A generative deep learning approach for real-time prediction of hypersonic vehicles in fluid-thermo-structural coupling fields-Reference-Cited by-同舟云学术

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A generative deep learning approach for real-time prediction of hypersonic vehicles in fluid-thermo-structural coupling fields

Published:2023-08 Issue: Volume:139 Page:108398
ISSN:1270-9638
Container-title:Aerospace Science and Technology
language:en
Short-container-title:Aerospace Science and Technology

Author:

Li Ni,Liu Yunqin,Gong Guanghong,Zhao Luming,Yuan Haitao

Funder

Natural Science Foundation of Beijing Municipality

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Aerospace Engineering

Reference26 articles.

1. Control-relevant modeling and performance limitation analysis for flexible air-breathing hypersonic vehicles;Shen;Aerosp. Sci. Technol.,2018

2. Fluid-structure coupled analysis of tandem 2d elastic panels;Zhou;Aerosp. Sci. Technol.,2021

3. Effects of the aerothermoelastic deformation on the performance of the three-dimensional hypersonic inlet;Ye;Aerosp. Sci. Technol.,2019

4. Non-intrusive reduced-order model for predicting transonic flow with varying geometries;Sun;Chin. J. Aeronaut.,2020

5. Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids;Han;Chin. J. Aeronaut.,2019

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