turbo-RANS: Straightforward and Efficient Bayesian Optimization of Turbulence Model Coefficients

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. Although tuning these coefficients can produce significantly improved predictive accuracy, their default values are often used. We believe users do not calibrate RANS models for several reasons: there is no clearly recommended framework to optimize these coefficients; the average user does not have the expertise to implement such a framework; and, the optimization of the values of these coefficients can be a computationally expensive process. In this work, we address these issues by proposing a semi-automated calibration of these coefficients using a new framework based on Bayesian optimization. We introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse, or dense reference data. We demonstrate the computationally efficient performance of turbo-RANS for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel. In the first two examples, we calibrate the $k$-$\omega$ shear stress transport (SST) turbulence model and, in the last example, we calibrate user-specified coefficients for the Generalized $k$-$\omega$ (GEKO) model in Ansys Fluent. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure. Towards the goal of facilitating RANS turbulence closure model calibration, we provide an open-source implementation of the turbo-RANS framework that includes OpenFOAM, Ansys Fluent, and solver-agnostic templates for user application.Comment: 30 pages, 18 figures. Submitted to Journal of Computational Physic