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Industrial simulations of turbulent flows often rely on Reynolds-averagedNavier-Stokes (RANS) turbulence models, which contain numerous closurecoefficients that need to be calibrated. Although tuning these coefficients canproduce significantly improved predictive accuracy, their default values areoften used. We believe users do not calibrate RANS models for several reasons:there is no clearly recommended framework to optimize these coefficients; theaverage user does not have the expertise to implement such a framework; and,the optimization of the values of these coefficients can be a computationallyexpensive process. In this work, we address these issues by proposing asemi-automated calibration of these coefficients using a new framework based onBayesian optimization. We introduce the generalized error and defaultcoefficient preference (GEDCP) objective function, which can be used withintegral, sparse, or dense reference data. We demonstrate the computationallyefficient performance of turbo-RANS for three example cases: predicting thelift coefficient of an airfoil; predicting the velocity and turbulent kineticenergy fields for a separated flow; and, predicting the wall pressurecoefficient distribution for flow through a converging-diverging channel. Inthe first two examples, we calibrate the $k$-$\omega$ shear stress transport(SST) turbulence model and, in the last example, we calibrate user-specifiedcoefficients for the Generalized $k$-$\omega$ (GEKO) model in Ansys Fluent. Anin-depth hyperparameter tuning study is conducted to recommend efficientsettings for the turbo-RANS optimization procedure. Towards the goal offacilitating RANS turbulence closure model calibration, we provide anopen-source implementation of the turbo-RANS framework that includes OpenFOAM,Ansys Fluent, and solver-agnostic templates for user application.

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