Modeling of Closed Loop Pulsating Heat Pipes by Neural Networks

Shokouhmand, Hossein; Gharib, Nima; Bahrami, Hafez

doi:10.1115/esda2006-95417

Cited by 6 publications

(3 citation statements)

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“…Optimal ANN structure requires a specific number of hidden layers with an optimal number of neurons. An ANN structure with a minimum of two hidden layers and optimal neurons is able to model the complex behaviour of PHP system [19][20][21][22]. ANN model with an excessive number of neurons causes overfitting, additional unnecessary training time and leads to complex interconnection weight structure whereas an inadequate number of neurons are not able to learn the complete relationship between data.…”

Section: Prediction Model Using Annmentioning

confidence: 99%

“…Numerical *For correspondence investigations are carried out with limited mass transfer mechanism and dimensionality of the PHP [17,18]. Artificial Neural Network (ANN) methods are also reported to predict the nonlinear behaviour and thermal performance of a PHP [19][20][21][22][23]. Nevertheless, it is worth noting that influence of flow pattern transition during operation on the oscillating/pulsating motion of vapour bubbles and liquid slugs is not considered.…”

Section: Introductionmentioning

confidence: 99%

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

Patel

Mehta

2018

Sādhanā

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Pulsating heat pipe (PHP) is one of the prominent research areas in the family of heat pipes. Heat transfer and fluid flow mechanism associated with PHP are quite involved. The analytical models are simple in nature and limited in scope and applicability. The regression models and Artificial Neural Network (ANN) are also limited to a number of input parameters, their ranges and accuracy. The present paper discusses the thermal performance prediction models of a PHP based on ANN and RCA approach. Totally 1652 experimental data are collected from the literature (2003-2017). Nine major influencing input variables are considered for the first time to develop the prediction models. Feed-forward back-propagation neural network is developed and verified. Backward regression analysis is used in RCA-based regression model. Linear and power-law regression correlations are developed for input heat flux in terms of dimensionless Kutateladze (Ku) number, which is a function of Jakob number (Ja), Morton number (Mo), Bond number (Bo), Prandtl number (Pr) and geometry of a PHP. The prediction accuracy of present regression models (R 2 = 0.95) is observed to be better as compared with literature-based correlations.

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Section: Prediction Model Using Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Patel

Mehta

2018

Sādhanā

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“…Various nonlinear models based on fluctuations in the wall temperatures to understand the complex flow distribution in the PHP are implemented by Qu et al [8], and Song and Xu [9]. A neural network of two layers to predict the PHP behaviour is studied by Shokouhmand et al [10]. The results obtained are appropriate for predicting effective parameters trend on performance of PHP and they are in concurrent with available data.…”

Section: Introductionmentioning

confidence: 96%

Mathematical Modeling of Closed Loop Pulsating Heat Pipe by Using Artificial Neural Networks

Nerella¹,

Nakka²,

Bhramara³

2021

IJHT

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Pulsating heat pipe is one of the prominent technology for thermal management of electronic devices. It consists of three sections namely evaporator, adiabatic and condenser section. PHP is a two phase passive device having efficient and quick ability of transferring heat from evaporator section to condenser section. At first an 8 turn pulsating heat pipe of closed loop ends (CLPHP) with copper tube capillary dimensions is investigated experimentally for different fill ratios and for different inclinations by varying range of heat inputs. Different working fluids viz Water, Acetone, Ethanol and Methanol are considered for the experimentation. One of the recent analytical technology for modelling of CLPHPs is Artificial Neural Network (ANN) approach. The analytical models are having limited scope of applicability and they are simple in nature. The present paper describes Validation of experimental data by training prediction model ANN with available data. Three input nodes such as input heat, fill ratio and angle of inclination and one output node corresponding to PHP that is thermal resistance are considered. The feed forward neural network (FFNN) architecture is adopted for predictions. By using the physical phenomena of the system modelling are clearly known for obtaining feasible results which is main function of ANN. The predicted data validates experimental data in a satisfactory range and the results are found to be in good agreement with in the range of ± 10 percent.

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Investigation of Thermal Performance of Cylindrical Heat Pipe Using Silver Nanofluid: Part-II: Prediction Using Artificial Neural Network

Chavda¹

2019

J. Inst. Eng. India Ser. C

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Modeling of Closed Loop Pulsating Heat Pipes by Neural Networks

Cited by 6 publications

References 0 publications

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

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

Mathematical Modeling of Closed Loop Pulsating Heat Pipe by Using Artificial Neural Networks

Investigation of Thermal Performance of Cylindrical Heat Pipe Using Silver Nanofluid: Part-II: Prediction Using Artificial Neural Network

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