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http://dx.doi.org/10.5139/JKSAS.2020.48.1.13

Rapid Estimation of the Aerodynamic Coefficients of a Missile via Co-Kriging  

Kang, Shinseong (Department of Aerospace Engineering, Pusan National University)
Lee, Kyunghoon (Department of Aerospace Engineering, Pusan National University)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.48, no.1, 2020 , pp. 13-21 More about this Journal
Abstract
Surrogate models have been used for the rapid estimation of six-DOF aerodynamic coefficients in the context of the design and control of a missile. For this end, we may generate highly accurate surrogate models with a multitude of aerodynamic data obtained from wind tunnel tests (WTTs); however, this approach is time-consuming and expensive. Thus, we aim to swiftly predict aerodynamic coefficients via co-Kriging using a few WTT data along with plenty of computational fluid dynamics (CFD) data. To demonstrate the excellence of co-Kriging models based on both WTT and CFD data, we first generated two surrogate models: co-Kriging models with CFD data and Kriging models without the CFD data. Afterwards, we carried out numerical validation and examined predictive trends to compare the two different surrogate models. As a result, we found that the co-Kriging models produced more accurate aerodynamic coefficients than the Kriging models thanks to the assistance of CFD data.
Keywords
Aerodynamic Coefficient; Machine Learning; Gaussian Process; Multi-Fidelity Modeling;
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