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Implementation of artificial intelligence in modeling and control of heat pipes: a review.

Author(s) : OLABI A. G., HARIDY S., SAYED E. T., RADI M. A., ALAMI A. H., ZWAYYED F., SALAMEH T., ABDELKAREEM M. A.

Type of article: Periodical article, Review

Summary

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.

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Pages: 18 p.

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Details

  • Original title: Implementation of artificial intelligence in modeling and control of heat pipes: a review.
  • Record ID : 30031243
  • Languages: English
  • Subject: Technology
  • Source: Energies - vol. 16 - 2
  • Publishers: MDPI
  • Publication date: 2023/01
  • DOI: http://dx.doi.org/https://doi.org/10.3390/en16020760

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