Enhanced multi-fidelity model for flight simulation using global exploration and the Kriging method

Author:

Lee Daeyeon1,Van Nguyen Nhu1,Tyan Maxim1,Chun Hyung-Geun1,Kim Sangho1,Lee Jae-Woo1

Affiliation:

1. Department of Aerospace Information Engineering, Konkuk University, Seoul, Korea

Abstract

Using the global exploration and Kriging-based multi-fidelity analysis methods, this study developed a multi-fidelity aerodynamic database for use in the performance analysis of flight vehicles and for use in flight simulations. Athena vortex lattice, a program based on vortex lattice method, was used as the low-fidelity analysis tool in the multi-fidelity analysis method. The in-house high-fidelity AADL-3D code was based on the Navier–Stokes equations. The AADL-3D code was validated by comparing the data and the analysis results of the Onera M-6 wing and NACA TN 3649. The design of experiment method and the Kriging method were applied to integrate low- and high-fidelity analysis results. General data tendencies were established from the low-fidelity analysis results. The high-fidelity analysis results and the Kriging method were used to generate a surrogate model, from which the low-fidelity analysis results were interpolated. To reduce repeated calculations, three design points were simultaneously added for each calculation. The convergence of three design points was avoided by considering only the peak points as additional design points. The reliability of the final surrogate model was determined by applying the leave-one-out cross-validation method and by obtaining the cross-validation root mean square error. Using the multi-fidelity model developed in this study, a multi-fidelity aerodynamic database was constructed for use in the three degrees of freedom flight simulation of flight vehicles.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Aerospace Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Introduction;Multi-fidelity Surrogates;2022-11-08

2. Rapid Airfoil Inverse Design Method with a Deep Neural Network and Hyperparameter Selection;International Journal of Aeronautical and Space Sciences;2022-09-05

3. Improved VFM Method for High Accuracy Flight Simulation;Journal of the Korean Society for Aeronautical & Space Sciences;2021-09-30

4. Efficient multi-response adaptive sampling algorithm for construction of variable-fidelity aerodynamic tables;Chinese Journal of Aeronautics;2019-03

5. Development of Advanced Aerodynamic Data Fusion Techniques for Flight Simulation Database Construction;2018 Modeling and Simulation Technologies Conference;2018-06-24

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