Evaluation of a Machine Learning Turbulence Model in a Square Transverse Jet in Crossflow

Film cooling is an important technique for the protection of axial turbine blades from high gas temperatures. An appropriate study is necessary in order to obtain a reliable representation of the flow characteristics involved in such phenomena. Because of the high computational cost of high-fidelity simulations, the low-fidelity simulation method Reynolds Averaged Navier Stokes (RANS) is commonly used in practical configurations. However, the majority of the current turbulent heat flux models fail to accurately predict heat transfer in film cooling flows. Recent work suggests the use of machine learning models to improve turbulent closure in these flows. In the present work, a machine learning model for spatially varying turbulent Prandtl number described in the literature is applied to a film cooling flow consisting of a row of square, transverse holes. The results obtained in the present work were compared to adiabatic effectiveness experimental data available in the literature to assess the performance of the machine learning model. The results shown that for low velocity ratio (r=0.2 and r=0.4) the proposed machine learning model has low performance. However, for the case with the highest velocity ratio (r=0,8), the proposed model presented better performance. These results are then explained in terms of the resulting turbulent Prandlt number field and suggest that the training set is not appropriate for capturing the turbulent heat flux in fully attached jets in crossflow.