Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural Network
Numerical simulations are usually used to analyze and optimize the performance of the nanofluid-filled absorber tube with fins. However, solving partial differential equations (PDEs) repeatedly requires considerable computational cost. This study develops two deep neural network-based reduced-order...
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| author | Yue Hua Chang-Hao Yu Jiang-Zhou Peng Wei-Tao Wu Yong He Zhi-Fu Zhou |
| author_facet | Yue Hua Chang-Hao Yu Jiang-Zhou Peng Wei-Tao Wu Yong He Zhi-Fu Zhou |
| author_sort | Yue Hua |
| collection | DOAJ |
| description | Numerical simulations are usually used to analyze and optimize the performance of the nanofluid-filled absorber tube with fins. However, solving partial differential equations (PDEs) repeatedly requires considerable computational cost. This study develops two deep neural network-based reduced-order models to accurately and rapidly predict the temperature field and heat flux of nanofluid-filled absorber tubes with rectangular fins, respectively. Both network models contain a convolutional path, receiving and extracting cross-sectional geometry information of the absorber tube presented by signed distance function (SDF); then, the following deconvolutional blocks or fully connected layers decode the temperature field or heat flux out from the highly encoded feature map. According to the results, the average accuracy of the temperature field prediction is higher than 99.9% and the computational speed is four orders faster than numerical simulation. For heat flux estimation, the R<sup>2</sup> of 81 samples reaches 0.9995 and the average accuracy is higher than 99.7%. The same as the field prediction, the heat flux prediction also takes much less computational time than numerical simulation, with 0.004 s versus 393 s. In addition, the changeable learning rate strategy is applied, and the influence of learning rate and dataset size on the evolution of accuracy are investigated. According to our literature review, this is the first study to estimate the temperature field and heat flux of the outlet cross section in 3D nanofluid-filled fined absorber tubes using a deep convolutional neural network. The results of the current work verify both the high accuracy and efficiency of the proposed network model, which shows its huge potential for the fin-shape design and optimization of nanofluid-filled absorber tubes. |
| first_indexed | 2024-03-09T19:17:50Z |
| format | Article |
| id | doaj.art-1f93d9bbad2148b099c834f43daad69c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:50Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1f93d9bbad2148b099c834f43daad69c2023-11-24T03:34:27ZengMDPI AGApplied Sciences2076-34172022-10-0112211088310.3390/app122110883Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural NetworkYue Hua0Chang-Hao Yu1Jiang-Zhou Peng2Wei-Tao Wu3Yong He4Zhi-Fu Zhou5Sino-French Engineer School, Nanjing University of Science and Technology, Nanjing 210094, ChinaSino-French Engineer School, Nanjing University of Science and Technology, Nanjing 210094, ChinaKey Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaState Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaNumerical simulations are usually used to analyze and optimize the performance of the nanofluid-filled absorber tube with fins. However, solving partial differential equations (PDEs) repeatedly requires considerable computational cost. This study develops two deep neural network-based reduced-order models to accurately and rapidly predict the temperature field and heat flux of nanofluid-filled absorber tubes with rectangular fins, respectively. Both network models contain a convolutional path, receiving and extracting cross-sectional geometry information of the absorber tube presented by signed distance function (SDF); then, the following deconvolutional blocks or fully connected layers decode the temperature field or heat flux out from the highly encoded feature map. According to the results, the average accuracy of the temperature field prediction is higher than 99.9% and the computational speed is four orders faster than numerical simulation. For heat flux estimation, the R<sup>2</sup> of 81 samples reaches 0.9995 and the average accuracy is higher than 99.7%. The same as the field prediction, the heat flux prediction also takes much less computational time than numerical simulation, with 0.004 s versus 393 s. In addition, the changeable learning rate strategy is applied, and the influence of learning rate and dataset size on the evolution of accuracy are investigated. According to our literature review, this is the first study to estimate the temperature field and heat flux of the outlet cross section in 3D nanofluid-filled fined absorber tubes using a deep convolutional neural network. The results of the current work verify both the high accuracy and efficiency of the proposed network model, which shows its huge potential for the fin-shape design and optimization of nanofluid-filled absorber tubes.https://www.mdpi.com/2076-3417/12/21/10883nanofluidabsorber tubefield reconstructionperformance parameter predictiondeep learning |
| spellingShingle | Yue Hua Chang-Hao Yu Jiang-Zhou Peng Wei-Tao Wu Yong He Zhi-Fu Zhou Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural Network Applied Sciences nanofluid absorber tube field reconstruction performance parameter prediction deep learning |
| title | Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural Network |
| title_full | Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural Network |
| title_fullStr | Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural Network |
| title_full_unstemmed | Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural Network |
| title_short | Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural Network |
| title_sort | thermal performance estimation of nanofluid filled finned absorber tube using deep convolutional neural network |
| topic | nanofluid absorber tube field reconstruction performance parameter prediction deep learning |
| url | https://www.mdpi.com/2076-3417/12/21/10883 |
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