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Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods

Ahmadi, M. H ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1016/j.colsurfa.2018.01.030
  3. Publisher: Elsevier B.V , 2018
  4. Abstract:
  5. Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The correlation coefficient values are very favorable and equal to 0.88125 and 0.87575 and 0.89999 by applying SOM, LM-BP algorithms and LSSVM, respectively. © 2018 Elsevier B.V
  6. Keywords:
  7. LSSVM ; Nanofluid ; Thermal conductivity ratio ; Alumina ; Backpropagation ; Backpropagation algorithms ; Conformal mapping ; Forecasting ; Nanofluidics ; Nanoparticles ; Neural networks ; Particle size ; Self organizing maps ; Support vector machines ; BP algorithm ; Correlation coefficient ; Least square support vector machines ; Levenberg Marquardt back propagation algorithms ; Nanofluids ; Volumetric concentrations ; Nanoparticle ; Unclassified drug ; Water ; Algorithm ; Article ; Artificial neural network ; Back propagation ; Genetic algorithm ; Human ; Human cell ; Least square analysis ; Least square support vector machine ; Mathematical analysis ; Nanotechnology ; Nerve cell ; Priority journal ; Support vector machine ; Temperature ; Thermal conductivity ; Volumetry
  8. Source: Colloids and Surfaces A: Physicochemical and Engineering Aspects ; Volume 541 , 2018 , Pages 154-164 ; 09277757 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0927775718300360