Strategies for Enhancing One-Equation Turbulence Model Predictions Using Gene-Expression Programming
Tony Di Fabbio,
Yuan Fang,
Eike Tangermann,
Richard D. Sandberg,
Markus Klein
Affiliations
Tony Di Fabbio
Department of Aerospace Engineering, Institute of Applied Mathematics and Scientific Computing, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Yuan Fang
Department of Mechanical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
Eike Tangermann
Department of Aerospace Engineering, Institute of Applied Mathematics and Scientific Computing, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Richard D. Sandberg
Department of Mechanical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
Markus Klein
Department of Aerospace Engineering, Institute of Applied Mathematics and Scientific Computing, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
This paper introduces innovative approaches to enhance and develop one-equation RANS models using gene-expression programming. Two distinct strategies are explored: overcoming the limitations of the Boussinesq hypothesis and formulating a novel one-equation turbulence model that can accurately predict a wide range of turbulent wall-bounded flows. A comparative analysis of these strategies highlights their potential for advancing RANS modeling capabilities. The study employs a single-case CFD-driven machine learning framework, demonstrating that machine-informed models significantly improve predictive accuracy, especially when baseline RANS predictions diverge from established benchmarks. Using existing training data, symbolic regression provides valuable insights into the underlying physics by eliminating ineffective strategies. This highlights the broader significance of machine learning beyond developing turbulence closures for specific cases.
