I have the same geometry and essentially the same mesh for both cases. In the compressible case, I use a total-pressure inlet and static-pressure outlet, and the solution converges quite well. The residuals decrease and the important flow parameters, such as mass flow and pressure, become stable. Interestingly, there is still a very high percentage of reverse flow at the outlet (around 97%), but the solution remains stable.

For subsonic compressible case,

Now,

In the incompressible case, I use a velocity inlet of 5 m/s and a pressure outlet. I ran many more iterations, but the residuals continue to oscillate, and quantities such as outlet mass flow and downstream pressure are also not very steady. The outlet reverse-flow percentage is actually lower than in the compressible case (around 81%), so I don't understand why this case behaves worse. The contour also changes with iteration, not steady like above.

See the contour for 300 and 1900 iterations,

In nutshell my questions are:

1. What is making not to converge smoothly for incompressible case compared to compressible one. What is difference between jets in compressible and incompressible flow?
2. If you say this is due to physical unsteadiness problem, then how for compressible case, solution got converged and giving fixed contour whereas incompressible it is not? Any other suggestions?

I would really appreciate any explanation of what could be causing such different behaviour between these two cases.

Apparently airfoil images are NSFW now

​

I'm a software engineer who recently got into computational fluid dynamics. Coming from that world I kept looking for something like LeetCode for it. Pick a problem, solve it, see where you went wrong, move up. Couldn't really find anything, so I ended up building one.

I also talked to a bunch of aero/mech students while making it and the same thing came up over and over: basically nobody does CFD outside of labs or coursework, because you need proper compute to run anything real. So it just stays theoretical.

Landing page is here if you want to poke at it: https://airfoillearner.com/demo There's an easy problem and one full module module one - I'd recommend just the easy one.

It's early and it's only airfoil stuff for now. If you're learning or teaching CFD I'd honestly love to know if the problems feel pitched right, and what you'd want next, or perpahs you might think that this does not fit the CFD world.

Hello!

I am attempting to diagnose/understand an Exit Code 137 that appeared while attempting to split-run two sims on PoD: 24/24 Cores. I investigated the server's CPU specs and found that, although there is hyperthreading with 2 threads per core, there are only 2 sockets, each with 14 cores. Additionally, there is 251 GiB of total RAM, and, interestingly, 3.9 GiB of swap is currently in use.

I am a novice, so of course I find it interesting that the server has processed 24/24 split sims before. How do threads influence an optimal setup? I apologize if this is easily accessible information, but is there any guide or information available on a proper setup?

My sim files are around 13-15GB, use k-sst, and contain roughly 50-60 million cells. I also attached pictures of the relevant specs and the OOM closure analysis, but I'm limited in my ability to understand it.

Any advice/help at all is much appreciated.

I completed the basics of fluid flow and am currently doing some simulations using Ansys Fluent and Python. Gonna start Combustion, jet flow, Volume of fluid and physics behind and inside the fluid flows. Idk how to study these and simulate these........any ideas or book recommendations please, it will be very helpful 🙏

Hi all,

In my latest video (https://www.youtube.com/watch?v=4PGiMpfaPdM), I work through why turbulence naturally splits into three distinct regions: energy-containing, inertial subrange, and dissipation range. From there it covers Kolmogorov's first two similarity hypotheses (local isotropy, and the universal equilibrium range), derives the Kolmogorov microscales via dimensional analysis, and shows that the Reynolds number at the smallest scale is exactly 1, a direct consequence of the dimensional argument.

Second half introduces two-point correlation functions (giving the "large scale" a rigorous, measurable definition via the integral length scale) and the second-order structure function, setting up the −5/3 spectrum and the exact 4/5 law for next video.

As always, feedback (positive and negative) and suggestions are welcomed.

Im trying to simulate on Ansys fluent the experiment shown in the first image, where a magnetic stirrer bar rotates at 160 rad/s creating a vortex in a cylindrical bathtub filled by seawater (light represents shear stress).

The problem is that i cant find a suitable viscous model that reflects accurately the shape of that vortex. Im using SST k-w and in the second image the shape is conical and not deep.

By adding the curvature correction term, the vortex goes too deep and reaches the stirrer bar (image 3)

By adding the turbolence dumping factor the vortex still is about to reach the bottom and it also spills from top (image 4)

Even by decreasing the correction terms, the same problems occur later on with the simulation. The water and air properties are set right (with constant surface tension), the rotation is accomplished thanks to a rotating reference frame (frame motion), the mesh is around 300k cells for about 50 mL of volume and increasing it doesnt change results, the continuity residuals always sit lower than the order of 10^-2. The last image shows the geometry used, where the green shape is a body of influence for the meshing. The stirring bar has 0.008m height and 0.03m lenght.

