中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 力学研究所
  3. 中国科学院力学研究所
  4. 流固耦合系统力学重点实验室(2012-)
Physics-informed dynamic mode decomposition for reconstruction and prediction of dense particulate pipe flows

文献类型：期刊论文

作者Zhang,Zhen3,4,5; Qin,ZeJun3; Huo,Jing3; Zhang Y(张岩)1,2; Liu,QingKuan3,4,5
刊名PHYSICS OF FLUIDS
出版日期2024-11-01
卷号36期号:11页码:17
ISSN号1070-6631
DOI10.1063/5.0240839
通讯作者Zhang, Yan(zhangyan162@imech.ac.cn)
英文摘要Dynamic mode decomposition (DMD) effectively captures the growth and frequency characteristics of individual modes, enabling the construction of reduced-order models for flow evolution, thereby facilitating the prediction of fluid dynamic behavior. However, DMD's predictive accuracy is inherently constrained by its inability to inherently incorporate physical principles. Therefore, for dense particulate pipe flows with complex flow mechanisms, we introduce a physics-informed dynamic mode decomposition (PIDMD) approach, which augments the purely data-driven DMD framework by incorporating the conservation of mass as a constraint. This ensures that the extracted dynamic modes adhere to known physical principles. Initially, we apply the DMD to reconstruct and predict the velocity field, comparing the results against benchmark computational fluid dynamics-discrete element method (CFD-DEM) simulations. Findings indicate that while DMD can reconstruct the flow field simulated by CFD-DEM and provide predictions of future flow states, its predictive accuracy gradually deteriorates over time. Next, we utilize both PIDMD and DMD to reconstruct and predict particle volume fraction, evaluating both models based on CFD-DEM outcomes. The results indicate that both PIDMD and DMD can predict particle aggregation toward the center, but PIDMD provides more accurate predictions regarding the size of particle aggregations and their distribution near the tube wall. Furthermore, the average prediction error for particle volume fraction using PIDMD is 6.54%, which is lower than the error of 13.49% obtained by DMD. Both qualitative and quantitative comparisons highlight the superior predictive capability of PIDMD. The methodology developed in this study provides valuable insights for high-precision predictions of particulate flows.
分类号一类/力学重要期刊
WOS关键词SIMULATION
资助项目National Natural Science Foundation of China10.13039/501100001809[12202291] ; National Natural Science Foundation of China10.13039/501100001809[12302516] ; National Natural Science Foundation of China10.13039/501100001809[12132018] ; National Natural Science Foundation of China[BJK2024177] ; Science and Technology Project of Hebei Education Department[2024T170956] ; China Postdoctoral Science Foundation[GKZD010089] ; State Key Laboratory of Ocean Engineering (Shanghai Jiao Tong University)[E2022210078] ; Innovation Research Group Project of Natural Science Foundation of Hebei Province of China
WOS研究方向Mechanics ; Physics
语种英语
WOS记录号WOS:001364205000007
资助机构National Natural Science Foundation of China10.13039/501100001809 ; National Natural Science Foundation of China ; Science and Technology Project of Hebei Education Department ; China Postdoctoral Science Foundation ; State Key Laboratory of Ocean Engineering (Shanghai Jiao Tong University) ; Innovation Research Group Project of Natural Science Foundation of Hebei Province of China
其他责任者Zhang, Yan
源URL[http://dspace.imech.ac.cn/handle/311007/97565]  
专题力学研究所_流固耦合系统力学重点实验室(2012-)
作者单位1.Shanghai Jiao Tong Univ, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China
2.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China;
3.Shijiazhuang Tiedao Univ, Sch Civil Engn, Shijiazhuang 050043, Peoples R China;
4.Innovat Ctr Wind Engn & Wind Energy Technol Hebei, Shijiazhuang 050043, Peoples R China;
5.Shijiazhuang Tiedao Univ, Key Lab Rd & Railway Engn Safety Control, Minist Educ, Shijiazhuang 050043, Peoples R China;
推荐引用方式
GB/T 7714		 Zhang,Zhen,Qin,ZeJun,Huo,Jing,et al. Physics-informed dynamic mode decomposition for reconstruction and prediction of dense particulate pipe flows[J]. PHYSICS OF FLUIDS,2024,36(11):17.
APA Zhang,Zhen,Qin,ZeJun,Huo,Jing,张岩,&Liu,QingKuan.(2024).Physics-informed dynamic mode decomposition for reconstruction and prediction of dense particulate pipe flows.PHYSICS OF FLUIDS,36(11),17.
MLA Zhang,Zhen,et al."Physics-informed dynamic mode decomposition for reconstruction and prediction of dense particulate pipe flows".PHYSICS OF FLUIDS 36.11(2024):17.

入库方式： OAI收割

来源：力学研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。