Joint optimization of sensor placement and sparse pressure field reconstruction with a two-stage framework for limited data
文献类型：期刊论文
| 作者 | Zou, Junhong2; Qiu, Wei1; Sun ZX(孙振旭)1; Zhang, Xiaomei2; Zhang, Zhaoxiang2; Zhu, Xiangyu2; Lei, Zhen2 |
| 刊名 | PHYSICS OF FLUIDS
 |
| 出版日期 | 2025-07-01 |
| 卷号 | 37期号:7页码:14 |
| ISSN号 | 1070-6631 |
| DOI | 10.1063/5.0278268 |
| 通讯作者 | Sun, Zhenxu(sunzhenxu@imech.ac.cn) ; Zhu, Xiangyu(xiangyu.zhu@ia.ac.cn) |
| 英文摘要 | The pressure field on a high-speed train's surface is critical to aerodynamic design, underpinning the calculation of physical quantities, including lift, drag, lateral force, and overturning moment. However, due to space limitations and high experimental costs, only a limited number of pressure sensors can be installed on the train's surface, leading to sparse measurements and reduced accuracy. A common alternative is to use computational fluid dynamics (CFD) to generate simulated data for training machine learning models that identify optimal sensor locations and reconstruct full pressure fields from sparse measurements. While traditional methods like compressed sensing are widely used in prior work, neural-network-based methods remain underexplored, primarily due to the lack of suitable algorithms for sensor placement and the limited availability of CFD data. In this paper, we propose PROSNet (Pressure-field Reconstruction with Optimal Sensor placement), a neural network framework addressing both challenges. It includes a differentiable node selection module that learns optimal sensor placements and adopts a two-stage training strategy that encourages exploration of diverse sensor combinations, thereby learning a more generalizable representation of the pressure field. This strategy enhances the model's performance on unseen test data and mitigates overfitting. Quantitative results demonstrate that our model can accurately reconstruct the pressure field using only 2-8 sensors. Compared to the commonly used compressed sensing method, our approach largely reduces error on test data. These findings highlight the potential of neural networks as effective tools for recovering sparse pressure fields, offering clear advantages for the aerodynamic design of high-speed trains. |
| 分类号 | 一类/力学重要期刊 |
| 资助项目 | China Railway10.13039/100015860[XDA0480103] ; Strategic Priority Research Program of Chinese Academy of Sciences[2025ZD0122000] ; National Key R&D Program of China[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:001539407300013 |
| 资助机构 | China Railway10.13039/100015860 ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Key R&D Program of China ; China National Railway Group Science and Technology Program |
| 其他责任者 | 孙振旭，Zhu, Xiangyu |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/102325]  |
| 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.Chinese Acad Sci, Key Lab Mech Fluid Solid Coupling Syst, Inst Mech, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zou, Junhong,Qiu, Wei,Sun ZX,et al. Joint optimization of sensor placement and sparse pressure field reconstruction with a two-stage framework for limited data[J]. PHYSICS OF FLUIDS,2025,37(7):14. |
| APA | Zou, Junhong.,Qiu, Wei.,孙振旭.,Zhang, Xiaomei.,Zhang, Zhaoxiang.,...&Lei, Zhen.(2025).Joint optimization of sensor placement and sparse pressure field reconstruction with a two-stage framework for limited data.PHYSICS OF FLUIDS,37(7),14. |
| MLA | Zou, Junhong,et al."Joint optimization of sensor placement and sparse pressure field reconstruction with a two-stage framework for limited data".PHYSICS OF FLUIDS 37.7(2025):14. |
入库方式： OAI收割
来源：力学研究所
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