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Generalisability and the consistency of the a posteriori results are the mostcritical points of view regarding data-driven turbulence models. This studypresents a progressive improvement of turbulence models using simulation-drivensurrogate optimisation based on Kriging. We aim for the augmentation ofsecondary-flow reconstruction capability in a linear eddy-viscosity modelwithout violating its original performance on canonical cases e.g. channelflow. Explicit algebraic Reynolds stress correction models (EARSCMs) for$k-\omega$ SST turbulence model are obtained to predict the secondary flowwhich the standard model fails to capture. The optimisation of the models isachieved by a multi-objective approach based on duct flow quantities, andnumerical verification of the developed models is performed for various testcases. The results of testing new models on channel flow cases guarantee thatnew models preserve the performance of the original $k-\omega$ SST model.Regarding the generalisability of the new models, results of unseen test casesdemonstrate a significant improvement in the prediction of secondary flows andstreamwise velocity. These results highlight the potential of the progressiveapproach to enhance the performance of data-driven turbulence models for fluidflow simulation while preserving the robustness and stability of the solver.

Progressive augmentation of Reynolds stress tensor models for secondary flow prediction by computational fluid dynamics driven surrogate optimisation