Repetitively enhanced neural networks method for complex engineering design optimisation problems

Abstract

Abstract A Repetitively Enhanced Neural Networks (RENN) method is developed and presented for complex and implicit engineering design problems. The enhanced neural networks module constructs an accurate surrogate model and avoids over-fitting during neural networks training from supervised learning data. The optimiser is executed by the enhanced neural networks models to seek a tentative optimum point. It is repetitively added into the supervised learning data set to refine the surfaces until the RENN tolerance is reached. The RENN method demonstrates the effectiveness and feasibility for a 2D highly non-linear numerical example and the structure design of a two-member frame reaching a convergent solution at 10 and 15 iterations at the maximum error of 1% when compared with the exact solution. Then, the RENN method is applied for a long endurance unmanned aerial vehicle (UAV) aerofoil design optimisation. A Class/Shape function transformation (CST) geometry parameterisation method represents an accurate UAV aerofoil with ten geometry design variables. The high-fidelity analysis solver with structured mesh is used for a UAV aerofoil design problem. Using the RENN method, an optimal UAV aerofoil is obtained using 88 high fidelity evaluations at an error of 1·24%. The process reduces the computational time by 81·2% compared with the full high fidelity model. The optimal aerofoil shows a drag reduction of 10·8% in the cruise condition and an improvement in the maximum lift coefficient and stall angle-of-attack when compared with the baseline AG24 aerofoil.