Download the report on pyCALC-RANS at

https://www.tfd.chalmers.se/~lada/postscript_files/py-calc-rans.pdf


For instructions how to run the code, see Section 11.3.


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The NN model for creating the NN model for EARSM is found in the folder NN/

The NN model is incorporated in pyCALC-RANS in the folder channel-10000-earsm-NN/

The work is presented in the paper 

L. Davidson, "Using Neural Network for Improving an Explicit Algebraic Stress Model in 2D Flow",
J. Tyacke and N. R. Vadlamani (eds.), Proceedings of the Cambridge Unsteady Flow
Symposium 2024, pp. 37--53, 2025, 

https://doi.org/10.1007/978-3-031-69035-8_2


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The PINN model for computing prand_k is located in folder  PINN/

compute vist_{t,PINN}:  PINN/compute-vist_k-solving-PDE-using-PINN.py

compute prand_{k,NN}, c_{k,NN} and  c_{\omega 2, NN}: PINN/compute-c_k-and-c_omega_2-from-balance-of-k-and-omega-eqns.py


Run PINN/compute-vist_k-solving-PDE-using-PINN.py

to create vist_{k,PINN}

Then run PINN/compute-c_k-and-c_omega_2-from-balance-of-k-and-omega-eqns.py

It is  used in the CFD code in folder channel-2000-half-channel-PINN/

It is used in the paper

 L. Davidson, 
"Using Physical Informed Neural Network (PINN) to Improve a k-omega Turbulence Model",
ERCOFTAC Symposium on Engineering Turbulence Modelling and Measurements (ETMM-15), Dubrovnik on 22-24 September 2025.
https://www.tfd.chalmers.se/~lada/postscript_files/Using-Physical-Informed-Neural-Network-PINN-to-Improve-a-k-omega-Turbulence-Model.pdf

https://www.tfd.chalmers.se/~lada/PINN-improve-a-k-omega-turbulence-model.html

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PINN scripts are found  in folder PINN-NN

compute vist_{t,PINN}:  PINN/compute-vist_k-solving-PDE-using-PINN.py

compute prand_{k,PINN} using vist_{t,PINN}: pytorch-solve-k-omega-with-vist_ML-c_k_ML-c_omega_2_ml-from-balance-half-channel-5200-plus-units-prand-max.eq.2-10-omega-bc-works-nj120/py-solve.py

this solver is a truncated form of pyCALC-RANS

compute c_{K,PINN} and  c_{\omega 2, PINN}: PINN-NN/pl_vist_ML-c_k-c_omega_2000_5200-plus-units.py

create NN model for c_{K,NN}: PINN-NN/neural-k-omega-c_k-vist-over-y-and-uv_tot.py

create NN model for c_{omega 2,NN}: PINN-NN/neural-k-omega-c_omega_2-vist-over-y-and-uv_tot.py

create NN model for prand_{k,NN}: PINN-NN/neural-k-omega-prand_k-vist-over-y-and-uv_tot.py

Use NN models in CFD code: channel-10000-half-channel-NN-PINN-vist-over-y-uv_tot-no-interp-averaged-m-3000-uv-max-0.995-nj-150/

Lars Davidson.
Using Physics Informed Neural Network (PINN) and Neural Network (NN) to Improve a $k-\omega$ Turbulence Model

