| Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation | |
Zhao QY(赵庆义)1,2; Jin GD(晋国栋)1,2 ; Zhou ZD(周志登)1,2
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| Corresponding Author | Jin, Guodong([email protected]) |
| Source Publication | AIP ADVANCES
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| 2022-12-01 | |
| Volume | 12Issue:12Pages:9 |
| Abstract | A super-resolution reconstruction model for the subgrid scale (SGS) turbulent flow field in large-eddy simulation (LES) is proposed, and it is called the meta-learning deep convolutional neural network (MLDCNN). Direct numerical simulation (DNS) data of isotropic turbulence are used as the dataset of the model. The MLDCNN is an unsupervised learning model, which only includes high-resolution DNS data without manually inputting preprocessed low-resolution data. In this model, the training process adopts the meta-learning method. First, in the a priori test, the SGS turbulent flow motions in the filtered DNS (FDNS) flow field are reconstructed, and the energy spectrum and probability density function of the velocity gradient of the DNS flow field are reconstructed with high accuracy. Then, in the a posteriori test, the super-resolution reconstruction of the LES flow field is carried out. The difficulty of LES flow field reconstruction is that it contains filtering loss and subgrid model errors relative to the DNS flow field. The super-resolution reconstruction of the LES flow field achieves good results through this unsupervised learning model. The proposed model makes a good prediction of small-scale motions in the LES flow field. This work improves the prediction accuracy of LES, which is crucial for the phenomena dominated by small-scale motions, such as relative motions of particles suspended in turbulent flows. (c) 2022 Author(s). |
| DOI | 10.1063/5.0127808 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000894508800006 |
| WOS Keyword | CIRCULAR-CYLINDER ; TURBULENCE ; FLOW ; DECONVOLUTION ; NETWORK ; MODEL |
| WOS Research Area | Science & Technology - Other Topics ; Materials Science ; Physics |
| WOS Subject | Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied |
| Funding Project | NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[11988102] ; NSFC Program[12272380] ; National Key Project[GJXM92579] |
| Funding Organization | NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; NSFC Program ; National Key Project |
| Classification | Q3 |
| Ranking | 1 |
| Contributor | Jin, Guodong |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/91342 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Zhao QY,Jin GD,Zhou ZD. Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation[J]. AIP ADVANCES,2022,12,12,:9.Rp_Au:Jin, Guodong |
| APA | 赵庆义,晋国栋,&周志登.(2022).Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation.AIP ADVANCES,12(12),9. |
| MLA | 赵庆义,et al."Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation".AIP ADVANCES 12.12(2022):9. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2022FA022.pdf（10288KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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