Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method
A novel turbulence promoter comprising a V-shaped perforated rectangular winglet vortex generator integrated with a circular ring is introduced. The investigation delves into the heat transfer enhancement and pressure loss associated with its insertion within a tube, utilizing the artificial neural...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25006720 |
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| author | Jiangbo Wang Chenzhe Li Liangcai Zeng Ting Fu Kai Liu |
| author_facet | Jiangbo Wang Chenzhe Li Liangcai Zeng Ting Fu Kai Liu |
| author_sort | Jiangbo Wang |
| collection | DOAJ |
| description | A novel turbulence promoter comprising a V-shaped perforated rectangular winglet vortex generator integrated with a circular ring is introduced. The investigation delves into the heat transfer enhancement and pressure loss associated with its insertion within a tube, utilizing the artificial neural network approach, particularly the Multi-Layer Perceptron (MLP), for regression analysis. The outcomes are embodied in the Nusselt number ratio (Nu/Nu0), friction coefficient ratio (f/f0), and thermal enhancement factor (TEF). The findings underscore that the proposed turbulence promoter adeptly generates mixing vortices in the fluid flow, thereby enhancing heat transfer, with a maximum heat transfer enhancement of 3.9 times, and the optimal TEF value is 1.4. To evaluate the performance of the predictions, various metrics are employed, including the Regression Coefficient (R2), Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error, for each output variable. Furthermore, the MLP model emerges as a highly efficient tool for estimating the Nu/Nu0, f/f0, and TEF of the targeted heat exchanger, where the R2 value of Nu/Nu0 is 0.9821, f/f0 is 0.9967, and TEF is 0.9370. |
| format | Article |
| id | doaj-art-1c9143a1fb0b4365acbbd5c60cf5ec92 |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-1c9143a1fb0b4365acbbd5c60cf5ec922025-08-20T02:17:25ZengElsevierCase Studies in Thermal Engineering2214-157X2025-08-017210641210.1016/j.csite.2025.106412Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning methodJiangbo Wang0Chenzhe Li1Liangcai Zeng2Ting Fu3Kai Liu4Key Laboratory of Metallurgical Equipment and Control Technology (Wuhan University of Science and Technology), Ministry of Education & Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology) & Precision Manufacturing Institute (Wuhan University of Science and Technology), Wuhan, 430081, People's Republic of ChinaKey Laboratory of Metallurgical Equipment and Control Technology (Wuhan University of Science and Technology), Ministry of Education & Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology) & Precision Manufacturing Institute (Wuhan University of Science and Technology), Wuhan, 430081, People's Republic of ChinaKey Laboratory of Metallurgical Equipment and Control Technology (Wuhan University of Science and Technology), Ministry of Education & Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology) & Precision Manufacturing Institute (Wuhan University of Science and Technology), Wuhan, 430081, People's Republic of China; Corresponding author.Key Laboratory of Metallurgical Equipment and Control Technology (Wuhan University of Science and Technology), Ministry of Education & Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology) & Precision Manufacturing Institute (Wuhan University of Science and Technology), Wuhan, 430081, People's Republic of China; Corresponding author.College of Intelligent Manufacturing, Wuhan Polytechnic, Wuhan, Hubei, 430074, People's Republic of ChinaA novel turbulence promoter comprising a V-shaped perforated rectangular winglet vortex generator integrated with a circular ring is introduced. The investigation delves into the heat transfer enhancement and pressure loss associated with its insertion within a tube, utilizing the artificial neural network approach, particularly the Multi-Layer Perceptron (MLP), for regression analysis. The outcomes are embodied in the Nusselt number ratio (Nu/Nu0), friction coefficient ratio (f/f0), and thermal enhancement factor (TEF). The findings underscore that the proposed turbulence promoter adeptly generates mixing vortices in the fluid flow, thereby enhancing heat transfer, with a maximum heat transfer enhancement of 3.9 times, and the optimal TEF value is 1.4. To evaluate the performance of the predictions, various metrics are employed, including the Regression Coefficient (R2), Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error, for each output variable. Furthermore, the MLP model emerges as a highly efficient tool for estimating the Nu/Nu0, f/f0, and TEF of the targeted heat exchanger, where the R2 value of Nu/Nu0 is 0.9821, f/f0 is 0.9967, and TEF is 0.9370.http://www.sciencedirect.com/science/article/pii/S2214157X25006720Perforated rectangular vortex generatorMachine learningMulti-layer perceptronHeat transfer enhancement |
| spellingShingle | Jiangbo Wang Chenzhe Li Liangcai Zeng Ting Fu Kai Liu Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method Case Studies in Thermal Engineering Perforated rectangular vortex generator Machine learning Multi-layer perceptron Heat transfer enhancement |
| title | Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method |
| title_full | Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method |
| title_fullStr | Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method |
| title_full_unstemmed | Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method |
| title_short | Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method |
| title_sort | thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method |
| topic | Perforated rectangular vortex generator Machine learning Multi-layer perceptron Heat transfer enhancement |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25006720 |
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