Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic Processes
Engineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries, and acquiring the temperature field and its evolution is a prerequisite for efficient thermal management. By harnessing the sensing data from sensor networks, an unpreced...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/10/2108 |
| _version_ | 1828839216902045696 |
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| author | Di Wang Xi Zhang |
| author_facet | Di Wang Xi Zhang |
| author_sort | Di Wang |
| collection | DOAJ |
| description | Engineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries, and acquiring the temperature field and its evolution is a prerequisite for efficient thermal management. By harnessing the sensing data from sensor networks, an unprecedented opportunity has emerged for an accurate estimation of the temperature field. However, limited resources of sensor deployment and computation capacity pose a great challenge while modeling the spatiotemporal dynamics of the temperature field. This paper presents a novel temperature field estimation approach to describe the dynamics of a temperature field by combining a physics-specific model and a spatiotemporal Gaussian process. To reduce the computational burden while dealing with a large set of spatiotemporal data, we employ a tapering covariance function and develop an associated parameter estimation procedure. We introduce a case study of grain storage to show the effectiveness and efficiency of the proposed approach. |
| first_indexed | 2024-12-12T19:18:50Z |
| format | Article |
| id | doaj.art-0a4406acbcd946d0b03e48caed309d61 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T19:18:50Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0a4406acbcd946d0b03e48caed309d612022-12-22T00:14:39ZengMDPI AGApplied Sciences2076-34172019-05-01910210810.3390/app9102108app9102108Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic ProcessesDi Wang0Xi Zhang1Department of Industrial Engineering and Management, Peking University, Beijing 100871, ChinaDepartment of Industrial Engineering and Management, Peking University, Beijing 100871, ChinaEngineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries, and acquiring the temperature field and its evolution is a prerequisite for efficient thermal management. By harnessing the sensing data from sensor networks, an unprecedented opportunity has emerged for an accurate estimation of the temperature field. However, limited resources of sensor deployment and computation capacity pose a great challenge while modeling the spatiotemporal dynamics of the temperature field. This paper presents a novel temperature field estimation approach to describe the dynamics of a temperature field by combining a physics-specific model and a spatiotemporal Gaussian process. To reduce the computational burden while dealing with a large set of spatiotemporal data, we employ a tapering covariance function and develop an associated parameter estimation procedure. We introduce a case study of grain storage to show the effectiveness and efficiency of the proposed approach.https://www.mdpi.com/2076-3417/9/10/21083D spatiotemporal fieldsensor networksphysics-specific modeltapering covariance function |
| spellingShingle | Di Wang Xi Zhang Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic Processes Applied Sciences 3D spatiotemporal field sensor networks physics-specific model tapering covariance function |
| title | Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic Processes |
| title_full | Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic Processes |
| title_fullStr | Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic Processes |
| title_full_unstemmed | Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic Processes |
| title_short | Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic Processes |
| title_sort | modeling of a 3d temperature field by integrating a physics specific model and spatiotemporal stochastic processes |
| topic | 3D spatiotemporal field sensor networks physics-specific model tapering covariance function |
| url | https://www.mdpi.com/2076-3417/9/10/2108 |
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