Surrogate-based aerodynamic shape optimization of a civil aircraft engine nacelle

Description/Abstract

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