Physics-informed neural networks for data-driven fluid model #2444
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…f flame front initialization
…cs-informed neural networks
pcarruscag
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Feb 15, 2025
EvertBunschoten
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pcarruscag
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Feb 23, 2025
pcarruscag
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Feb 23, 2025
| su2double drhode_p = -dPde_rho/dPdrho_e; | ||
| su2double dTde_p = dTde_rho + dTdrho_e*drhode_p; | ||
| su2double dhde_p = dhde_rho + drhode_p*dhdrho_e; | ||
| Cp = dhde_p / dTde_p; |
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Are Cv and Cp local variables? Why are they not preaccumulated? Do you have any adjoint verification cases yet to be playing with preaccumulation?
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I initially copied the preaccumulation from the Peng-Robinson fluid model. I added some unit tests for fluid model consistency and I'm planning to add some AD unit tests as well.
… to separate structs
…ig accessor functions.
…tructs rather than config accessor functions.
…ations from structs rather than config accessor functions.
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pcarruscag
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Mar 1, 2025
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Thanks your changes look good, but having the Ramp PR here doesn't.
Probably better to wait for @joshkellyjak to finish the Ramp PR and merge it instead of trying to fix it here.
Check the code QL warnings and const& comments in the meantime.
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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!
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