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Journal Articles

An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

Bivas Bhaumik, Satyasaran Changdar, Soumen De
Journal: ASME Journal of Heat and Mass Transfer
Publisher: ASME
Article Type: Research-Article
J. Heat Mass Transfer. October 2022, 144(10): 103501.
Paper No: HT-21-1791
https://doi.org/10.1115/1.4055116
Published Online: August 18, 2022
... perceptron (MLP) are applied to compare the present physics-aware MLP model based on different statistical indicators. The accuracy analyses reveal that the use of physical views to monitor the learning of neural networks shows better results with mean absolute percentage error (MAPE) = 0.7075%, root-mean...
View Article titled, An Expert Model Based on Physics-Aware <span class="search-highlight">Neural</span> <span class="search-highlight">Network</span> for the Prediction of Thermal Conductivity of Nanofluids
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Topics: Artificial neural networks, Nanofluids, Physics, Thermal conductivity, Temperature, Nanoparticles, Algorithms, Fluids, Machine learning
Journal Articles

Comparative Study on the Numerical Methods for View Factor Computation for Packed Pebble Beds: Back Propagation Neural Network Methods Versus Monte Carlo Methods

Quan Zou, Nan Gui, Xingtuan Yang, Jiyuan Tu, Shengyao Jiang
Journal: ASME Journal of Heat and Mass Transfer
Publisher: ASME
Article Type: Research-Article
J. Heat Mass Transfer. August 2021, 143(8): 083301.
Paper No: HT-20-1523
https://doi.org/10.1115/1.4051075
Published Online: June 2, 2021
... neural networks (FCNs) has been developed to realize the fast calculation of view factors. In order to verify the accuracy and effects of the networks, the neural networks are compared with the Monte Carlo (MC) algorithm. The results show that, in most cases, the relative errors of the FCN method can...
View Article titled, Comparative Study on the Numerical Methods for View Factor Computation for Packed Pebble Beds: Back Propagation <span class="search-highlight">Neural</span> <span class="search-highlight">Network</span> Methods Versus Monte Carlo Methods
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Topics: Artificial neural networks, Errors, Radiative heat transfer, Temperature
Includes: Supplementary data
Journal Articles

Network Modeling of Fin-and-Tube Evaporator Performance Under Dry and Wet Conditions

Ling-Xiao Zhao, Liang Yang, Chun-Lu Zhang
Journal: ASME Journal of Heat and Mass Transfer
Publisher: ASME
Article Type: Technical Briefs
J. Heat Mass Transfer. July 2010, 132(7): 074502.
https://doi.org/10.1115/1.4000950
Published Online: May 5, 2010
...Ling-Xiao Zhao; Liang Yang; Chun-Lu Zhang A new neural network modeling approach to the evaporator performance under dry and wet conditions has been developed. Not only the total cooling capacity but also the sensible heat ratio and pressure drops on both air and refrigerant sides are modeled...
View Article titled, Network Modeling of Fin-and-Tube Evaporator Performance Under Dry and Wet Conditions
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Topics: Artificial neural networks, Heat exchangers, Modeling, Refrigerants, Cooling, Physics, Temperature, Heat