Repetitively enhanced neural networks method for complex engineering design optimisation problems

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 review of the artificial neural network surrogate modeling in aerodynamic design

“…For example, the surrogate models by implementing enhanced neural networks (ENNs) have been conducted in establishing a hybrid optimizer, which is executed to search for the first tentative optimal point. 12 The analysis code is performed on the tentative optimal point to check the difference between the surrogate model ENN and the analysis code.…”

“…21 Its accurate generalization and parallel computation capabilities in complex engineering design problems are helpful in the rapid investigation of design space and searching for optimalities. 12 For example, ANN has been used to expedite decision-making process in early stages of aircraft design process and to select proper combination of engine thrust, wing area, and the aircraft weight without going through elaborate details of other direct approaches. 22 The applications and prospects in some new frontiers of ANN surrogate modeling will be discussed in a later section in detail.…”

“…That is to say, obtaining enough information to predict a design landscape in a hypercube of increasing dimensions is a barrier in many optimizations. 12 Approaches including abstraction of problem dimensions and simplification of geometry topology have been adopted in many studies to relieve this difficulty, which yet leads to the inevitable deviation from real design. 13 A strategy of optimization is demanded to be efficient, intelligent, and credible for balancing the modeling efforts and design space scope.…”

Efficient stall compliance prediction method for trimmed very light aircraft with high-lift devices

“…Thus, high fidelity analysis tools such as computational fluid dynamics (CFD) and the finite element method (FEM) have recently been integrated into the system to enhance the fidelity and reliability of aircraft performance results. 5,6 Chiba et al 7 implemented the RANS solver and Nastran for the coupled aero-structural wing shape design to enhance the wing configuration results. Peigin et al 8 performed the aerodynamic shape design optimisation for business jet using CFD tool.…”

An Efficient Low-Speed Airfoil Design Optimization Process Using Multi-Fidelity Analysis for UAV Flying Wing

“…These points are used to define the airfoil shape. N. V. Nguyen et al [9] modeled airfoil geometry by the class shape function transformations (CST) method [10]. CST method is defined by combined class function with shape function.…”