Thermal performance prediction models for a pulsating heat pipe using Artificial Neural Network (ANN) and Regression/Correlation Analysis (RCA)

Patel, Vipul M.; Mehta, Hemant B

doi:10.1007/s12046-018-0954-3

Cited by 30 publications

(12 citation statements)

References 54 publications

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“…The trained network was validated with experimental data and used to evaluate the objective function to maximize the thermal efficiency of the system. A comprehensive discussion of the thermal performance prediction of PHPs based on an artificial neural network (ANN) and regression/correlation analysis (RCA) was proposed by Patel and Metha [42]. The authors investigated the influence of nine major input variables, considering more than 1600 experimental points from the literature.…”

Section: Machine Learning Algorithms For Two-phase Flow Heat Transfermentioning

confidence: 99%

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Loyola-Fuentes¹,

Pietrasanta

Marengo

et al. 2022

Energies

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Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the predictions of the flow regimes.

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Section: Machine Learning Algorithms For Two-phase Flow Heat Transfermentioning

confidence: 99%

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Loyola-Fuentes¹,

Pietrasanta

Marengo

et al. 2022

Energies

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“…It was observed increasing the heat input resulted caused the translational and combined oscillatory-translational motion to become dominant, thus increasing its thermal performance. B. Mehta [17] developed models of PHP to predict its thermal performance based on Artificial Neural Network and Regression / Correlation Analysis (RCA) approach using literature-based dataset. A Coefficient Of Determination abbreviated as R 2 of 0.89 which affirms good agreement with higher prediction accuracy was found for the model developed based on feed-forward back-propagation ANN, while the RCA model was not observed to be in agreement with R 2 = 0.38.…”

Section: Theoretical Modeling Of Phpsmentioning

confidence: 99%

A Review on Pulsating Heat Pipes: Latest Research, Applications and Future Scope

Dave¹,

Dandale

Shrivastava

et al. 2021

Journal of Thermal Engineering

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Heat pipes have become popular as passive heat dissipation devices for many applications ranging from electronics to heat recovery in recent years. Pulsating Heat Pipe abbreviated as PHP is an exclusive category of a heat pipe which does have wick structure. It transfers heat between its evaporator and condenser sections due to the to and fro movement of working fluid caused due to the continuous change of phase taking place in it. Its operation involves combined hydrodynamic and thermodynamic effects causing the two-phase flow, making it difficult to fully understand it. Nevertheless, the PHPs have drawn the attention of investigators worldwide due to their simplicity in construction and a wide range of applications requiring high heat flux transfer. This review starts with a prologue of the attempts made by various investigators to explain the operation of PHPs through mathematical modeling. Later numerical simulations through Computational Fluid Dynamics of PHP performed by various investigators have been discussed. Review of latest experimental investigations performed on PHPs, is presented concisely in a tabular form. Novel concepts like the application of nanofluids, magnetic field and surfactant, applied for enhancing the performance of the PHPs have been discussed. At the end, applications of PHP and future scope have been discussed.

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“…It was shown that the relative errors of 90% of the data points were less than 5%, and the remaining points lay between 5% and 12%. Another ANN model was suggested by Patel et al [19], with inner diameter, outside diameter, lengths of evaporation and condensation section, number of turns, heating power, filling ratio, and inclination angle as the inputs. The working fluids were examined by index.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Small-channel Pulsating Heat Pipe Based on Dimensional Analysis and ANN Model

Wang¹,

Wright²,

Liu³

et al. 2022

Energy Engineering

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The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips, as it is characterised by excellent heat transfer ability and flexibility for miniaturisation. To boost the application of PHP, reliable heat transfer performance evaluation models are especially important. In this paper, a heat transfer correlation was firstly proposed for closed PHP with various working fluids (water, ethanol, methanol, R123, acetone) based on collected experimental data. Dimensional analysis was used to group the parameters. It was shown that the average absolute deviation (AAD) and correlation coefficient (r) of the correlation were 40.67% and 0.7556, respectively. For 95% of the data, the prediction of thermal resistance and the temperature difference between evaporation and condensation section fell within 1.13 K/W and 40.76 K, respectively. Meanwhile, an artificial neural network model was also proposed. The ANN model showed a better prediction accuracy with a mean square error (MSE) and correlation coefficient (r) of 7.88e-7 and 0.9821, respectively.

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Thermal performance prediction models for a pulsating heat pipe using Artificial Neural Network (ANN) and Regression/Correlation Analysis (RCA)

Cited by 30 publications

References 54 publications

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

A Review on Pulsating Heat Pipes: Latest Research, Applications and Future Scope

Performance Evaluation of Small-channel Pulsating Heat Pipe Based on Dimensional Analysis and ANN Model

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