| DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data | |
Lv JQ(吕家琦); Hong QZ(洪启臻) ; Wang XY(王小永); Mao, Zhiping; Sun QH(孙泉华)
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| Source Publication | JOURNAL OF COMPUTATIONAL PHYSICS
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| 2023-10-15 | |
| Volume | 491Pages:112344 |
| ISSN | 0021-9991 |
| Abstract | The hypersonic flow is in a thermochemical nonequilibrium state due to the high temperature caused by the strong shock compression. In a thermochemical nonequilibrium flow, the distribution of molecular internal energy levels strongly deviates from the equilibrium distribution (i.e., the Boltzmann distribution). It is intractable to directly obtain the microscopic nonequilibrium distribution from existed experimental measurements usually described by macroscopic field variables such as temperature or velocity. Motivated by the idea of deep multi-scale multi-physics neural network (DeepMMNet) proposed in [1], we develop in this paper a data assimilation framework called DeepStSNet to accurately reconstruct the quantum state-resolved thermochemical nonequilibrium flowfield by using sparse experimental measurements of vibrational temperature and pre trained deep neural operator networks (DeepONets). In particular, we first construct several DeepONets to express the coupled dynamics between field variables in the thermochemical nonequilibrium flow and to approximate the state-to-state (StS) approach, which traces the variation of each vibrational level of molecule accurately. These proposed DeepONets are then trained by using the numerical simulation data, and would later be served as building blocks for the DeepStSNet. We demonstrate the effectiveness and accuracy of DeepONets with different test cases showing that the density and energy of vibrational groups as well as the temperature and velocity fields are predicted with high accuracy. We then extend the architectures of DeepMMNet by considering a simplified thermochemical nonequilibrium model, i.e., the 2T model, showing that the entire thermochemical nonequilibrium flowfield is well predicted by using scattered measurements of full or even partial field variables. We next consider a more accurate and complex thermochemical nonequilibrium model, i.e., the StS-CGM model, and develop a DeepStSNet for this model. In this case, we employ the coarse-grained method, which divides the vibrational levels into groups (vibrational bins), to alleviate the computational cost for the StS approach in order to achieve a fast but reliable prediction with DeepStSNet. We test the present DeepStSNet framework with sparse numerical simulation data showing that the predictions are in excellent agreement with the reference data for test cases. We further employ the DeepStSNet to assimilate a few experimental measurements of vibrational temperature obtained from the shock tube experiment, and the detailed non-Boltzmann vibrational distribution of molecule oxygen is reconstructed by using the sparse experimental data for the first time. Moreover, by considering the inevitable uncertainty in the experimental data, an average strategy in the predicting procedure is proposed to obtain the most probable predicted fields. The present DeepStSNet is general and robust, and can be applied to build a bridge from sparse measurements of macroscopic field variables to a microscopic quantum state-resolved flowfield. This kind of reconstruction is beneficial for exploiting the experimental measurements and uncovering the hidden physicochemical processes in hypersonic flows. & COPY; 2023 Elsevier Inc. All rights reserved. |
| Keyword | Hypersonic Thermochemical nonequilibrium State-to-state approach Deep learning Multiphysics Data assimilation |
| DOI | 10.1016/j.jcp.2023.112344 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001051274200001 |
| WOS Research Area | Computer Science ; Physics |
| WOS Subject | Computer Science, Interdisciplinary Applications ; Physics, Mathematical |
| Funding Organization | National Key Ramp ; D Program of China [2022YFA1004500] ; Strategic Priority Research Program of Chinese Academy of Sciences [XDA17030100] ; China Postdoctoral Science Foundation [2022M723233] ; National Natural Science Foundation of China [12171404] |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | Hong, QZ (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China. ; Mao, ZP (corresponding author), Xiamen Univ, Sch Math Sci, Fujian Prov Key Lab Math Modeling & High Performan, Xiamen 361005, Peoples R China. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/92644 |
| Collection | 高温气体动力学国家重点实验室 |
| Affiliation | 1.{Lv Jiaqi, Hong Qizhen, Wang Xiaoyong, Sun Quanhua} Chinese Acad Sci Inst Mech State Key Lab High Temp Gas Dynam Beijing 100190 Peoples R China 2.{Lv Jiaqi, Sun Quanhua} Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China 3.{Mao Zhiping} Xiamen Univ Sch Math Sci Fujian Prov Key Lab Math Modeling & High Performan Xiamen 361005 Peoples R China |
| Recommended Citation GB/T 7714 | Lv JQ,Hong QZ,Wang XY,et al. DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2023,491:112344.Rp_Au:Hong, QZ (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China., Mao, ZP (corresponding author), Xiamen Univ, Sch Math Sci, Fujian Prov Key Lab Math Modeling & High Performan, Xiamen 361005, Peoples R China. |
| APA | 吕家琦,洪启臻,王小永,Mao, Zhiping,&孙泉华.(2023).DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data.JOURNAL OF COMPUTATIONAL PHYSICS,491,112344. |
| MLA | 吕家琦,et al."DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data".JOURNAL OF COMPUTATIONAL PHYSICS 491(2023):112344. |
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