Creating Data-Driven Turbulence Models Using PIV

Abstract

Data-driven turbulence models have been created for a k-ε RANS model based on PIV data in a jet in crossflow. In the simpler of two implementations, the nominal value of the model coefficient Cµ was replaced with an optimized value calibrated to the PIV results. Despite being based on only four flow cases of a canonical configuration, the optimized model demonstrated superior performance over 48 flow cases of increasing complexity. A second, more sophisticated data-driven model has been created by mapping a spatially variable Cµ to flow state variables using machine learning of experimentally measured flow field properties of the turbulence. This second model has been implemented in a production RANS code but requires further improvements before it can return results deviating from either the nominal or calibrated Cµ models.