What could I be missing to obtain the correct curvature? Thanks in advance for your time.

First 1.4 Mach & 3000m
Second testing hydrogen rocket engine (I forgot to turn off the walls)

I'm working on lil project for school, and I want to see if theres any simulator I can use online that I can 3d model/import step files in and add components such as fans , and run simulations to see how the fan works with the model. Idk if there's something that's like this but if there is or something similar I would appreciate it.

This was done using an open-source LBM code.

The STL file for 3D model is obtained from grabcad and voxelized. It contains roughly 37 million cells. Ran on single A100 GPU, took 48 hours to simulate 1.5 seconds of real-life

It is not full engineering grade simulation, but it is very close.
For example, it is upscaled to reduce number of cells, tires don't rotate, and it used single precision to reduce memory overhead etc. But, the computed drag and lift coefficients agree with reference values.

The visualization is done using ParaView.

So much can be done with LBM, it is amazing.

https://reddit.com/link/1w313ez/video/gz2dyddphmmh1/player

I am simulating a piping network with two inlets and two outlets, flow is multiphase with a liquid and gas. Model is vof, both fluids are incompressible and no phase change, chemical reactions.

Inlet and outlet volume flowrate is balanced. I am interested in the retained volume in domain. The retained volume of a phase in the domain is not matching with the expression, retained volume = volume from previous time step*(inlet vfr- outlet vfr)*dt.

Hi all, I am an HVAC engineer looking to get into CFD. After some serious Google + ChatGPT research, ANSYS seems to be the best software(i might be wrong) .

Any tips on where to start? Happy to spend a little on a good course. Recommendations for courses, books, or YouTube channels would be greatly appreciated.

Trying to go from “What is CFD?” to “I know what what we arw talking about"🤔

I'm running an incompressible steady-state RANS case for a blended wing body (BWB) in OpenFOAM (simpleFoam/SIMPLEC).

Setup: ~0.15 Mach, ~6e6 Re

Mesh: ~30M cells, tetra + prism layers at walls, O-type symmetric far-field domain(radius ~ 30c), max skewness ~5, max nonOrthogonality >75°

BCs: freestream (velocity inlet / pressure outlet style far-field)

Turbulence: k-omega SST, wall-modeled (y+ > 30)

Schemes: second-order upwind for all equations

Algorithm: SIMPLEC

Solution strategy: start with low URFs, ramp up every 100 iterations; case runs "on its own" past ~250 iterations

Problem:

Based on past experience I expect convergence by 500–600 iterations. Here, residuals flatten out but very slowly, and by 1500+ iterations:

CD looks converged/flatlined

CL is still steadily rising, not flatlining

Has anyone run into similar slower convergence rate or is it normal for high cell count meshes? Looking for likely causes and what to check/try next.

Hey, everyone!

I’m new to the world of Computational Fluid Dynamics (CFD) and just jumped right into FluidX3D. I was completely blown away by the performance and speed of the GPU simulations that people have been showing off.

The problem is that I’m a beginner, and the reality check was harsh: I haven’t found any beginner-friendly tutorials that I can actually understand from scratch, let alone get my hands dirty and run a little something on my own. Almost everything out there assumes you already know a lot about programming or the physics behind LBMs (Lattice Boltzmann Methods).

If anyone here has already gone through this learning curve or uses the tool on a daily basis, I’d like to ask for some help with a few basic questions:

Where should a complete beginner start? Is there any material, guide, or simplified example in the repository that serves as a good starting point?

How do you usually visualize the results? Is there a user-friendly graphical interface (GUI), or is the entire workflow done via code and exporting to external visualizers like ParaView?

Where do people usually get stuck? Are there any pitfalls when compiling or setting up the environment that tend to frustrate beginners?

Any tips, step-by-step guides, or constructive feedback are very welcome. Thanks so much for the help!

Guys one small doubt

1. What can be the reasons if solution gets converged faster for steady RANS simulation, like converging way earlier in 500 or 600th iteration itself? Note mesh quality is good and hybrid initialisation is used? Is it a good or bad sign? I used residuals for continuity as 10e-4 and velocities 10e-6, energy 10e-5? Tell what can be the reasons in general, when it is acceptable and when it is not acceptable? My cell count is around 5.4 million.
2. If you say dont judge residuals based on convergence, but when you see any parameter in flow field vs iterations, if it is not steady with iterations, still changing but you got solution converged purely based on residuals, then what I can do now?? What can be the mistake seriously??
3. Can residuals become very low while the actual solution is still inaccurate??

If there is something wrong with my setup, how should I correct it so that the solution reaches convergence naturally, rather than trying to force it to converge at a particular iteration?”

