Gene expression programming for improving turbulence models

CFD is becoming increasingly important in the design of gas turbines because correlation based methods are unable to further improve efficiency and laboratory experiments are prohibitively expensive. As first-principles based CFD is too computationally costly in a design context, RANS-based CFD is typically used where turbulence is modelled. However, the inaccuracies introduced by RANS limits the impact CFD can have on technology development.
In this presentation, a novel machine-learning based approach is introduced that uses high-fidelity data to improve turbulence closures. It will be shown that closure models developed using the gene-expression programming approach outperform traditional models both for cases they were trained on and for cases not seen before.