A compressed sensing based framework for surface pressure field reconstruction from sparse measurement
文献类型：期刊论文
| 作者 | Qiu W(邱伟)2,3; Sun ZX(孙振旭)2; Wang, Junxiang2,3; Bai, Ye4,5; Yao, Shuanbao1; Guo DL(郭迪龙)2; Yang GW(杨国伟)2 |
| 刊名 | PHYSICS OF FLUIDS
 |
| 出版日期 | 2025-04-01 |
| 卷号 | 37期号:4页码:17 |
| ISSN号 | 1070-6631 |
| DOI | 10.1063/5.0264594 |
| 通讯作者 | Sun, Zhenxu(sunzhenxu@imech.ac.cn) |
| 英文摘要 | Traditional experimental methods usually measure the aerodynamic load characteristics of an object by deploying a large number of pressure sensors on its surface, which are often challenging to economically and efficiently obtain accurate surface pressure distribution due to limitation imposed by experimental space, the complexity of geometry, and the cost of measurement instruments. To address this, a compressed sensing (CS) based framework has been proposed in this paper to investigate the reconstruction of the original surface pressure field from extremely sparse measurement data. The proposed framework integrates the generalized proper orthogonal decomposition method for flow field dimensionality reduction, the CS technique for accurate reconstruction of the original signal, and the improved particle swarm optimization algorithm for optimizing sensor placement strategies. Unlike image and unsteady flow field reconstructions, the method presented in this paper has been successful in reconstructing surface pressure fields of high-speed train under various conditions. Based on the accurate reconstruction of surface pressure fields, further aerodynamic load data can be obtained. Additionally, this paper optimizes the traditional pressure sensor layout using a particle swarm optimization method, which not only improves reconstruction accuracy but also significantly reduces the deployment of redundant sensors. Moreover, traditional point selection strategies based on experience can still be incorporated into the pressure sensor layout scheme and effectively reduce reconstruction errors under crosswind conditions. Comparison of the results showed that the proposed framework can accurately and efficiently reconstruct the surface flow field of three-dimensional complex-shaped objects from sparse measurement. |
| 分类号 | 一类/力学重要期刊 |
| WOS关键词 | RESTRICTED ISOMETRY PROPERTY ; SIGNAL RECOVERY ; SUPERRESOLUTION RECONSTRUCTION ; DECOMPOSITION ; FLOWS |
| 资助项目 | China Railway10.13039/100015860[N2024J040] ; China National Railway Group Science and Technology Program |
| WOS研究方向 | Mechanics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001466296700009 |
| 资助机构 | China Railway10.13039/100015860 ; China National Railway Group Science and Technology Program |
| 其他责任者 | 孙振旭 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/101073]  |
| 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.CRRC Qingdao Sifang CO Ltd, Qingdao 266111, Shandong, Peoples R China 2.Chinese Acad Sci, Key Lab Mech Fluid Solid Coupling Syst, Inst Mech, Beijing, Peoples R China; 3.Univ Chinese Acad Sci, UCAS, Beijing, Peoples R China; 4.China Acad Railway Sci, Locomot & Car Res Inst, Beijing 100081, Peoples R China; 5.Beijing Zongheng Electromech Technol Co Ltd, Beijing 100094, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Qiu W,Sun ZX,Wang, Junxiang,et al. A compressed sensing based framework for surface pressure field reconstruction from sparse measurement[J]. PHYSICS OF FLUIDS,2025,37(4):17. |
| APA | 邱伟.,孙振旭.,Wang, Junxiang.,Bai, Ye.,Yao, Shuanbao.,...&杨国伟.(2025).A compressed sensing based framework for surface pressure field reconstruction from sparse measurement.PHYSICS OF FLUIDS,37(4),17. |
| MLA | 邱伟,et al."A compressed sensing based framework for surface pressure field reconstruction from sparse measurement".PHYSICS OF FLUIDS 37.4(2025):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。