Variable-fidelity surrogate modeling offers an efficient way to generate aerodynamic data for aero-loads prediction based on a set of CFD methods with varying degree of fidelity and computational expense. In this paper, new algorithms, such as a Gradient-Enhanced Kriging method (direct GEK) and a generalized hybrid bridge function, have been developed to improve the efficiency and accuracy of the existing Variable-Fidelity Modeling (VFM) approach. These new algorithms and features are demonstrated and evaluated for analytical functions and used to construct a global surrogate model for the aerodynamic coefficients and drag polar of an RAE 2822 airfoil. It is preliminarily shown in this paper that they are very promising and can be used to significantly improve the efficiency and accuracy of VFM in the context of aero-loads prediction.
On Improving Efficiency and Accuracy of Variable-Fidelity Surrogate Modeling in Aero-data for Loads Context
2009 ; Manchester, UK
2009-10-01
Conference paper
Electronic Resource
English
A Variable-Fidelity Modeling Method for Aero-Loads Prediction
Tema Archive | 2010
|A Variable-Fidelity Modeling Method for Aero-Loads Prediction
German Aerospace Center (DLR) | 2008
|Variable-Fidelity and Reduced-Order Models for Aero Data for Loads Predictions
Springer Verlag | 2013
|Variable-Fidelity and Reduced-Order Models for Aero Data for Loads Predictions
British Library Conference Proceedings | 2013
|Variable Fidelity Methods and Surrogate Modeling of Critical Loads on X-31 Aircraft
British Library Conference Proceedings | 2013
|