Turbulence modeling

The general subject of my research is computation of fluid flow and heat convective transfer using finite volume computations. I'm working with Python finite volume codes (pyCALC-LES, pyCALC-RANS-LES), which have been developed by myself. Turbulence modeling for fluid flow and heat transfer is the focus of my research. A large part of the research is concentrated on Large Eddy Simulations (LES).

Since around 2004, I have focused on hybrid LES/RANS, an approach to make it possible to use LES for wall-bounded flows at high Reynolds numbers. In 2022, I started to work on Machine Learning to improve wall functions and turbulence models.

Below you find some of my publications.

• L. Davidson, "Large Eddy Simulations: how to evaluate resolution", International Journal of Heat and Fluid Flow, Vol. 30(5), pp. 1016-1025,2009.
  Link to pdf

• L. Davidson, "Hybrid LES-RANS: back scatter from a scale-similarity model used as forcing", Phil. Trans. of the Royal Society A, Vol. 367, Issue 1899, pp. 2905-2915, 2009.
  Link to pdf

• L. Davidson, "Zonal PANS: evaluation of different treatments of the RANS-LES interface", Journal of Turbulence, Volume 17, Issue 3, pp. 274-307, 2016.
  Link to pdf

• L. Davidson, "Two-equation hybrid RANS-LES models: A novel way to treat k and omega at inlets and at embedded interfaces", Journal of Turbulence, Volume 18, Issue 4, pp. 291-315, 2017.
  Link to pdf

• L. Davidson and C. Friess, "A new formulation of _fk for the PANS model", Journal of Turbulence, Vol. 20(5), pp. 322-336, 2019.
  Link to Taylor & Francis online

• C. Friess and L. Davidson, "A formulation of PANS able to mimic IDDES", International Journal of Heat and Fluid Flow, Vol. 86, No. 108666, 2020.
  Link to Science Direct

•  L. Davidson, "pyCALC-LES: A Python Code for DNS, LES and Hybrid LES-RANS", Division of Fluid Dynamics, Dept. of Mechanics and Maritime Sciences, Chalmers University of Technology, Göteborg, 2020.
  Link to pdf

• L. Davidson and Ch. Friess, "Detached Eddy Simulations: Analysis of a limit on the dissipation term for reducing spectral energy transfer at cut-off", ETMM13: The 13th International ERCOFTAC symposium on engineering, turbulence, modelling, Rhodes, Greece, 15--17 September, 2021.
  Link to pdf

• L. Davidson, "pyCALC-RANS: A Python Code for Two-Dimensional Turbulent Steady Flow", Division of Fluid Dynamics, Dept. of Mechanics and Maritime Sciences, Chalmers University of Technology, Göteborg, 2021. 
  Link to publication

• L. Davidson, "Non-Zonal Detached Eddy Simulation coupled with a steady RANS solver in the wall region", International Journal of Heat and Fluid Flow,  Vol.92, 108880, 2021.
  Link to Science Direct

• L. Davidson, "Using Machine Learning for formulating new wall functions for  Detached Eddy Simulation", ERCOFTAC symposium on Engineering, Turbulence, Modelling and Measurements (ETMM14), in Mini-Symposium: Machine learning for turbulence, Barcelona, Spain 6th - 8th September 2023a.
  Link to pdf

• L. Davidson, "Using Neural Network for Improving an Explicit Algebraic Stress Model in 2D Flow",
  CUFS 2024, Proceedings of the Cambridge Unsteady Flow Symposium 2024
  Editors: J. C. Tyacke and N. R. Vadlamani.  Publisher: Springer Nature Switzerland AG (to appear)
  Download Python 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

• L. Davidson, "Using Physical Informed Neural Network (PINN) to Improve a k-omega Turbulence Model", ERCOFTAC symposium on Engineering, Turbulence, Modelling and Measurements (ETMM15) in Mini-Symposium: Machine learning for turbulence, Dubrovnik on 22-24 September 2025
  Link to pdf
  Download Python script and CDF code

• Lars Davidson, "Using Physics Informed Neural Network (PINN) and Neural Network (NN) to Improve a k-omega Turbulence Model", Journal of Turbulence, vol. 27, issue 7, pp. 187--208, 2026
  Link to publication
  Download the PINN and NN Pythons scripts and the CFD code

For more info, please visit my personal www-page.