Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network

Wang, Xuehui; Hou, Yudong; Gao, Neng; Chen, Guangming

doi:10.1007/s11630-019-1158-9

Cited by 35 publications

(9 citation statements)

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“…The results revealed that the proposed ANN model was created in such a way that it can forecast the Al 2 O 3 ‐Cu/EG hybrid nanofluid's thermal conductivity with ultimate accuracy. Wang et al 23 developed an ANN to predict the thermal conductivity of ethylene glycol‐based nanofluids, which they arranged at different concentrations in the scale of 0.05%‐10%, using different nanoparticles of 2 nm nanoparticle size, in the temperature among of 4°C‐90°C. In the ANN model created with a total of 391 tentative data, temperature, nanoparticle diameter and concentration were selected as input parameters and thermal conductivity in the output layer was estimated.…”

Section: Introductionmentioning

confidence: 99%

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

Çolak

2021

Int J Energy Res

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Summary In this study, the effect of the number of neurons on the predictive performance of artificial neural networks (ANN) has been investigated using experimental data. For this purpose, 6 different ANN have been developed by using a total of 60 experimental data of ZrO2/water nanofluid obtained from the literature. In ANN developed with the number of 5, 10, 15, 20, 25, and 30 neurons, all other parameters have been kept constant, and the effect of only the number of neurons on the prediction performance has been investigated. The performance of each ANN has been calculated separately and then their performance has been analyzed by comparing them with each other. As a consequence of the study, it has been seen that the model with the most ideal predictive performance has been developed with 5 neurons with an average error rate of 0.001%, and the highest margin of error the model has been developed with 15 neurons and had an error rate of 0.026%. In the light of the obtained data, it has been concluded that ANN are generally high performance predictive tools, and it is not possible to reach a standard correlation to regulate the number of neurons to be used in the optimization of ANN.

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Section: Introductionmentioning

confidence: 99%

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

Çolak

2021

Int J Energy Res

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“…Due to great stability, flexibility, and adaptability of Levenberg-Marquardt algorithm, it was selected as the learning algorithm of the ANN model. The prediction performance of the ANN model is usually described by correlation efficient (r) and mean square error (MSE), and they are defined by the following equations [45]:…”

Section: Ann Model For the Phpmentioning

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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“…It should be noted that the term of α k I was added to increase the adaptability of the algorithm. The performance of the ANN model was evaluated through correlation coefficient (r), mean square error (MSE) and average absolute deviation (AAD), and they are defined by 24,25…”

Section: Building Of the Ann Modelmentioning

confidence: 99%

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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Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network

Cited by 35 publications

References 50 publications

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

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

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

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Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network

Cited by 35 publications

References 50 publications

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO 2 nanofluid

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO 2 nanofluid

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

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

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Product

Resources

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A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid