Multi-Objective Surrogate-Based Optimization Method for the Scaling of Combustion Chambers

Abstract

Abstract In this study, we developed an efficient computer-aided design tool for scaling combustor designs. From a limited number of fluid simulations, an original design is scaled while preserving most of the flow properties, using a multi-objective optimization method and deep neural network or kriging surrogate models. The accuracy and robustness of the method were first tested with a simple geometry. Investigations have shown a strong sensitivity of the surrogate models to the sample distribution, which can be reduced using a strategic sampling method. Subsequently, the geometry was scaled down to a factor of two using both surrogate models while preserving most of the flow features.