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APPLICABILITY OF ARTIFICIAL NEURAL NETWORKS TO PREDICT EFFECTIVE THERMAL CONDUCTIVITY OF HIGHLY POROUS METAL FOAMS

Volume 16, Issue 7, 2013, pp. 585-596
DOI: 10.1615/JPorMedia.v16.i7.10
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ABSTRACT

This paper presents the applicability of artificial neural networks to predict effective thermal conductivity of highly porous metal foams. Artificial neural network models are based on feedforward backpropagation network with training functions such as gradient descent (GD), gradient descent with adaptive learning rate (GDA), gradient descent with momentum (GDM), gradient descent with momentum and adaptive learning rate (GDX), and scaled conjugate gradient (SCG). Volume fraction of fluid phase and thermal conductivity of solid and fluid phases are input parameters for the artificial neural network to predict the effective thermal conductivity. The training algorithm for neurons and hidden layers for different feedforward backpropagation networks runs at the uniform threshold function TANSIG-PURELIN for 500 epochs. Better agreement of predicted effective thermal conductivity values is obtained by using artificial neural networks with the experimental results. A comparison with other models is also made and it is found that the values of effective thermal conductivity predicted by using the present model are in good agreement with the reported experimental values.

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  1. Hemmat Esfe Mohammd, Wongwises Somchai, Naderi Ali, Asadi Amin, Safaei Mohammad Reza, Rostamian Hadi, Dahari Mahidzal, Karimipour Arash, Thermal conductivity of Cu/TiO2–water/EG hybrid nanofluid: Experimental data and modeling using artificial neural network and correlation, International Communications in Heat and Mass Transfer, 66, 2015. Crossref

  2. Aghaei Alireza, Khorasanizadeh Hossein, Sheikhzadeh Ghanbar Ali, Measurement of the dynamic viscosity of hybrid engine oil -Cuo-MWCNT nanofluid, development of a practical viscosity correlation and utilizing the artificial neural network, Heat and Mass Transfer, 54, 1, 2018. Crossref

  3. Baghban Alireza, Pourfayaz Fathollah, Ahmadi Mohammad Hossein, Kasaeian Alibakhsh, Pourkiaei Seyed Mohsen, Lorenzini Giulio, Connectionist intelligent model estimates of convective heat transfer coefficient of nanofluids in circular cross-sectional channels, Journal of Thermal Analysis and Calorimetry, 132, 2, 2018. Crossref

  4. Vakili Masoud, Karami Maryam, Delfani Shahram, Khosrojerdi Soheila, Kalhor Koosha, Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions, Journal of Thermal Analysis and Calorimetry, 129, 2, 2017. Crossref

  5. Baghban Alireza, Sasanipour Jafar, Pourfayaz Fathollah, Ahmadi Mohammad Hossein, Kasaeian Alibakhsh, Chamkha Ali J., Oztop Hakan F., Chau Kwok-wing, Towards experimental and modeling study of heat transfer performance of water- SiO2 nanofluid in quadrangular cross-section channels, Engineering Applications of Computational Fluid Mechanics, 13, 1, 2019. Crossref

  6. Maaoui Walaeddine , Lazhar Ramzi , Najjari Mustapha , SOIL MOISTURE RETRIEVAL MODEL BASED ON DIELECTRIC MEASUREMENTS AND ARTIFICIAL NEURAL NETWORK , Journal of Porous Media, 25, 8, 2022. Crossref

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