Authors: Catherine S. Johnson; Mark Woodgate; George N. Barakos
Addresses: CFD Laboratory, School of Engineering, University of Liverpool, L69 3GH, UK. ' CFD Laboratory, School of Engineering, University of Liverpool, L69 3GH, UK. ' CFD Laboratory, School of Engineering, University of Liverpool, L69 3GH, UK; Kazan State Technical University, named after A.N. Tupolev, 10 K. Marx St., Kazan 420111, Russian Federation, Russia
Abstract: This work presents a method for the optimisation of aspects of rotor blade shape in forward flight. The proposed technique employs CFD in conjunction with artificial neural networks (ANNs) and genetic algorithms (GAs). The developed method was used to optimise the anhedral and sweep of the UH60-A rotor blade in forward flight. A parameterisation method was defined, a specific objective function was created using the initial CFD data and the metamodel was used for evaluating the objective function during the optimisation. The obtained results suggest optima in agreement with engineering intuition but provide precise information about the shape of the final lifting surface and its performance. The results were checked using different optimisation methods and metamodels and were not sensitive to the employed techniques with substantial overlap between the outputs of the selected methods. The main CPU cost was associated with populating the CFD database necessary for the metamodel.
Keywords: aerodynamics; helicopter rotor design; optimisation; genetic algorithms; GAs; artificial neural networks; ANNs; metamodelling; computational fluid dynamics; CFD; UH60-A; harmonic balance; rotors; rotor blade shape; forward flight; helicopters.
International Journal of Engineering Systems Modelling and Simulation, 2012 Vol.4 No.1/2, pp.79 - 93
Published online: 30 Aug 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article