Physics-informed neural networks for data-driven fluid model

[EvertBunschoten]

Proposed Changes

The data-driven fluid model in SU2 has been upgraded with the ability to use physics-informed neural networks (PINN's) to compute the fluid thermodynamic state for NICFD flows. This functionality was presented during the 2024 SU2 conference and the issues regarding non-physical behavior have been addressed.

The use of PINN makes the data-driven fluid model substantially more robust and efficient for flow and adjoint simulations of flows of fluids in the non-ideal thermodynamic state.

PINN's can be trained using SU2 DataMiner for any fluid or compatible mixture of fluids available in the CoolProp library. An elaborate tutorial integrating SU2 DataMiner with the data-driven fluid solver in SU2 will follow soon!

Related Work

PR Checklist

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[pcarruscag]

LGTM but @joshkellyjak should approve because I expect the way the merge and unmerge with the turbo ramp branch was done is going to create either conflicts or silently revert the work in that branch.

[joshkellyjak]

LGTM but @joshkellyjak should approve because I expect the way the merge and unmerge with the turbo ramp branch was done is going to create either conflicts or silently revert the work in that branch.

I'm happy to approve this and then update in turbo ramps branch. Still having the same regression test issue and couldn't find a solution. Probably easier to merge this and then fix on my side.

[joshkellyjak]

Could be a good oppurtunity to apply @oleburghardt 's tagging tool in #2343 , should dsde_rho be declared as an input to the preaccumlation region?