, sice you are using a URANS approach the real physical meaning of your unsteady simulation can be debated. If your flow problem is statistically steady, your URANS shouldn't be somehow different from a RANS solution.

The key is that you cannot fix arbitrarily a number of time steps without knowing the nature of your flow problem.

I suggest to monitor in time the mean values

it may help you:

Best

The problem I faced while using SST I need to put the value of turbulence intensity at the entrance, and if I specify it to zero then my solution does not converge.

If you want to use the RANS formulation you should assume your flow field is totally developed. That means you can set the inflow profile according to a statistically mean velocity in a channel

Can provide more information

simply you can save your case and data file at the current time step , and then reopen it and procced the calculation again . the error at each time step will disappear.

Ok, I can't stop making some practical comments.

To "Refinement in this scenario should give a converged result for the chosen filter width etc": Theoretically maybe, but not in numerical practice. If you do not fully resolve, without modelling, you have to be lucky for a simple physical behavior of the filtered/averaged part. Sometimes this holds, and/or the error is not significant, and modelling works well for some aspects of the flow.

"... admit different types of modelling strategies?": Formally yes in my view, but eventually it is always about how to view/sell the result. There are many LES modellings (SGS, or with explicit or implicit filtering, or with designing the truncation error to 'exactly' model the missing parts, or ...), and still, no one is definitely superior in numerical practice other than for the cases shown by the designers for which they know what to do. Also, the numerical implementation may play a role especially for compressible flow.

So you see, I am a DNS guy, and methods with less resolution need

be stable and can be more or less acceptable; the reasonings for the behavior varies with the method.

The late J. Ferziger (Stanford) once said: "FDs of second order with Smagorinsky is the best", at a time where higher order was used already, so he was convinced of the method he used as a well reputated specialist. The same was said by a German group one decade later, comparing 2nd and higher-order FDs in a LES journal paper. At the same it turned out however that switching-off the model yielded virtually the same results in the latter case. Oops, but this is not untypical.

Here, I found the answer. I follow what Asst.Prof. Joe suggested, in which the additional terms are added by weak contribution.

Dear Hossein Rabieh

You have to define for example your desired Kolmogorov scale through UDF.

Cheers,

You should decrease the residuals. How many residuals did you have when you used the transient calculation. If you can not decrease the residuals you should increase the number of elements. If you have a separation zone then the residuals periodicity will remain.

Hi Amit Bharadwa,

My answer is a bit late. But might be of help to others.

I am not a user of Ansys. I cannot tell you whether you need a used defined routine to obtain the wave kinematics at inflow.

However, I have experience with generating waves in CFD and recently published a paper in which I also compare a generated CFD wave with the Stokes 5th order theory and I also computed wave loads on a vertical surface piercing circular cylinder [1], but both with incompressible fluids. The results of the latter are also compared against experiments.

Your inflow BC should include wave kinematics (e.g. orbital velocity vectors for a regular wave) and the volume fraction according to the wave you are using (Stokes 3rd order). Some codes allow also coupling with potential flow codes that include several irregular wave spectra. The wave theory should also be chosen according to their applicability ranges. See e.g. Lé Méhauté.

Depending on the domain, you might see reflections at outflow, sides, objects, etc. Therefore, you need to tune your damping algorithm to your case. Further details see [5].

As Mohammad Mehdi Baseri pointed out, the grid/mesh is important. Usually, people try to relate the cell size in vertical and propagation direction to the wave height and length, respectively. However, this also depends on the steepness of the wave. A steeper wave might need different resolution. And it also depends on the structure you investigate, i.e. the diameter of the cylinder or any other difficult areas.

Furthermore, the time discretisation is also very important. Time discretisation can also be related to the wave period. Time discretisation is important because it influences the iterative convergence. Some interface capturing schemes like HRIC depend on the CFL (Courant-Friedrichs-Lewy) number[3]. They switch to lower order methods for larger CFL numbers for robustness but this depends also on the CFD code you use. Thus, you will need to investigate different time step sizes. If you see irregular waves for regular inputs, the iterative convergence might be the problem. When you do the grid and time step sensitivity study, try to follow ITTC guidelines for refinement[2]. If you want to read more about this, you are welcome to read my paper: Towards credible CFD simulations for floating offshore wind turbines [1]. I believe test case 2 is similiar to yours. I will add a couple of further reading material below.

The iterative convergence is also important. To achieve convergence is usually difficult for simulations with VOF, see [3]. Residuals in the order of Linf<1e-3 might be okay for a first investigation. Then, you can check the influence for 1e-4, 1e-5 etc. But this comes once your grid and time step are acceptable and the results in the same order as the experiment. The iterative convergence is also influenced by the relaxation parameters, the number of iterations per time step, the numerical schemes, the grid quality, etc.

Good luck!

Cheers, SImon

[1]

[2] https://www.ittc.info/media/8153/75-03-01-01.pdf

[3]

[4] https://www.marin.nl/publications/towards-guidelines-for-consistent-wave-propagation-in-cfd-simulations

[5] https://doi.org/10.1080/

09377255.2015.1119921

Good topic

Dear Md Nuruzzaman,

Use ANSYS FLUENT 12.0 Tutorial Guide to develop temperature contour data at specific time set up. Use the solution animation feature to save contour plots of temperature every five time steps. .ANSYS FLUENT to update the animation sequence at every time step and contour plot in Fluent at specified intervals of a calculation.

Step Condition

1. Geometry and boundary conditions.

2. Overall mesh generation

3. Independence studies. (a) Grid Independence; (b) Time Independence

4. Temperature contour for different time step sizes (top = 0.5 s, middle = 0.01 s, bottom = 0.005 s)

5. Animation

6. Post solution and save

Ashish

???

Just go to flow domain --> turbulence model and select the Shear-Stress Transport (SST) model. So what is here the issue?

Regards,

Dr. Th. Frank.

Hi,

you could use a combination of refinement on the wall surfaces and then apply inflation layers of your choice ( you may input the first layer height), thus you could generate a mesh of good quality in terms of skewness as well as aspect ratio.

hope it helps.

Regards,

Rajesh.

You can begin by trying different k-epsilon models (standard, RNG, realisable ) and compare it with experimental data.

The Y+ value depends on the grid points that are near the wall. The more grid points near the wall the less Y+. FLUENT has the Y+ plot option.