OPTIMIZATION OF FILM COOLING SIMULATION VIA NEURAL NETWORK-BASED GEKO TURBULENCE MODEL TRAINING

Abstract

The present study investigates the GEKO turbulence model trained by a Feed Forward Neural Network for the improvement of gas turbine film cooling simulations. The key flow features from the GEKO simulation are used as the inputs for the neural network, and the turbulence kinetic energy source coefficient of the GEKO model serves as the output parameter. Both the inputs and the output are being updated by the adjoint optimization during each training cycle, known as a “design iteration”. The training of GEKO model is successful on a very coarse mesh of the single-hole, jet-in-crossflow MIT Liner film cooling case. The trained GEKO solutions better match the LES and the experiments than the untrained GEKO on the wall heat fluxes. Plus, the trained GEKO model can be extended to another flow blowing ratio and cooling material of the MIT Liner, without any retraining. Furthermore, the same trained GEKO can even be applied to a multi-hole, film-cooled Perforated Plate case with complex geometry, where the trained GEKO considerably improves the plate's wall temperature predictions, better matching the LES and the measurements than the untrained GEKO model. The present automated GEKO training workflow, and the good generalizability of the trained GEKO model across different film cooling conditions and geometries, may help the gas turbine industry speed-up the film cooling design process for components such as combustor liners and turbine blades, saving a significant amount of computational time and resources.