Optimizing the trench shaped film cooling design

Abstract

In this numerical study an optimized trench film cooling design is determined using a Bayesian algorithm and neural network trained RANS model. Three objective functions were considered, the area-averaged film cooling efficiency, spatial standard deviation of film cooling efficiency and hot gas ingestion into the trench. Nine geometrical design parameters were varied to allow for a 3D trench shape and to find an optimal trench design based on the initial parametrization of the trench. Jet-engine like inflow boundary conditions with respect to turbulence intensity and length scales were applied. The investigated momentum ratios (I) were 1 and 8 at a main flow Reynolds number (ReD) of 2500. For each design the steady state Reynolds Averaged Navier-Stokes (RANS) equations were solved using the commercial Computational Fluid Dynamics (CFD) code Ansys Fluent V2022 R1. The turbulence model coefficients of the generalized k−ω (GEKO) model were tuned to approximate the time-averaged 3D temperature field from a predictive Large Eddy Simulation (LES) and trained by a neural network to improve the prediction capability. The tuned GEKO model shows improved agreement with experimental data of a literature case compared to the standard GEKO model. With this tuned RANS model optimized trench designs are found and validated by additional LES’s. The optimized designs include angled side walls to improve former trench designs, particularly in mitigating hot gas entrainment into the trench, which could be omitted almost entirely.