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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

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This paper describes a possibility-based multidisciplinary optimisation for electric-powered unmanned aerial vehicles (UAVs) design. An in-house integrated UAV (iUAV) analysis program that uses an electric-powered motor was developed and validated by a Predator A configuration for aerodynamics, weight, and performance parameters. An electric-powered propulsion system was proposed to replace a piston engine and fuel with an electric motor, power controllers, and battery from an eco-system point of view. Moreover, an in-house Possibility-Based Design Optimisation (iPBDO) solver was researched and developed to effectively handle uncertainty variables and parameters and to further shift constraints into a feasible design space. A sensitivity analysis was performed to reduce the dimensions of design variables and the computational load during the iPBDO process. Maximising the electric-powered UAV endurance while solving the iPBDO yields more conservative, but more reliable, optimal UAV configuration results than the traditional deterministic optimisation approach. A high fidelity analysis was used to demonstrate the effectiveness of the process by verifying the accuracy of the optimal electric-powered UAV configuration at two possibility index values and a baseline.

The global variable-fidelity modelling (GVFM) method presented in this article extends the original variable-complexity modelling (VCM) algorithm that uses a low-fidelity and scaling function to approximate a high-fidelity function for efficiently solving design-optimization problems. GVFM uses the design of experiments to sample values of high-and low-fidelity functions to explore global design space and to initialize a scaling function using the radial basis function (RBF) network. This approach makes it possible to remove high-fidelity-gradient evaluation from the process, which makes GVFM more efficient than VCM for high-dimensional design problems. The proposed algorithm converges with 65% fewer high-fidelity function calls for a one-dimensional problem than VCM and approximately 80% fewer for a two-dimensional numerical problem. The GVFM method is applied for the design optimization of transonic and subsonic aerofoils. Both aerofoil design problems show design improvement with a reasonable number of high-and low-fidelity function evaluations.