Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad

doi:10.1007/s00231-016-1759-8

Cited by 49 publications

(21 citation statements)

References 18 publications

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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%

“…Evaluation of an optimal number of neurons is achieved through trial and error based on the criteria of R (coefficient of correlation). Totally 11 ANN models are developed based on the number of neurons (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and tabulated in table 4. The value of overall R is found to be the maximum (0.9448) for the fifth ANN model with 14 number of neurons.…”

Section: Prediction Model Using Annmentioning

confidence: 99%

“…Power-law regression model for Ku is given by Eq. (19). It is converted into equivalent linear form by taking natural log on both sides and shown as Eq.…”

Section: Parametersmentioning

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: Prediction Model Using Annmentioning

confidence: 99%

“…Power-law regression model for Ku is given by Eq. (19). It is converted into equivalent linear form by taking natural log on both sides and shown as Eq.…”

Section: Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Generally, the artificial neural network has been utilized for system identification and controller in wide areas of applications from the industrial nonlinear process to the vehicle control system. These included the thermal dynamic of pulsating heat pipe [49] and greenhouse temperature [50] to the autonomous vehicle control [51] and UAVs [19,23,24].…”

Section: The Direct Inverse Control Of the Ann-based Controllermentioning

confidence: 99%

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

Muliadi

Kusumoputro

2018

Journal of Advanced Transportation

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This article proposes a comparative method to assess the performance of artificial neural network's direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

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Development of a novel artificial neural network model for closed pulsating heat pipe with water and aqueous solutions

Wang

Wright

Liu

et al. 2021

Asia-Pacific J Chem Eng

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Aqueous solutions are increasingly introduced as the working fluids of pulsating heat pipe (PHP), to balance and optimise certain thermo-physical properties of pure water. In this paper, a first attempt is conducted to predict the heat transfer performance of closed PHP with pure water and available aqueous solutions based on a fully connected feed-forward artificial neural network (ANN). The notable dimensionless numbers, Kutateladze number (Ku), Bond number (Bo), Morton number (Mo), Jackob number (Ja), Prandtl number (Pr), Laplace number (La), d/L e and number of turns (N) were selected as the inputs of the ANN model. It was found that the prediction of proposed ANN model posed a good agreement with experimental data with the MSE and correlation coefficient of 0.026 and 0.979, respectively. For 81.02% of the collected data, the absolute deviation of prediction for thermal resistance was within 25%. For 95% of collected data, the prediction of thermal resistance and temperature difference between the evaporation and condensation section fell within ±0.37 K/W, and ±17.1 K, respectively. The evaluation of evaporation and condensation section temperatures was also presented based on the proposed ANN model.

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Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

Cited by 49 publications

References 18 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)

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

Development of a novel artificial neural network model for closed pulsating heat pipe with water and aqueous solutions

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