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Special Topics & Reviews in Porous Media: An International Journal
ESCI SJR: 0.259 SNIP: 0.466 CiteScore™: 0.83

ISSN 印刷: 2151-4798
ISSN オンライン: 2151-562X

Special Topics & Reviews in Porous Media: An International Journal

DOI: 10.1615/SpecialTopicsRevPorousMedia.v3.i2.30
pages 115-123

PREDICTION OF EFFECTIVE THERMAL CONDUCTIVITY OF POLYMER COMPOSITES USING AN ARTIFICIAL NEURAL NETWORK APPROACH

Rajpal S. Bhoopal
Thermal Physics Laboratory, Department of Physics, University of Rajasthan, Jaipur 302 055, India
P. K. Sharma
Thermal Physics Laboratory, Department of Physics, University of Rajasthan, Jaipur 302 055, India
Sajjan Kumar
Thermal Physics Laboratory, Department of Physics, University of Rajasthan, Jaipur 302 055, India
Alok Pandey
Department of Electronics & Communication, Global Institute of Technology, Jaipur 302 022, India
R. S. Beniwal
CSIR-National Institute of Science Communication and Information Resources, New Delhi 110 012, India
Ramvir Singh
Thermal Physics Laboratory, Department of Physics, University of Rajasthan, Jaipur 302 055, India

要約

The effective thermal conductivity (ETC) of polymer composites is studied using artificial neural networks. Artificial neural networks are a form of artificial intelligence, which attempt to mimic the function of the human brain and nervous system. Artificial neural networks provide a great deal of promise but they suffer from a number of shortcomings, such as knowledge extraction, extrapolation, and uncertainty. This paper presents the use of the artificial neural network for prediction of ETC of metal-filled polymer composites due to their increasing importance in many fields of engineering applications and technological developments. Artificial neural networks models are based on a radial basis with the training function: the more efficient design radial basis network (NEWRB) and the feedforward backpropagation network with training functions conjugate gradient with Powell-Beale restarts, Levenberg-Marquardt, one-step secant, random order incremental, and resilient backpropagation. The volume fraction and thermal conductivity of continuous (matrix) and dispersed (filler) phases are input parameters to predict the ETC. The resultant predictions of ETC using the different models of artificial neural networks agree well with the available experimental data.