DNN-Based Surrogate Modeling-Based Feasible Performance Reliability Design Methodology for Aircraft Engine

Abstract

The risks and costs of developing a new aeroengine are fundamentally depending on the performance design final proposal. Thus, this paper presents a novel aeroengine performance design methodology that is committed to managing the effectiveness and economic availability of the design proposal. To reach such a target, the presented methodology formulates the traditional thermal cycle design problem as a reliability-based fuzzy optimization. The performance reliability is predicted by the deep neural network (DNN)-based surrogate models while a hyperparameter tuning technique is proposed to find the optimal DNN topology for better implementing a particular deep learning task. The testing results imply the DNN models with optimized topology possess remarkable function approximation capability in global so that achieves significantly higher prediction accuracy. Moreover, the DNN-based surrogate models only cost nearly 0.003% as much computing time as MC simulation (2.3591 sec vs 64746 sec, for 20 samples). Such kind of remarkably higher computational efficiency facilitates the optimization for reliability-based fitness calculation. The efficiency of the presented methodology can be further verified by abundant feasible cycle proposals. The obtained cycle solutions can achieve expected reliability (>95%) in all reference states without unnecessary performance redundancy. Besides, the diversity of feasible cycle solutions contributes to the selection of best proposal associated with engineering situation. The presented effort is favorable to acquire a more cost-efficient design proposal and enrich thermodynamic system design theory.