Multi-fidelity model based optimization of shaped film cooling hole and experimental validation

Abstract

An optimization framework is developed for the film cooling hole shape design to achieve highest film cooling effectiveness with affordable cost. A key to this design method is the development and applications of a multi-fidelity model (MFM) to improve the efficiency of surrogate model construction. To construct multi-fidelity model, empirical correlations from existing literature are utilized as low-fidelity data and Reynolds-Averaged Navier-Stokes (RANS) simulations provide high-fidelity data. Compared with single-fidelity surrogate models, multi-fidelity model developed in this work combines vast existing knowledge about shaped film cooling hole and three dimensional numerical simulation. As a result, the computation cost has been substantially decreased by 64.5% while accuracy is maintained with similar level, which provides a firm basis for the optimization. Considering three geometric parameters as design variables, i.e. laidback angel, lateral angle, and hole length-diameter ratio, the shaped hole is optimized with genetic algorithm (GA) combined with sequential quadratic programming (SQP) algorithm. After the optimization, an experiment campaign is further carried out to validate the optimization result using pressure sensitive paint (PSP) technology. With current multi-fidelity model, the spatially averaged film cooling effectiveness has been improved by 39% compared to the baseline, which is an already well-designed shaped hole and the improvement is also verified by experiment. This work verifies the effectiveness of current multi-fidelity model for the design and optimization of shaped film cooling hole for better performance.