A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln
The push-plate kiln is a kind of kiln equipment widely used in the oxygen-free sintering of high-temperature alloy materials. Its flow field monitoring has an important application value for the manufacturing industry. However, traditional simulation methods cannot meet the requirements of real-time...
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MDPI AG
2023-01-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/2/51 |
| _version_ | 1797620883548798976 |
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| author | Pin Wu Lulu Ji Wenyan Yuan Zhitao Liu Tiantian Tang |
| author_facet | Pin Wu Lulu Ji Wenyan Yuan Zhitao Liu Tiantian Tang |
| author_sort | Pin Wu |
| collection | DOAJ |
| description | The push-plate kiln is a kind of kiln equipment widely used in the oxygen-free sintering of high-temperature alloy materials. Its flow field monitoring has an important application value for the manufacturing industry. However, traditional simulation methods cannot meet the requirements of real-time applications due to the high computational cost and being time-consuming. The rapid development of artificial intelligence technology will empower the traditional manufacturing industry. In this paper, we propose a data-driven digital twin framework for real-time flow field prediction by combining the CFD modeling simulation, IoT, and deep learning technologies. The framework integrates geometric, rule, physical, and neural network models to achieve the real-time simulation of physical and twin objects. The proper orthogonal decomposition (POD) and multiscale convolutional neural network (MCNN) are innovatively embedded into the framework. The POD is used to map high-dimensional data to low-dimensional features, and the MCNN is used to construct models predicting low-dimensional features for fast flow field prediction. The effectiveness of the proposed model is verified by the push-plate kiln case. The results show that the digital twin can quickly predict multi-physics fields based on the perceptual data to achieve the real-time evaluation of the operating state of the push-plate kiln. |
| first_indexed | 2024-03-11T08:47:55Z |
| format | Article |
| id | doaj.art-68d6e0b23f6d42768bf0e2fc36db43a2 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-11T08:47:55Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-68d6e0b23f6d42768bf0e2fc36db43a22023-11-16T20:37:36ZengMDPI AGFuture Internet1999-59032023-01-011525110.3390/fi15020051A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate KilnPin Wu0Lulu Ji1Wenyan Yuan2Zhitao Liu3Tiantian Tang4School of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaChengdu Xingyun Zhilian Technology Co. Ltd., Chengdu 610000, ChinaThe push-plate kiln is a kind of kiln equipment widely used in the oxygen-free sintering of high-temperature alloy materials. Its flow field monitoring has an important application value for the manufacturing industry. However, traditional simulation methods cannot meet the requirements of real-time applications due to the high computational cost and being time-consuming. The rapid development of artificial intelligence technology will empower the traditional manufacturing industry. In this paper, we propose a data-driven digital twin framework for real-time flow field prediction by combining the CFD modeling simulation, IoT, and deep learning technologies. The framework integrates geometric, rule, physical, and neural network models to achieve the real-time simulation of physical and twin objects. The proper orthogonal decomposition (POD) and multiscale convolutional neural network (MCNN) are innovatively embedded into the framework. The POD is used to map high-dimensional data to low-dimensional features, and the MCNN is used to construct models predicting low-dimensional features for fast flow field prediction. The effectiveness of the proposed model is verified by the push-plate kiln case. The results show that the digital twin can quickly predict multi-physics fields based on the perceptual data to achieve the real-time evaluation of the operating state of the push-plate kiln.https://www.mdpi.com/1999-5903/15/2/51proper orthogonal decompositionconvolutional neural networkcomputational fluid dynamicsdigital twin |
| spellingShingle | Pin Wu Lulu Ji Wenyan Yuan Zhitao Liu Tiantian Tang A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln Future Internet proper orthogonal decomposition convolutional neural network computational fluid dynamics digital twin |
| title | A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln |
| title_full | A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln |
| title_fullStr | A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln |
| title_full_unstemmed | A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln |
| title_short | A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln |
| title_sort | digital twin framework embedded with pod and neural network for flow field monitoring of push plate kiln |
| topic | proper orthogonal decomposition convolutional neural network computational fluid dynamics digital twin |
| url | https://www.mdpi.com/1999-5903/15/2/51 |
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