Combining direct and indirect sparse data for learning generalizable turbulence models

doi:10.1016/j.jcp.2023.112272

IMECH-IR > 非线性力学国家重点实验室

	Combining direct and indirect sparse data for learning generalizable turbulence models
	Zhang XL(张鑫磊); Xiao, Heng ; Luo, Xiaodong ; He GW(何国威)
发表期刊	JOURNAL OF COMPUTATIONAL PHYSICS
	2023-09-15
卷号	489 页码:112272
ISSN	0021-9991
摘要	Learning turbulence models from observation data is of significant interest in discovering a unified model for a broad range of practical flow applications. Either the direct observation of Reynolds stress or the indirect observation of velocity has been used to improve the predictive capacity of turbulence models. In this work, we propose combining the direct and indirect sparse data to train neural network-based turbulence models. The backpropagation technique and the observation augmentation approach are used to train turbulence models with different observation data in a unified ensemble-based framework. These two types of observation data can explore synergy to constrain the model training in different observation spaces, which enables learning generalizable models from very sparse data. The present method is tested in secondary flows in a square duct and separated flows over periodic hills. Both cases demonstrate that combining direct and indirect observations is able to improve the generalizability of the learned model in similar flow configurations, compared to using only indirect data. The ensemble-based method can serve as a practical tool for model learning from different types of observations due to its non-intrusive and derivative-free nature.
关键词	Ensemble Kalman method Turbulence modeling Direct data Indirect data
DOI	10.1016/j.jcp.2023.112272
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:001028900500001
WOS研究方向	Computer Science ; Physics
WOS类目	Computer Science, Interdisciplinary Applications ; Physics, Mathematical
项目资助者	NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics [11988102] ; National Natural Science Foundation of China [12102435] ; China Postdoctoral Science Foundation [2021M690154] ; Research Council of Norway [331644] ; National Centre for Sustainable Subsurface Utilization of the Norwegian Continental Shelf, Norway - Research Council of Norway ; [NCS2030]
论文分区	一类/力学重要期刊
力学所作者排名	1
RpAuthor	He, GW (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China. ; He, GW (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China. ; Xiao, H (corresponding author), Univ Stuttgart, Stuttgart Ctr Simulat Sci SC SimTech, D-70569 Stuttgart, Germany.
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/92554
专题	非线性力学国家重点实验室
作者单位	1.{Zhang, Xin-Lei, He, Guowei} Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China 2.{Zhang, Xin-Lei, He, Guowei} Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 3.{Xiao, Heng} Univ Stuttgart, Stuttgart Ctr Simulat Sci SC SimTech, D-70569 Stuttgart, Germany 4.{Luo, Xiaodong} Norwegian Res Ctr NORCE, N-5008 Bergen, Norway
推荐引用方式 GB/T 7714	Zhang XL,Xiao, Heng,Luo, Xiaodong,et al. Combining direct and indirect sparse data for learning generalizable turbulence models[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2023,489:112272.Rp_Au:He, GW (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China., He, GW (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China., Xiao, H (corresponding author), Univ Stuttgart, Stuttgart Ctr Simulat Sci SC SimTech, D-70569 Stuttgart, Germany.
APA	张鑫磊,Xiao, Heng,Luo, Xiaodong,&何国威.(2023).Combining direct and indirect sparse data for learning generalizable turbulence models.JOURNAL OF COMPUTATIONAL PHYSICS,489,112272.
MLA	张鑫磊,et al."Combining direct and indirect sparse data for learning generalizable turbulence models".JOURNAL OF COMPUTATIONAL PHYSICS 489(2023):112272.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Jp2023A245.pdf（7111KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载