You can give general opinions also since I want to explore what can be the reasons behind this?

I will post photos below,

Those gaps are walls of distortion screen, void is kept for pipe wall thickness and outer fluid is open ambient still fluid,

I have done fluent meshing, mesh quality is good. In section view,

Note mesh refinement is done only in small region right from pipe end. See axial velocity countour below

Merging of those jets are not smooth, there is sudden step change which is not ideal. I thought mesh resolution is not good, so I decided to refine it more. Before that, I ran upto 20000 iteratinos still it is not converging. See residual and flow parameters below,

So I refined mesh like below,

For above case, cell count is same as previous case and quality is also same. Now see residuals and flow parameter variation with iterations,

My questions are as follows:

1. In my first case, the mesh quality was good, but the downstream region was not as strongly refined as in my second case. Despite this, the solution struggled to converge even after 20,000+ iterations. What could cause such slow convergence?
2. In the second case, I refined the downstream region along the jet direction. I used first-order discretization for the first 200 iterations and then switched to second-order. After this, the solution converged much faster and the monitored flow parameters also became steady.

Both cases used hybrid initialization. The flow is compressible with an ideal-gas model and Sutherland viscosity, and I used the default URF values.

Is this behavior normal? Can stronger downstream mesh refinement actually make a complex turbulent compressible-flow case converge faster, or should I be concerned that the rapid convergence is caused by some numerical issue rather than a physically better-resolved solution?

Hello Everyone!

I'm currently doing small walkthroughs by watching videos on the ANSYS website, but they don't really explain what the Bodies Of Interests(BOI) really represent. they just show you how to make them. I'm aware of the enclosure (wind tunnel) being there. Could someone explain more in depth what the bodies of interests really mean?

Thanks!

My Background & Constraints:

Education: Completed a Diploma in Mechanical Engineering in 2021. Enrolled in B.E. (Mechanical) in 2021, but due to academic/personal delays, my graduation date pushed from 2024 to 2028.

Current Job: Working full-time in a non-tech BPO/operations job in India to stay financially afloat.

Current State: Analytical skills are rusty after years in BPO operations, but I want to break out and build a real, high-leverage technical career in engineering simulation.

Available Time: ~15–18 hours/week (1.5 hours every morning before my shift + dedicated blocks on weekends).

The Target Path:

I want to self-study Computational Fluid Dynamics (CFD), Conjugate Heat Transfer (CHT), and simulation automation (using Python, OpenFOAM, Gmsh, and Linux) to land an entry-level Junior CFD / Thermal Analyst role at an engineering services firm or automotive/electronics supplier, and eventually transition into automation/computational design.

Where I Need Your Reality Check:

The Resume/HR Filter in India: Given my 4-year degree gap (B.E. 2021–2028) and BPO work history, will Indian engineering service companies, tier-1 suppliers, or GCCs (Global Capability Centers) automatically filter out my resume via automated screening, regardless of portfolio?

Portfolio vs. Degree: If I build a public GitHub portfolio containing verified, reproducible OpenFOAM validation cases (e.g., CHT on electronics cooling, mesh independence/GCI studies, and Python meshing scripts), is that actually enough to bypass HR and get a technical interview with an engineering manager?

Open-Source vs. Commercial Stack: How viable is entering the industry with strong OpenFOAM and Python skills compared to candidates who only know GUI commercial tools like ANSYS Fluent or STAR-CCM+?

Feasibility & Timeline: Is reaching employable competency in OpenFOAM, Linux, and fluid/thermal theory within 6–9 months on a 15–18 hr/week schedule realistic, or is the learning curve too steep alongside full-time non-tech employment?

Any unfiltered critiques, alternative pathways, or blind spots I’m missing would be deeply appreciated.

I need to solve a simple steady 1D compressible pipe flow using Eulers equation with friction source term. I also need to solve energy equation (total enthalpy form). Could you recommend a suitable numerical method I can use for this problem. Thanks!

Hi all,

I'm working on modelling cavitation using the Schnerr-Sauer method. I'm using RANS so I want to apply the Reboud correction. I know this is possible as Bensow et al have a paper which used this method is starccm https://research.chalmers.se/publication/550373/file/550373_Fulltext.pdf but I can't find the setting.

If someone could let me know where to find it that would be great, thanks

According to the formula:

Fd = 1/2 * pv² * Cd * A

please help me.

For context, it's thermal and internal flow stuff, and only a few studies a year. 

I'm trying to look at this by the numbers, not just trusting my guts.

So where's the tipping point for you? I think the license cost is the number everyone sees.

But I think we should also consider stuff like validation, getting good at prepping the model for the software, and keeping someone practiced enough to trust their own results.

For teams that only do this a few times a year, what made you pick one way over the other?