Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data

  • Mohammad Hemmat Esfe Faculty of Mechanical Engineering, Semnan University, Semnan, Iran

Abstract

In this article, thermal conductivity data of aqueous nanofluids of CuO have been modeled through one of the instruments of empirical data modeling. The input data of 5 different volume fractions of nanofluid obtained in four temperatures through experiments have been considered as network inputs. Also, triangular function, due to providing the best responses, has been used as membership function in ANFIS structure. The modeling results show that fuzzy networks are able to model thermal conductivity results of nanofluids with good precision. Regression coefficient of this modeling has been 0.99.

Keywords

nanofluids, fuzzy networks, thermal conductivity, ANFIS
Published
16-03-2017
How to Cite
HEMMAT ESFE, Mohammad. Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data. Periodica Polytechnica Chemical Engineering, [S.l.], mar. 2017. ISSN 1587-3765. Available at: <https://pp.bme.hu/ch/article/view/9670>. Date accessed: 26 sep. 2017. doi: https://doi.org/10.3311/PPch.9670.
Section
Articles