Implementation of Artificial Intelligence in Modeling and Control of Heat Pipes: A Review

Abdul Ghani Olabi, Salah Haridy, Enas Taha Sayed, Muaz Al Radi, Abdul Hai Alami, Firas Zwayyed, Tareq Salameh and Mohammad Ali Abdelkareem
Additional contact information
Abdul Ghani Olabi: Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Salah Haridy: Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Enas Taha Sayed: Chemical Engineering Department, Faculty of Engineering, Minia University, Minya 61519, Egypt
Muaz Al Radi: Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
Abdul Hai Alami: Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Firas Zwayyed: Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Tareq Salameh: Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Mohammad Ali Abdelkareem: Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates

Energies, 2023, vol. 16, issue 2, 1-18

Abstract: Heat pipe systems have attracted increasing attention recently for application in various heat transfer-involving systems and processes. One of the obstacles in implementing heat pipes in many applications is their difficult-to-model operation due to the many parameters that affect their performance. A promising alternative to classical modeling that emerges to perform accurate modeling of heat pipe systems is artificial intelligence (AI)-based modeling. This research reviews the applications of AI techniques for the modeling and control of heat pipe systems. This work discusses the AI-based modeling of heat pipes focusing on the influence of chosen input parameters and the utilized prediction models in heat pipe applications. The article also highlights various important aspects related to the application of AI models for modeling heat pipe systems, such as the optimal AI model structure, the models overfitting under small datasets conditions, and the use of dimensionless numbers as inputs to the AI models. Also, the application of hybrid AI algorithms (such as metaheuristic optimization algorithms with artificial neural networks) was reviewed and discussed. Next, intelligent control methods for heat pipe systems are investigated and discussed. Finally, future research directions are included for further improving this technology. It was concluded that AI algorithms and models could predict the performance of heat pipe systems accurately and improve their performance substantially.

Keywords: modeling; artificial intelligence; heat pipes; controlling; prediction; literature review (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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