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13 December 2024 Research on supersonic flow field reconstruction using physics-informed neural networks
Tingxuan Fan, Ying Huai
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Proceedings Volume 13492, AOPC 2024: Laser Technology and Applications; 1349216 (2024) https://doi.org/10.1117/12.3047858
Event: Applied Optics and Photonics China 2024 (AOPC2024), 2024, Beijing, China
Abstract
The shock wave system in supersonic flow fields is a critical factor influencing the quality of chemical laser beams. This study develops a comprehensive interpretation technique for supersonic flow field information by utilizing a deep learning method incorporating physical explanations. A compressible flow analysis framework of Physics-Informed Neural Networks (PINNs) is established for supersonic jets with a maximum Mach number of 10, combined with Planar Laser-Induced Fluorescence (PLIF) technology. This approach enables the reconstruction of velocity and temperature fields from concentration field measurement data. The proposed method achieves high accuracy in reconstructing data in the shock wave region, with relative L2 errors of 20.56% and 23.78% verified by Computational Fluid Dynamics (CFD) data. Experimental demonstrations showcase the ability of this method to reconstruct measured shock waves, providing an effective technical means for the comprehensive analysis of supersonic shock wave characteristics.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tingxuan Fan and Ying Huai "Research on supersonic flow field reconstruction using physics-informed neural networks", Proc. SPIE 13492, AOPC 2024: Laser Technology and Applications, 1349216 (13 December 2024); https://doi.org/10.1117/12.3047858
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KEYWORDS
Neural networks
Laser induced fluorescence
Chemical lasers
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