Suscripción a Biblioteca: Guest
Portal Digitalde Biblioteca Digital eLibros Revistas Referencias y Libros de Ponencias Colecciones
Journal of Porous Media
Factor de Impacto: 1.752 Factor de Impacto de 5 años: 1.487 SJR: 0.43 SNIP: 0.762 CiteScore™: 2.3

ISSN Imprimir: 1091-028X
ISSN En Línea: 1934-0508

Volumes:
Volumen 23, 2020 Volumen 22, 2019 Volumen 21, 2018 Volumen 20, 2017 Volumen 19, 2016 Volumen 18, 2015 Volumen 17, 2014 Volumen 16, 2013 Volumen 15, 2012 Volumen 14, 2011 Volumen 13, 2010 Volumen 12, 2009 Volumen 11, 2008 Volumen 10, 2007 Volumen 9, 2006 Volumen 8, 2005 Volumen 7, 2004 Volumen 6, 2003 Volumen 5, 2002 Volumen 4, 2001 Volumen 3, 2000 Volumen 2, 1999 Volumen 1, 1998

Journal of Porous Media

DOI: 10.1615/JPorMedia.v16.i7.10
pages 585-596

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

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
Ramvir Singh
Thermal Physics Laboratory, Department of Physics, University of Rajasthan, Jaipur 302 055, India
R. S. Beniwal
CSIR-National Institute of Science Communication and Information Resources, New Delhi 110 012, India

SINOPSIS

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.


Articles with similar content:

EXPERIMENTAL AND NUMERICAL INVESTIGATIONS OF EFFECTIVE THERMAL CONDUCTIVITY OF LOW-DENSITY POLYETHYLENE FILLED WITH Ni AND NiO PARTICLES
Composites: Mechanics, Computations, Applications: An International Journal, Vol.3, 2012, issue 1
Rajpal S. Bhoopal, Ramvir Singh, Dharmendra Tripathi, Madhusree Kole, T.K. Dey
PREDICTION OF THE MECHANICAL PROPERTIES OF COPPER POWDER-FILLED LOW-DENSITY POLYETHYLENE COMPOSITES. A COMPARISON BETWEEN THE ANN AND THEORETICAL MODELS
Composites: Mechanics, Computations, Applications: An International Journal, Vol.6, 2015, issue 1
P. K. Sharma, A. S. Luyt, Ramvir Singh, R. S. Bhoopal
PREDICTION OF EFFECTIVE THERMAL CONDUCTIVITY OF POLYMER COMPOSITES USING AN ARTIFICIAL NEURAL NETWORK APPROACH
Special Topics & Reviews in Porous Media: An International Journal, Vol.3, 2012, issue 2
P. K. Sharma, Rajpal S. Bhoopal, Ramvir Singh, R. S. Beniwal, Sajjan Kumar, Alok Pandey
EXPERIMENTAL EVALUATION AND ANN MODELING OF THERMAL CONDUCTIVITY OF AL2O3 NANOPARTICLES DISPERSED IN DIFFERENT BASE FLUIDS
Proceedings of the 24th National and 2nd International ISHMT-ASTFE Heat and Mass Transfer Conference (IHMTC-2017), Vol.0, 2017, issue
Dilip Singh Naruka, H. E. Patel, Pawan Kumar Singh, Prabhat Dansena
EXPERIMENTAL AND ANALYTICAL INVESTIGATIONS OF THERMOPHYSICAL PROPERTIES OF NANOFLUIDS
International Heat Transfer Conference 13, Vol.0, 2006, issue
Kai Choong Leong, Charles Yang, Sohel Murshed