Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning
This paper presents the flow field reconstruction and prediction of powder fuel transport systems based on representative feature extraction from scattering images using deep learning techniques. A laboratory-built powder fuel supply system was used to conduct scattering spectroscopy experiments on...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4613 |
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| author | Hongyuan Du Zhen Cao Yingjie Song Jiangbo Peng Chaobo Yang Xin Yu |
| author_facet | Hongyuan Du Zhen Cao Yingjie Song Jiangbo Peng Chaobo Yang Xin Yu |
| author_sort | Hongyuan Du |
| collection | DOAJ |
| description | This paper presents the flow field reconstruction and prediction of powder fuel transport systems based on representative feature extraction from scattering images using deep learning techniques. A laboratory-built powder fuel supply system was used to conduct scattering spectroscopy experiments on boron-based fuel under various flow rate conditions. Based on the acquired scattering images, a prediction and reconstruction method was developed using a deep network framework composed of a Stacked Autoencoder (SAE), a Backpropagation Neural Network (BP), and a Long Short-Term Memory (LSTM) model. The proposed framework enables accurate classification and prediction of the dynamic evolution of flow structures based on learned representations from scattering images. Experimental results show that the feature vectors extracted by the SAE form clearly separable clusters in the latent space, leading to high classification accuracy under varying flow conditions. In the prediction task, the feature vectors predicted by the LSTM exhibit strong agreement with ground truth, with average mean square error, mean absolute error, and r-square values of 0.0027, 0.0398, and 0.9897, respectively. Furthermore, the reconstructed images offer a visual representation of the changing flow field, validating the model’s effectiveness in structure-level recovery. These results suggest that the proposed method provides reliable support for future real-time prediction of powder fuel mass flow rates based on optical sensing and imaging techniques. |
| format | Article |
| id | doaj-art-a0532b23c20e4bfc9c350fc3d997a0d1 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a0532b23c20e4bfc9c350fc3d997a0d12025-08-20T03:04:43ZengMDPI AGSensors1424-82202025-07-012515461310.3390/s25154613Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep LearningHongyuan Du0Zhen Cao1Yingjie Song2Jiangbo Peng3Chaobo Yang4Xin Yu5National Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, ChinaThis paper presents the flow field reconstruction and prediction of powder fuel transport systems based on representative feature extraction from scattering images using deep learning techniques. A laboratory-built powder fuel supply system was used to conduct scattering spectroscopy experiments on boron-based fuel under various flow rate conditions. Based on the acquired scattering images, a prediction and reconstruction method was developed using a deep network framework composed of a Stacked Autoencoder (SAE), a Backpropagation Neural Network (BP), and a Long Short-Term Memory (LSTM) model. The proposed framework enables accurate classification and prediction of the dynamic evolution of flow structures based on learned representations from scattering images. Experimental results show that the feature vectors extracted by the SAE form clearly separable clusters in the latent space, leading to high classification accuracy under varying flow conditions. In the prediction task, the feature vectors predicted by the LSTM exhibit strong agreement with ground truth, with average mean square error, mean absolute error, and r-square values of 0.0027, 0.0398, and 0.9897, respectively. Furthermore, the reconstructed images offer a visual representation of the changing flow field, validating the model’s effectiveness in structure-level recovery. These results suggest that the proposed method provides reliable support for future real-time prediction of powder fuel mass flow rates based on optical sensing and imaging techniques.https://www.mdpi.com/1424-8220/25/15/4613scattering imagefeature extractiontemporal predictionstacked autoencoderLSTM network |
| spellingShingle | Hongyuan Du Zhen Cao Yingjie Song Jiangbo Peng Chaobo Yang Xin Yu Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning Sensors scattering image feature extraction temporal prediction stacked autoencoder LSTM network |
| title | Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning |
| title_full | Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning |
| title_fullStr | Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning |
| title_full_unstemmed | Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning |
| title_short | Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning |
| title_sort | flow field reconstruction and prediction of powder fuel transport based on scattering images and deep learning |
| topic | scattering image feature extraction temporal prediction stacked autoencoder LSTM network |
| url | https://www.mdpi.com/1424-8220/25/15/4613 |
| work_keys_str_mv | AT hongyuandu flowfieldreconstructionandpredictionofpowderfueltransportbasedonscatteringimagesanddeeplearning AT zhencao flowfieldreconstructionandpredictionofpowderfueltransportbasedonscatteringimagesanddeeplearning AT yingjiesong flowfieldreconstructionandpredictionofpowderfueltransportbasedonscatteringimagesanddeeplearning AT jiangbopeng flowfieldreconstructionandpredictionofpowderfueltransportbasedonscatteringimagesanddeeplearning AT chaoboyang flowfieldreconstructionandpredictionofpowderfueltransportbasedonscatteringimagesanddeeplearning AT xinyu flowfieldreconstructionandpredictionofpowderfueltransportbasedonscatteringimagesanddeeplearning |