Modelling of thermo-hydraulic behavior of a helical heat exchanger using machine learning model and fire hawk optimizer
The study of the fluid flow in heat exchangers and its related mass and heat transfer processes is a very complicated research subject. Modeling of coupled fluid flow and heat transfer processes using numerical and analytical approaches are cumbersome problems. Hence, the current study investigates...
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| Published in | Case studies in thermal engineering Vol. 49; p. 103294 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.09.2023
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2214-157X 2214-157X |
| DOI | 10.1016/j.csite.2023.103294 |
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| Abstract | The study of the fluid flow in heat exchangers and its related mass and heat transfer processes is a very complicated research subject. Modeling of coupled fluid flow and heat transfer processes using numerical and analytical approaches are cumbersome problems. Hence, the current study investigates the modeling thermo-hydraulic behavior of a helical plate heat exchanger (HPHE) using advanced machine learning models. The outlet temperatures of hot and cold fluids are predicted considering different cross-sectional areas and pitches of the flow channels. This paper fulfills the research gap about fluid flow and heat transfer mechanisms by considering advanced machine learning approaches. The proposed model helps different manufacturers of heat exchangers to estimate the thermal performance and characteristics. The developed model aims to provide an advanced random vector functional link (RVFL) optimized fire hawk optimizer to predict outlet temperatures of working fluids of HPHE. The proposed model was compared with four optimized models using grey wolf, jellyfish, sine-cosine, and hybrid salp swarm-whale optimizers. The results of all models were compared and fire hawk optimizers showed superior accuracy compared with other models. Fire hawk optimizer had the highest R2 (0.999) followed by jellyfish (0.998) for both investigated responses. Hybrid salp swarm-whale and sine-cosine optimizers had the lowest R2 (0.987) in the case of hot fluid. |
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| AbstractList | The study of the fluid flow in heat exchangers and its related mass and heat transfer processes is a very complicated research subject. Modeling of coupled fluid flow and heat transfer processes using numerical and analytical approaches are cumbersome problems. Hence, the current study investigates the modeling thermo-hydraulic behavior of a helical plate heat exchanger (HPHE) using advanced machine learning models. The outlet temperatures of hot and cold fluids are predicted considering different cross-sectional areas and pitches of the flow channels. This paper fulfills the research gap about fluid flow and heat transfer mechanisms by considering advanced machine learning approaches. The proposed model helps different manufacturers of heat exchangers to estimate the thermal performance and characteristics. The developed model aims to provide an advanced random vector functional link (RVFL) optimized fire hawk optimizer to predict outlet temperatures of working fluids of HPHE. The proposed model was compared with four optimized models using grey wolf, jellyfish, sine-cosine, and hybrid salp swarm-whale optimizers. The results of all models were compared and fire hawk optimizers showed superior accuracy compared with other models. Fire hawk optimizer had the highest R2 (0.999) followed by jellyfish (0.998) for both investigated responses. Hybrid salp swarm-whale and sine-cosine optimizers had the lowest R2 (0.987) in the case of hot fluid. |
| ArticleNumber | 103294 |
| Author | Abd Elaziz, Mohamed Mudhsh, Mohammed El-Said, Emad M.S. Elshamy, Samir M. Elsheikh, Ammar H. Aseeri, Ahmad O. Almodfer, Rolla |
| Author_xml | – sequence: 1 givenname: Mohammed surname: Mudhsh fullname: Mudhsh, Mohammed email: mudhish@hist.edu.cn organization: School of Computer Science &Technology, Henan Institute of Science and Technology, Xinxiang, 453003, China – sequence: 2 givenname: Emad M.S. surname: El-Said fullname: El-Said, Emad M.S. email: emspeng@gmail.com organization: Department of Mechanical Power Engineering, Faculty of Engineering, Damietta University, Damietta, Egypt – sequence: 3 givenname: Ahmad O. surname: Aseeri fullname: Aseeri, Ahmad O. organization: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia – sequence: 4 givenname: Rolla surname: Almodfer fullname: Almodfer, Rolla organization: School of Computer Science &Technology, Henan Institute of Science and Technology, Xinxiang, 453003, China – sequence: 5 givenname: Mohamed surname: Abd Elaziz fullname: Abd Elaziz, Mohamed organization: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt – sequence: 6 givenname: Samir M. surname: Elshamy fullname: Elshamy, Samir M. organization: High Institute of Engineering, 6 October, Egypt – sequence: 7 givenname: Ammar H. orcidid: 0000-0003-0944-4938 surname: Elsheikh fullname: Elsheikh, Ammar H. email: ammar_elsheikh@f-eng.tanta.edu.eg organization: Department of Production Engineering and Mechanical Design, Tanta University, Tanta 31527, Egypt |
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| Keywords | Fire hawk optimizer Counter flow heat exchanger Plate heat exchanger Machine learning Random vector functional link |
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| Title | Modelling of thermo-hydraulic behavior of a helical heat exchanger using machine learning model and fire hawk optimizer |
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