Predicting continuum breakdown with deep neural networks
文献类型：期刊论文
| 作者 | Xiao TB(肖天白); Schotthoefer, Steffen; Frank, Martin |
| 刊名 | JOURNAL OF COMPUTATIONAL PHYSICS
 |
| 出版日期 | 2023-09-15 |
| 卷号 | 489页码:112278 |
| 关键词 | Computational fluid dynamics Kinetic theory Boltzmann equation Multi-scale method Deep learning |
| ISSN号 | 0021-9991 |
| DOI | 10.1016/j.jcp.2023.112278 |
| 英文摘要 | The multi-scale nature of gaseous flows poses tremendous difficulties for theoretical and numerical analysis. The Boltzmann equation, while possessing a wider applicability than hydrodynamic equations, requires significantly more computational resources due to the increased degrees of freedom in the model. The success of a hybrid fluid-kinetic flow solver for the study of multi-scale flows relies on accurate prediction of flow regimes. In this paper, we draw on binary classification in machine learning and propose the first neural network classifier to detect near-equilibrium and non-equilibrium flow regimes based on local flow conditions. Compared with classical semi-empirical criteria of continuum breakdown, the current method provides a data-driven alternative where the parameterized implicit function is trained by solutions of the Boltzmann equation. The ground-truth labels are derived rigorously from the deviation of particle distribution functions and the approximations based on the Chapman-Enskog ansatz. Therefore, no tunable parameter is needed in the criterion. Following the entropy closure of the Boltzmann moment system, a data generation strategy is developed to produce training and test sets. Numerical analysis shows its superiority over simulation-based samplings. A hybrid Boltzmann-Navier-Stokes flow solver is built correspondingly with an adaptive partition of local flow regimes. Numerical experiments including the one-dimensional Riemann problem, shear flow layer, and hypersonic flow around a circular cylinder are presented to validate the current scheme for simulating cross-scale and non-equilibrium flow physics. The quantitative comparison with a semi-empirical criterion and benchmark results demonstrates the capability of the current neural classifier to accurately predict continuum breakdown. The code for the data generator, hybrid solver, and neural network implementation is available in the open source repositories [1,2].& COPY; 2023 Elsevier Inc. All rights reserved. |
| 分类号 | 一类/力学重要期刊 |
| WOS研究方向 | Computer Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001032966700001 |
| 其他责任者 | Xiao, TB (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing, Peoples R China. |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/92555]  |
| 专题 | 力学研究所_高温气体动力学国家重点实验室 |
| 作者单位 | 1.{Schotthoefer, Steffen, Frank, Martin} Karlsruhe Inst Technol, Steinbuch Ctr Comp, Karlsruhe, Germany 2.{Xiao, Tianbai} Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiao TB,Schotthoefer, Steffen,Frank, Martin. Predicting continuum breakdown with deep neural networks[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2023,489:112278. |
| APA | 肖天白,Schotthoefer, Steffen,&Frank, Martin.(2023).Predicting continuum breakdown with deep neural networks.JOURNAL OF COMPUTATIONAL PHYSICS,489,112278. |
| MLA | 肖天白,et al."Predicting continuum breakdown with deep neural networks".JOURNAL OF COMPUTATIONAL PHYSICS 489(2023):112278. |
入库方式： OAI收割
来源：力学研究所
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