r/FluidMechanics

They remain one of the best collections of Fluid Mechanics videos I have ever come across, absolutely worth watching!

I haven’t seen a better visual resource that explains the fundamentals of fluid mechanics so effectively!

Created nearly half a century ago under the direction of the National Committee for Fluid Mechanics Films, these videos brilliantly cover almost all fundamental fluid flow phenomena.

What makes them stand out is how heavily they are illustrated with real experimental visuals.

Just google "National Committee for Fluid Mechanics Films (NCFMF)" to find these lectures.

3 weeks series free course on openFOAM!

This series gives the possibility of getting a more detailed understanding of the basics of OpenFOAM.

It can be completed in about three weeks.

▶ Week - 1
openFOAM Installation
Introduction to openFOAM
Theory & Fun simulations

▶ Week - 2
Geometry preparation & Meshing
Turbulence Modeling
Multiphase Modeling
Parallelization in openFOAM

▶ Week - 3
Programming in openFOAM

Just google, "3 weeks series openfoam" you can find it easily.

Check out this amazing series. It's free!

I had an unusually vivid dream right before waking and sketched the process I saw. I’m not an engineer and haven’t been researching helium or fluid/gas systems recently, which is why it stood out to me.

The diagram showed a constant left-to-right flow originating from the box on the left. Molecules moved through several sealed chambers separated by barriers or membranes. In the final chamber, the flow curved downward, around, and then upward before exiting. The impression in the dream was that a lighter component (I somehow understood it as helium) was progressively separated and finally exited upward, creating lift on a thin plate above it.

Just curious whether this resembles any known concepts — membrane separation, staged gas systems, buoyancy systems, airlift pumps, or something else entirely.

Does this remind anyone of a real process or technology?

So, I am a bit stumped. My friend and I came across this line in the water and ran some tests to figure out what was causing it.

We sprayed ethanol on the surface to destroy any biofilms, stirred up the water to disturb any temp gradient, stirred up the material under the surface, moved the logs flanking it around and away. All to no effect.

The line was only visible through light diffraction and the best way I can describe it is almost like gravitational lensing. Any ideas would be great, I plan on going back tonight to collect samples, test the diffraction gradient, and get better footage.

EDIT: I couldn't add the photos and the video so I am going to post the photos seperatly

Below is the link to the photo post:

https://www.reddit.com/r/FluidMechanics/s/8Wfb0vQeYZ

The wall function uses an empirical formula formulated based on experimental measurements and documented as the "Law of the Wall"

That empirical formula satisfies the log-region velocity profile.

So, you have to put the first cell centroid in the log layer.

In the second-order finite volume method, the variation across the cell for any flow variable is linear.

You are not resolving the velocity gradient as first cell placed in the log layer, but the wall shear stress must be correct.

The wall function modifies the viscosity in the cell adjacent to wall such that the product of velocity gradient and viscosity, shear stress, remains correct.

We will have wall functions computed near the wall for other flow quantities.

The thickness of the log layer plays a critical role in the selection of the wall function.

Wall Function should not be used for the flows prone to separation.

In resolved boundary layer approach, mesh up-to the viscous sub layer is resolved such that the first cell centroid lies within the viscous sublayer.

This allows direct resolution of the velocity and turbulence gradients in this thin layer, instead of relying on wall functions.

In resolved boundary layer approach, Low-Re models are often used.

Low-Re turbulence models is a modified version of a traditional turbulence model (like k-ε or k-ω).

There are additional source terms in the turbulence equations to control how turbulence is suppressed near the wall.

These extra terms or functions account the effects of viscosity near the wall.

Different models use different damping functions​ which are dependent on local Reynolds numbers and vanish near the wall to suppress turbulence there.

Convergence of "resolved boundary layer approach" can be very slow because of high aspect ratio cells near the wall and very high overall mesh count.

Image Source: "Computational Modelling of Non-Equilibrium Condensing Steam Flows In Low-Pressure Steam Turbines 0.1016/j.rineng.2019.100065 Ahmed M. Nagib Elmekawy, Mohey Eldeen H.H. Ali"

I noticed something strange from a CFD simulation that I've been trying to find an answer for and nothing seems to fully explain it.

In the picture is the Mach contour for an aerospike nozzle, which has its central plug slightly off-center, which produces a deviation in the jet (simulation is made with cold air at 4 bar at the inlet, in atmospheric conditions, gravity off).

If I measure this deviation through the forces acting on all its surfaces (using the Ansys Post force calculation tool; angle = arctan(Fy/Fx)), I get an angle of 2.1 degrees. If I measure the jet visually, as pictured, I get 5.2 degrees. A colleague measured it more "correctly" by averaging the angle of velocity for all the cells of the jet and he got a similar answer.

Where is this difference coming from? Shouldn't a momentum change in the velocity cause an opposite force at the same angle on the nozzle?

If you want to learn how to code CFD, you have to start with this!

