Surrogate-based Sensitivity Analysis for Safety Assessment of General Aviation Heavy-fueled Engines

Abstract

This paper has developed a framework for surrogate-based sensitivity analysis in SSA considering the complexity of Heavy-Fueled Engines (HFE) models. Multi-dimensional HFE whole engine models are extreme time consuming and computationally expensive and not practical for engineering applications. To reduce the computational costs, Artificial Neural Networks (ANN) is selected as surrogate model to establish high-fidelity HFE system model. Based on the developed ANN model, global sensitivity analysis is conducted to provide information on parameters importance, which are significant in complex system safety analysis. The results reveal that, by using global sensitivity analysis, the parameters could be ranked with respect to their importance, including first order indices and total sensitivity indices. For the particular study, the importance indices indicate that compression ratio and start angle of injection are more important with respect to the influence on the maximum pressure for HFE. The results also show that ANN-based surrogate model is an efficient way for sensitivity analysis in HFE safety assessment.