DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data-Reference-Cited by-同舟云学术

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DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data

Published:2023-10 Issue: Volume:491 Page:112344
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Lv Jiaqi,Hong Qizhen^ORCID,Wang Xiaoyong,Mao Zhiping,Sun Quanhua

Funder

Chinese Academy of Sciences

China Postdoctoral Science Foundation

Postdoctoral Research Foundation of China

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference47 articles.

1. DeepM&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators;Mao;J. Comput. Phys.,2021

2. Rate effects in hypersonic flows;Candler;Annu. Rev. Fluid Mech.,2019

3. Assessment of two-temperature kinetic model for ionizing air;Park;J. Thermophys. Heat Transf.,1989

4. Nonequilibrium Hypersonic Aerothermodynamics;Park,1990

5. Chemical relaxation with preferential dissociation from excited vibrational levels;Marrone;Phys. Fluids,1963

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