A hybrid modelling method for preliminary design of civil aircraft engine nacelles

Abstract

Accurately modelling the nonlinear drag map within the design parameter space is a key challenge in preliminary nacelle design. However, due to sharp gradient variations in the drag distribution and the limited size of the available dataset, traditional surrogate models often suffer from insufficient predictive accuracy and poor generalization. To address this challenge, this study systematically evaluates the modelling performance of representative reduced-order models and deep learning–based approaches, and proposes a hybrid modelling framework (PMG) that integrates Proper Orthogonal Decomposition (POD), Multilayer Perceptron (MLP), and Gaussian Process Regression (GPR). The performance of various methods is evaluated and validated using high-resolution numerical simulations across the nacelle design parameter space. The results show that the PMG model reduces the required number of samples by 80% while accurately capturing the characteristics of complex drag distributions. Under small-sample conditions, the PMG model demonstrates superior predictive accuracy compared to traditional reduced-order models and deep learning-based approaches. This framework provides a promising approach for the rapid evaluation of preliminary nacelle designs.