A detailed review of pulsating heat pipe correlations and recent advances using Artificial Neural Network for improved performance prediction. (15th June 2023)

• Record Type: Journal Article
• Authors: Kholi, Foster Kwame; Park, Seongho; Yang, Jae Sung; Ha, Man Yeong; Min, June Kee
• Is Part Of: International journal of heat and mass transfer. Volume 207(2023)
• Publication Date: 2023-06-15
• Subjects: Pulsating heat pipes -- Heat transfer correlations -- Regression correlation analysis -- Intelligent predictions -- Artificial Neural Network -- Working fluids -- Thermal management; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2023.124010
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Influencing factors governing PHP thermo-hydrodynamics are assessed. Current and future PHP applications in different thermal sectors are summarized. Development and progress of PHP semi-empirical correlations are elucidated. Recent development using ANN models for PHP analysis are discussed. PHP performance prediction with coupled correlation and ANN models is discussed. Abstract: The unique thermal properties and flexible design of pulsating heat pipes ( PHP ) offer opportunities for relatively lightweight, low-cost, and reliable phase-change thermal solutions. Nevertheless, the performance of PHP is affected by multiple factors making mathematical predictions of their performance difficult. So, costly experiments in restricted test environments and time-consuming numerical analysis are typical methods for detecting internal thermo-hydrodynamics and data acquisition. Since theoretical models are entirely data-driven that require substantial data for validation, shortfalls in available data affect their prediction accuracy. This trend reduces the accuracy of semi-empirical correlations ( SEC ) and other mathematical models obtained through Regression Correlation Analysis, the Buckingham Theorem, and Intelligent predictions. In this review, the major developments and shortcomings of the SEC between 2003 and 2022 were reported. The opportunities for improvement have been discussed exhaustively. Moreover, the recent advances in Artificial Neural Networks ( ANN ) for PHP performance prediction have been adequately reviewed. Since ANN models are based on black-box analyses, with few physical explanations of heat transfer phenomena, this review suggests a potential coupling between ANN models and SEC . By inferring real phenomena from the dimensionless numbers in SEC, faster, more accurate, and holistic PHP thermo-hydrodynamics can be attained.