Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model
Considering the importance of combustion diagnosis in industrial manufacturing and many fields, efficient, quick, and real-time multidimensional reconstruction is necessary and indispensable. Hence, focusing on the combustion field dynamic and multi-dimensional reconstruction, a modified U-ConvLSTM...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10457001/ |
| _version_ | 1797289059962322944 |
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| author | Xiaodong Huang Xiaojian Hao Baowu Pan Shaogang Chen Shenxiang Feng Pan Pei |
| author_facet | Xiaodong Huang Xiaojian Hao Baowu Pan Shaogang Chen Shenxiang Feng Pan Pei |
| author_sort | Xiaodong Huang |
| collection | DOAJ |
| description | Considering the importance of combustion diagnosis in industrial manufacturing and many fields, efficient, quick, and real-time multidimensional reconstruction is necessary and indispensable. Hence, focusing on the combustion field dynamic and multi-dimensional reconstruction, a modified U-ConvLSTM model was proposed to combine with the TDLAS method to resolve the real-time reconstruction and short prediction. By dividing the combustion field into space and time slices, we used discretized spatiotemporal slices to complete the 2-D distribution reconstruction and then expanded them into higher dimensions. The simulation results demonstrate that our design can effectively reconstruct different 2-D distributions, achieving a reconstruction error of less than 5%. Three-step predictions also performed well, a PSNR no less than 30 dB, and an SSIM no less than 0.75. In general, our multidimensional combustion field reconstruction method, based on the spatiotemporal discretization U-ConvLSTM model, can enhance the accuracy of combustion field reconstruction and provide short-term predictions. This work will contribute to closed-loop control in industrial fields. |
| first_indexed | 2024-03-07T18:58:45Z |
| format | Article |
| id | doaj.art-bf412369c1bc433eac4dda68b7688d46 |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-03-07T18:58:45Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-bf412369c1bc433eac4dda68b7688d462024-03-02T00:00:12ZengIEEEIEEE Photonics Journal1943-06552024-01-0116211010.1109/JPHOT.2024.336642510457001Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM ModelXiaodong Huang0https://orcid.org/0000-0003-2018-8664Xiaojian Hao1https://orcid.org/0000-0003-0295-701XBaowu Pan2https://orcid.org/0009-0001-6011-7118Shaogang Chen3https://orcid.org/0000-0002-8147-7411Shenxiang Feng4https://orcid.org/0009-0002-1510-1499Pan Pei5https://orcid.org/0000-0002-6677-0409State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, ChinaConsidering the importance of combustion diagnosis in industrial manufacturing and many fields, efficient, quick, and real-time multidimensional reconstruction is necessary and indispensable. Hence, focusing on the combustion field dynamic and multi-dimensional reconstruction, a modified U-ConvLSTM model was proposed to combine with the TDLAS method to resolve the real-time reconstruction and short prediction. By dividing the combustion field into space and time slices, we used discretized spatiotemporal slices to complete the 2-D distribution reconstruction and then expanded them into higher dimensions. The simulation results demonstrate that our design can effectively reconstruct different 2-D distributions, achieving a reconstruction error of less than 5%. Three-step predictions also performed well, a PSNR no less than 30 dB, and an SSIM no less than 0.75. In general, our multidimensional combustion field reconstruction method, based on the spatiotemporal discretization U-ConvLSTM model, can enhance the accuracy of combustion field reconstruction and provide short-term predictions. This work will contribute to closed-loop control in industrial fields.https://ieeexplore.ieee.org/document/10457001/Tunable diode laser absorption spectroscopy (TDLAS)U-model convolutional long short-term memory (U-ConvLSTM)combustion sitereconstructionpredicted |
| spellingShingle | Xiaodong Huang Xiaojian Hao Baowu Pan Shaogang Chen Shenxiang Feng Pan Pei Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model IEEE Photonics Journal Tunable diode laser absorption spectroscopy (TDLAS) U-model convolutional long short-term memory (U-ConvLSTM) combustion site reconstruction predicted |
| title | Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model |
| title_full | Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model |
| title_fullStr | Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model |
| title_full_unstemmed | Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model |
| title_short | Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model |
| title_sort | combustion field prediction and diagnosis via spatiotemporal discrete u convlstm model |
| topic | Tunable diode laser absorption spectroscopy (TDLAS) U-model convolutional long short-term memory (U-ConvLSTM) combustion site reconstruction predicted |
| url | https://ieeexplore.ieee.org/document/10457001/ |
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