Prediction of two-phase flow distribution in microchannel heat exchangers using artificial neural network

Giannetti, Niccolò; Redo, Mark Anthony; Sholahudin,; Jeong, Jae–Tack; Yamaguchi, Seiichi; Saito, Kiyoshi; Kim, Hyunyoung

doi:10.1016/j.ijrefrig.2019.11.028

Cited by 33 publications

(7 citation statements)

References 50 publications

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“…Combined with the classical theoretical model, they calculated the viscosity and density of the liquid and compared the predicted data with the measured data, they found that the prediction accuracy of the ANN is high, which proves the ANN is a powerful tool to optimize the microchannel heat sink. Giannetti et al [124] established a model to predict the distribution of two-phase flow in the microchannel heat exchanger by the ANN. Comparing the previous experimental data with the output data of their model, it is found out that most of the deviations are less than 10%, and the highest correlation index is more than 98%.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Fluid flow and heat transfer in microchannel heat sinks: Modelling review and recent progress

Gao

Yang

et al. 2022

Thermal Science and Engineering Progress

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Fluid flow and heat transfer in microchannel heat sinks: Modelling review and recent progress

Gao

Yang

et al. 2022

Thermal Science and Engineering Progress

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“…In this technique, the weights and biases are considered random variable with certain distributions and are being updated using the Levenberg-Marquardt. The efficiency of this technique has been proven in difficult optimization problems [46], and therefore, it was implemented in the ANN toolbox MATALAB [21]. On the other hand, the Bayesian regularization function takes a long time compared with the usual Levenberg-Marquardt algorithm before it convergences [46].…”

Section: Training Function and Learning Processmentioning

confidence: 99%

Simulating the response of ionization chamber system to 137Cs irradiator using the artificial neural network modeling algorithm

Sadek

2020

SN Appl. Sci.

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The artificial neural network algorithm has been used to simulate the response of an ionization chamber dosimetric system to the 137 Cs irradiator facility at the National Institute for Standards, Egypt. The performance of the designed model has been investigated over different processes functions and a wide range of parameters till the optimum performance has been approached. The standard uncertainty of the designed model has been evaluated for different dose-rate levels. It has been found that the dose-rate can be evaluated using the designed model with an uncertainty < 2.0% for the dose-rate level < 10 2 mGy/h and with an uncertainty < 0.5% for the dose-rate level [10 2 −10 3 ] mGy/h . The bias of the designed model has been evaluated and compared with the usual interpolation algorithms. The errors in the evaluated dose-rate using these methods have exceeded the 100% error level and approached the 20% error level for the dose rates less and greater than 10 mGy/h , respectively. While, the error in the evaluated dose-rate using the designed model did not exceed 7% and 2% for the same dose-rate levels, respectively.

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“…[7]. Other recently reported work on neural networks for two-phase flow regime identification can be found in [7], [25], [26], [27], and [28].…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline riser using a Doppler ultrasonic sensor and deep neural networks

Nnabuife

Kuang

Whidborne

et al. 2021

Chemical Engineering Journal

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Prediction of two-phase flow distribution in microchannel heat exchangers using artificial neural network

Cited by 33 publications

References 50 publications

Fluid flow and heat transfer in microchannel heat sinks: Modelling review and recent progress

Fluid flow and heat transfer in microchannel heat sinks: Modelling review and recent progress

Simulating the response of ionization chamber system to 137Cs irradiator using the artificial neural network modeling algorithm

Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline riser using a Doppler ultrasonic sensor and deep neural networks

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