A new procedure for the optimisation of aerodynamic properties of trains is presented, where simple RS (response surface) models are used as a basis for optimisation instead of a large number of evaluations of the Navier-Stokes solver. The suggested optimisation strategy is demonstrated in two flow optimisation cases. Optimisation of the train's front for the crosswind stability and optimisation of VGs (vortex generator) for the purpose of drag reduction. Besides finding the global minimum for each aerodynamic objective, a strategy for finding a set of optimal solutions is demonstrated. This is based on the use of genetic algorithms on RS models. The resulting Pareto-optimal solutions are used to explore the extreme designs and find trade-offs between design objectives. For the optimisation of VGs, three different RS models are used: polynomial functions, RBNNs (radial basis neural network), and RBNN-enhanced polynomial RSs. The three approaches produce different results, and the combination of RBNN and polynomial functions in the last approach is found to be the best as it enables the construction of high-order polynomial functions, and the model's fit with the data is the best.
Shape optimization of high-speed trains for improved aerodynamic performance
2009
14 Seiten, 11 Bilder, 9 Tabellen, 23 Quellen
Aufsatz (Zeitschrift)
Englisch
Shape optimization of high-speed trains for improved aerodynamic performance
British Library Online Contents | 2009
|Shape optimization of high-speed trains for improved aerodynamic performance
SAGE Publications | 2009
|Shape optimization of high-speed trains for improved aerodynamic performance
Online Contents | 2009
|Numerical Simulation of Aerodynamic Performance of High Speed Trains
British Library Conference Proceedings | 1994
|