Multi-objective CFD-driven development of coupled turbulence closure models

This paper introduces two novel concepts in data-driven turbulence modeling that enable the simultaneous development of multiple closure models and training towards objectives. The extend evolutionary framework by Weatheritt Sandberg (2016) [1], which derives interpretable implementation-ready expressions from high-fidelity simulation data. By assigning a shared fitness value to evolved utilizing CFD-driven approach Zhao et al. (2020) [2], multi-expression concept introduced here is able account for coupling between trained models, i.e. Reynolds stress anisotropy, turbulent heat flux production correction models. As second concept, multi-objective optimization algorithm applied framework. extension yields diverse set candidate allows trade-off objectives after analyzing results. In this study, are benchmark periodic hills case vertical natural convection flow. predictions mean flow quantities improved compared decoupled strategies with distinct robust improvements strongly coupled momentum thermal fields. balancing considered important capabilities on path generalized