A high-fidelity low-cost aerodynamic model using proper orthogonal decomposition
In this paper a high-fidelity aerodynamic model is presented for use in parametric studies of weapon aerodynamics. The method employs a reduced-order model obtained from the proper orthogonal decomposition (POD) of an ensemble of computational fluid dynamics (CFD) solutions with varying parameters. This decomposition produces an optimal linear set of orthogonal basis functions that best describe the ensemble of numerical solutions. These solutions are then projected onto this set of basis functions to provide a finite set of scalar coefficients that represent the solutions. A pseudo-continuous representation of these projection coefficients is constructed, which allows predictions to be made of parameter combinations not in the original set of observations. The paper explores the performance of a few design-of-experiment approaches for the generation of the initial ensemble of computational experiments. Response surface construction methods based on parametric and non-parametric models for the pseudo-continuous representation of the projection coefficients are also evaluated. The model has been applied to two-flow problems related to high-speed weapon aerodynamics, inviscid flow around a flare-stabilized hypersonic projectile and supersonic turbulent flow around a fin-stabilized projectile with drooping nose control. Comparisons of model predictions with high-fidelity CFD simulations suggest that the POD provides a reliable and robust approach to the construction of reduced-order models. The practicality of the model is shown to be sensitive to the technique used to generate the ensemble of observations from which the model is constructed, while the accuracy of the approach depends on the pseudo-continuous representation of the projection coefficients. Copyright (C) 2009 John Wiley & Sons, Ltd.
proper orthogonal decomposition reduced-order modelling response surfaces singular value decomposition linear regression radial basis functions