Using automated optimisation to calibrate a correlation-based transition model

Abstract

Transition models used in turbomachinery CFD are based on empirical correlations derived from a series of fundamental test cases. When these models are applied to complex configurations they often do not perform as desired and need to be tuned to the respective area of application. In this paper, an approach to tune a correlation based transition model to a given set of turbine rigs using automated optimisation is presented. The model is optimised such that deviations from experiments in terms of blade pressure distribution and global losses are minimised. The optimisation strategy and results are analysed in detail showing strengths and weaknesses of the proposed approach. While the agreement with experimental data of the turbine rigs can be improved, an analysis of zero pressure gradient flat plate flow shows that the resulting models cannot be considered as general as the original model.