A detailed review of pulsating heat pipe correlations and recent advances using Artificial Neural Network for improved performance prediction

Kholi, Foster Kwame; Park, Seong-Ho; Yang, Jae Sung; Ha, Man Yeong; Min, June Kee

doi:10.1016/j.ijheatmasstransfer.2023.124010

Cited by 14 publications

(2 citation statements)

References 161 publications

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“…Benefiting from its powerful data processing capabilities, it can be used to solve the classification problem of edible crops and the regression problem. However, ANN relies excessively on large sample sizes and requires sufficient training data to maintain the stability of the model ( Kholi et al, 2023 ). To improve the ANN model, Yuan et al (2023) proposed a self-adjusting particle swarm optimization (APSO) algorithm to optimize the weights, thresholds, and number of neurons of the ANN.…”

Section: Machine Learningmentioning

confidence: 99%

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

Zhang,

Wang

2023

Food Chemistry: X

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Section: Machine Learningmentioning

confidence: 99%

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

Zhang,

Wang

2023

Food Chemistry: X

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“…High prediction accuracies were also assessed by other neural network models, such as the ones by Jokar et al [19] (average relative error less than 5%) and by Ahmadi et al [20] (minimum deviation of 3.33% achieved through the least-squares support-vector machine model). However, as underlined by Kholi et al in their review article [21], artificial neural network models per se provide no physical insight into the PHP thermo-fluid dynamics, resulting in poor flexibility and accuracy outside the training dataset ranges. Such an issue inevitably undermines a full understanding of the complex physics underlining PHP functioning as well as a robust conceptualization of design techniques to be used for reliable, large-scale employment by industries [2].…”

Section: Introductionmentioning

confidence: 99%

Are Local Heat Transfer Quantities Useful for Predicting the Working Behavior of Different Pulsating Heat Pipe Layouts? A Comparative Study

Pagliarini,

Bozzoli

2024

Fluids

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Despite a continuous effort devoted by the scientific community, a large-scale employment of Pulsating Heat Pipes for thermal management applications is still nowadays undermined by the low reliability of such heat transfer systems. The main reason underlying this critical issue is linked to the strongly chaotic thermofluidic behavior of these devices, which prevents a robust prediction of their working behavior for different geometries and operating conditions, consequently hampering proper industrial design. The present work proposes to thoroughly compare data referring to previous infrared investigations on different Pulsating Heat Pipe layouts, which have focused on the estimation of heat fluxes locally exchanged at the wall–fluid interfaces. The aim is to understand the beneficial contribution of local heat transfer quantities in the prediction of the complex physics underlying such heat transfer systems. The results have highlighted that, regardless of the considered geometry and working conditions, wall-to-fluid heat fluxes are able to provide useful quantities to be employed, to some extent, to generalize Pulsating Heat Pipe operation and to improve their existing numerical models.

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Numerical Investigation and Machine Learning Predictions for Enhanced Thermal Management in Pulsating Heat Pipes: Modeling Turbulent Flow and Heat Transfer Characteristics in Nuclear Applications

Khosravian

2024

Numerical Methods in Fluids

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This paper presents a comprehensive numerical investigation of the performance of pulsating heat pipes (PHPs) within nuclear reactor cooling systems. A volume of fluid (VOF) method was used to simulate the complex multiphase flow, providing detailed insights into fluid distribution, phase interactions, and temperature variations under different operating conditions. The simulations revealed distinct phase separation and convective flow patterns that enhance heat transfer efficiency, which is critical for optimizing thermal management in nuclear reactors. Additionally, artificial neural network (ANN) models were employed to predict volume fractions and wall temperatures, achieving high accuracy with R2 values of 0.99 and 0.98, respectively, and low mean absolute errors (MAE). The ANN models also reduced computational time by 90% compared to traditional numerical simulations. These findings highlight the potential of PHPs to improve heat transfer in nuclear systems and demonstrate the practicality of ANN models for real‐time thermal optimization. The research contributes to enhancing the safety and efficiency of nuclear reactor cooling systems, with broader implications for thermal management across various engineering applications.

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A detailed review of pulsating heat pipe correlations and recent advances using Artificial Neural Network for improved performance prediction

Cited by 14 publications

References 161 publications

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

Are Local Heat Transfer Quantities Useful for Predicting the Working Behavior of Different Pulsating Heat Pipe Layouts? A Comparative Study

Numerical Investigation and Machine Learning Predictions for Enhanced Thermal Management in Pulsating Heat Pipes: Modeling Turbulent Flow and Heat Transfer Characteristics in Nuclear Applications

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