Step by Step 12 python module to write your own 2D Navier-Stokes finite-difference solver from scratch.

1️⃣ 1D Linear Convection
2️⃣ 1D Non-Linear Convection
3️⃣ 1D Diffusion Equation
4️⃣ 1D Burger's Equation
5️⃣ 2D Linear Convection
6️⃣ 2D Non-Linear Convection
7️⃣ 2D Diffusion Equation
8️⃣ 2D Burger's Equation
9️⃣ 2D Laplace Equation
1️⃣0️⃣ 2D Poisson Equation
1️⃣1️⃣ 2D Cavity Flow
1️⃣2️⃣ 2D Channel Flow

Just google "12 Steps to Navier Stokes" to find these modules!

So im wondering if you can have a fullsize outside pool that has a small opening near the bottom that you can swim through and it brings you into a pool in the basement.

Ive thought about having a catchbasin around the basement pool with a pump attached to constantly refill the outside pool, but im wondering how fast the water would be moving through the divider, and how much the pump would need to pump up. I tried using bernoullis equation and got 6.3m/s for the fluid velocity, but that seems too fast, and its been a while since ive used it.

Ive also thought about just pressurizing the basement room to 1.2atm.

We are this peristatic pump to impregnation the stator . But we are facing series pipe cut issue in pumping area, is there any other alternative method ? current using silicon hose

One of the most interesting things in CFD and Fluid Mechanics is how the physics of flow changes with PDE classification.

• Elliptic PDEs → equilibrium & potential flow problems
• Parabolic PDEs → diffusion and dissipative transport
• Hyperbolic PDEs → wave propagation, shocks, compressible flow

Each equation type has completely different mathematical behavior, boundary conditions, and solution characteristics.

CFD Roadmap + AI/ML Roadmap + CFD Theory Material

Fluid dynamics looks complex… it is just one equation at its core!

➡️ Navier–Stokes Equations
The most general equation that governs how fluids move.

➡️ Euler Equations
High speed fluid motion when we ignore viscosity (no friction effects).

➡️ Stokes Equations
Very slow flow where inertia is negligible and viscous effects dominate.

➡️ Hydrostatic Equation
Pressure variation in a fluid that is completely at rest under gravity.

Based on dominant physics, these are simplified forms of Navier-Stokes.

Different numerical methods solving same fluid & heat flow physics.

All of these methods aim to solve the same governing equations (mass, momentum, and energy conservation) but from very different perspectives:

FDM: approximates derivatives directly on structured grids using Taylor Series.

FVM: enforces conservation locally over control volumes (industry favorite).

FEM: uses variational formulation & shape functions (strong in solid–fluid coupling).

LBM: mesoscopic approach using particle distribution functions & Boltzmann equation.

SPH: Mesh free Lagrangian, particle-based method for highly deforming flows.

The physics doesn’t change but the mathematics and discretization philosophy do.

As CFD engineers, understanding why a method works is far more important than just knowing how to run a solver.

Ciao a tutti,

ho sviluppato **AeroTwin**, un motore ibrido che combina Intelligenza Artificiale (Metis) con fisica reale per fare simulazioni aerodinamiche di monoposto F1 e hypercar **in tempo reale**.

Invece di aspettare ore o giorni con i CFD tradizionali, si ottengono drag, downforce, wake, linee di flusso ed efficienza quasi istantaneamente.

Ecco la demo completa del video:

[https://youtu.be/m8rs9IhWyXI]

Cosa ne pensate?

- Potrebbe essere utile per studenti, ingegneri amatoriali o team minori?

- Quali funzioni vorreste vedere in futuro? (ottimizzazione automatica, altri modelli di auto, ecc.)

Sono disponibile a rispondere a tutte le domande tecniche!

CFD postprocessing is storytelling!

When we run CFD, the solver gives us numbers, contours, and vectors.

But those plots alone rarely convince anyone.

At the end of the day, CFD doesn’t sell itself.

The story you tell through your postprocessing does.

When I look back at my early CFD projects, I realize something important.

I thought running the solver was the hardest part.

Mesh generation, turbulence models, BCs, I obsessed over them.

When the results came in, I proudly shared contour plots and vector fields.

And the response was silence.

Why? Because nobody else in the room could see what I was seeing.

To them, it was just a rainbow of colors.

To me, it was flow separation, pressure recovery, and efficiency losses.

That’s when I understood, post processing is not plotting. It’s storytelling.

The job of a CFD engineer is not just to simulate. It’s to translate.

To take terabytes of raw data and weave them into a story that drives action.

Anyone can generate plots.

But only an engineer who can tell the story can create impact.

Source: SimScale [Vorticity at the back of the estateback DrivAer model]

Video

How is the area for the first term in the denominator pi d L and not 2 pi L like the second term. Is the same term for convection on a plane wall and cylindrical pipe used in both cases? I hope that made sense I’m only a day deep into heat transfer.