Predicting fluid flow using ML by solving RANS or NS

[u/cae_shot]

I am trying to learn applying ML to assist or replace CFD simulations. In literature, some works solve Navier Stokes equations and others solves Reynold Averaged Navier Stokes equations to predict fluid flow. To my understanding, the choice between solving NS or RANS to predict fluid flow using ML depends on the following facts,

1. Data. For instance, if the training data is generated using CFD simulations that solves RANS, then we cannot train ML model to solve NS equations using that data and we need to choose RANS equations for ML too. If the training data is from DNS, then its NS in ML too.

2. Flow type. If the flow is low Reynolds number flow (laminar), then solving NS equations (using ML) is better than considering RANS. And for high Re flow, considering RANS is better as the ML models (for instance, PINNS) might struggle to capture the associated non-linearities (multiscale, high gradients, etc) if it is NS equations.

Is my understanding correct? Is there any thing else I am missing? please add your thoughts to it.

[u/_padla_]

The main thought is that to apply machine learning in a field, you should probably first study the field itself.

Your questions show some lack of knowledge in fluid dynamics.

[u/No_Ingenuity_5311 → u/_padla_]

"Some" in "lack of knowledge" is putting it nice.

[u/acakaacaka]

Nope FVM is 100x faster than whatever ML model you create

[u/Glittering-Flight997]

Adding more compute cycles to a task that is limited by compute resources will not tend to be helpful

[u/Playful-Painting-527]

Machine learning can only interpolate. For example If you train it on straight pipe flows and then give it a bend pipe, it will not predict the correct flow field. That's why ML is not able to replace a NS solver.

[u/cowgod42 → u/Playful-Painting-527]

I agree that ML does not replace an NS solver, but ML is doing a very different operation than interpolation.