pyCALC-RANS

• June 2024: pyCALC-RANS was extended with EARSM which was improved using Machine Learning (Neural Network)
• June 2025: pyCALC-RANS was extended with PINN (Physical-Informed-Neural-Network) for improving the k-omega turbulence model
• Nov 2025: pyCALC-RANS was extended with MUSCL scheme and k-eps model
• Nov 2025: pyCALC-RANS was extended with PINN (Physical-Informed-Neural-Network) and Neural Network (NN) for improving the k-omega turbulence model
• Nov 2025: pyCALC-RANS was extended with the 2D periodic hill flow

pyCALC-RANS is a 2D finite volume code. It is fully vectorized (i.e. no for loops). The solution procedure is based on the pressure-correction method (SIMPLEC). Two methods for discretizing the convection terms are available, second-order central differencing and a hybrid scheme of first-order upwind and second-order central differencing. The discretized equations are solved with Pythons sparse matrix solvers. The standard k-omega model is implented.

pyCALC-RANS has now been extended with EARSM (Explicit Algebraic Reynolds Stress turbulence Model) which has been improved using Neural Network. To run the Neural Network code and the EARSM, please read the README file.

• Download the code here (44 MB, 28 Nov 2025)
• Flowchart
• Download the pyCALC-RANS report. In Section 11.3 you find instructions on how to run the code.
  
• My papers on Machine Learing:
  • 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
    View PDF file
    Proceedings
    
  • 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.
    View paper
    Download Python script and CFD code
    
    
  • L. Davidson
    "Hybrid LES/RANS for flows including separation: A new wall function using Machine Learning based on binary search trees", Journal of Turbulence, 2025.
    Get article at publisher
    Download Python script and databases
    
    
  Download the 3D DNS/LES/DES pyCALC-LES code here

  
  
  
  
  

  • Prof. Lars Davidson www page
    
  • MTF073 CFD course
    
  • Heat Transfer course
    
  • Mechanics of fluids
    
  • Turbulence modeling
    
  • Compressible flow
  • OpenFOAM
    
  • PhD course: LES and DES using an in-house Python code
    
  • International MSc programme in Applied Mechanics
  • PhD course in CFD
  
  
  
  Department of Mechanics and Maritime Sciences
  Division of Fluid Dynamice