Correlation Coefficient Filtering Method for Optimal Sensor Layout Strategies in Inverse Problems
The gappy POD method is described as an inverse problem-solving technique based on proper orthogonal decomposition (POD). It is used to reconstruct global information with minimal measurement points and has applications in fields such as heat transfer and fluid dynamics. The accuracy and stability o...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/je/5142118 |
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| _version_ | 1825199981085065216 |
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| author | Qingyang Yuan Xiangji Guo Jiajie Han Bingyue Han Bo Zhang Tian Lan |
| author_facet | Qingyang Yuan Xiangji Guo Jiajie Han Bingyue Han Bo Zhang Tian Lan |
| author_sort | Qingyang Yuan |
| collection | DOAJ |
| description | The gappy POD method is described as an inverse problem-solving technique based on proper orthogonal decomposition (POD). It is used to reconstruct global information with minimal measurement points and has applications in fields such as heat transfer and fluid dynamics. The accuracy and stability of the gappy POD method depend heavily on sensor number and placement. The paper proposes a correlation coefficient filtering method to optimize sensor layout, addressing the inefficiencies of traditional methods reliant on matrix condition numbers. The method’s effectiveness is tested using the one-dimensional Burgers’ equation and later the two-dimensional lid-driven cavity flow. The paper introduces two key indicators: expected coefficient of correlation (α) and remaining points (RPs). Finally, the study explores the impact of database sparsity and demonstrates the importance of this method in determining optimal sensor placement for future engineering applications. The study reflects the significant value of the correlation coefficient filtering method, based on the assumption of global correlation maximization, in evaluating optimal measurement points of the system, laying a foundation for the subsequent application of this algorithm in engineering. |
| format | Article |
| id | doaj-art-c5a7e1a77e334fcf987e673f1c27520e |
| institution | Kabale University |
| issn | 2314-4912 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-c5a7e1a77e334fcf987e673f1c27520e2025-02-08T00:00:01ZengWileyJournal of Engineering2314-49122025-01-01202510.1155/je/5142118Correlation Coefficient Filtering Method for Optimal Sensor Layout Strategies in Inverse ProblemsQingyang Yuan0Xiangji Guo1Jiajie Han2Bingyue Han3Bo Zhang4Tian Lan5School of Energy and Power EngineeringSchool of Energy and Power EngineeringSchool of Energy and Power EngineeringSchool of Energy and Power EngineeringSchool of Energy and Power EngineeringZhejiang SHIP Electronics Technology Co., Ltd.The gappy POD method is described as an inverse problem-solving technique based on proper orthogonal decomposition (POD). It is used to reconstruct global information with minimal measurement points and has applications in fields such as heat transfer and fluid dynamics. The accuracy and stability of the gappy POD method depend heavily on sensor number and placement. The paper proposes a correlation coefficient filtering method to optimize sensor layout, addressing the inefficiencies of traditional methods reliant on matrix condition numbers. The method’s effectiveness is tested using the one-dimensional Burgers’ equation and later the two-dimensional lid-driven cavity flow. The paper introduces two key indicators: expected coefficient of correlation (α) and remaining points (RPs). Finally, the study explores the impact of database sparsity and demonstrates the importance of this method in determining optimal sensor placement for future engineering applications. The study reflects the significant value of the correlation coefficient filtering method, based on the assumption of global correlation maximization, in evaluating optimal measurement points of the system, laying a foundation for the subsequent application of this algorithm in engineering.http://dx.doi.org/10.1155/je/5142118 |
| spellingShingle | Qingyang Yuan Xiangji Guo Jiajie Han Bingyue Han Bo Zhang Tian Lan Correlation Coefficient Filtering Method for Optimal Sensor Layout Strategies in Inverse Problems Journal of Engineering |
| title | Correlation Coefficient Filtering Method for Optimal Sensor Layout Strategies in Inverse Problems |
| title_full | Correlation Coefficient Filtering Method for Optimal Sensor Layout Strategies in Inverse Problems |
| title_fullStr | Correlation Coefficient Filtering Method for Optimal Sensor Layout Strategies in Inverse Problems |
| title_full_unstemmed | Correlation Coefficient Filtering Method for Optimal Sensor Layout Strategies in Inverse Problems |
| title_short | Correlation Coefficient Filtering Method for Optimal Sensor Layout Strategies in Inverse Problems |
| title_sort | correlation coefficient filtering method for optimal sensor layout strategies in inverse problems |
| url | http://dx.doi.org/10.1155/je/5142118 |
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