Surrogate-Based Aerodynamic Shape Optimization of a Civil Aircraft Engine Nacelle

Abstract

In this paper, we present a study on the aerodynamic shape optimization of a three-dimensional subsonic engine\nnacelle using computational fluid dynamics simulations. Gaussian process-based surrogate modeling (kriging) and\nparameter screening techniques are combined to tackle the high cost associated with both computational fluid\ndynamics simulations and the large number of design variables involved, with a multi-objective genetic algorithm\nbeing used to obtain the Pareto fronts. The primary goal of the study was to identify the tradeoff between\naerodynamic performance and noise effects associated with various geometric features within practical\ncomputational costs. The fan face total pressure recovery is used to measure the aerodynamic performance, and the\nscarf angle is used as an indicator of the noise impact on the ground. The geometry is modeled using a feature-based\nparametric computer-aided design package. An unstructured tetrahedral mesh is generated for the subsequent\nsolution using the Reynolds averaged Navier–Stokes flow equations. Analyses of variance techniques are used to\nidentify the dominant geometry parameters, thereby reducing the number of design variables and computational\ncost in the trade study. Multiple Pareto fronts are constructed using progressively built kriging models based on\nsimulation data with the reduced parameter set. A full-scale search was also carried out for comparison with the\nresults produced using the reduced parameter set. The procedures outlined can be further applied to other\noptimization problems with significant numbers of parameters and high-fidelity analysis codes.