Subsonic Aircraft Design Optimization with Neural Network and Regression Approximators

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.

doi:10.2514/1.11113

Cited by 3 publications

(1 citation statement)

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“…The two metal models demonstrated a 27% reduction in hub vibration and a 45% reduction in flap power (9) . In addition, ANN was also applied for subsonic aircraft design optimisation at the conceptual design stage due to its accurate regression and parallel computing (10) . S. Wei et al used neural networks for global aerodynamics and rocket propulsion components to avoid the computational loads during the optimisation loop.…”

Section: Introductionmentioning

confidence: 99%

Repetitively enhanced neural networks method for complex engineering design optimisation problems

et al. 2015

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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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Section: Introductionmentioning

confidence: 99%