Optimization of density and coefficient of thermal expansion of MWCNT in thermal oil nanofluid and modeling using MLP and response surface methodology. (May 2023)

• Record Type: Journal Article
• Authors: Esfe, Mohammad Hemmat; Motallebi, Seyed Majid; Hatami, Hossein; Amiri, Mahmoud Kiannejad; Esfandeh, Saeed; Toghraie, Davood
• Is Part Of: Tribology international. Volume 183(2023)
• Publication Date: 2023-05
• Subjects: Nanofluid -- Optimization -- RSM -- MLP -- MWCNT -- Thermal oil -- Thermal expansion; Tribology -- Periodicals; 621.89
• DOI: 10.1016/j.triboint.2023.108410
• Languages: English
• ISSNs: 0301-679X

Abstract

Abstract: In this study, the rheological properties including viscosity, thermal conductivity (TC), density and coefficient of thermal expansion of MWCNT nanofluid based on Thermal Oil (THO) were investigated. The laboratory study was carried out by a two-step method and at a solid volume fraction of SVF= 0.1–1% and a temperature of T = 20 °C. RSM and MLP intelligent methods were used for modeling and optimization. The convergence presented for TC, viscosity, density and thermal expansion (TE) coefficient have a convergence coefficient of 0.9961, 0.9969, 0.9999 and 0.9999, respectively, which shows the high accuracy of the relations. The results show that increasing the SVF increases the TC, viscosity and density of NFs and decreases the TE coefficient of NFs. Increasing temperature increases TC and TE coefficient and decreases the viscosity and density of NF. The MLP structure was obtained with two hidden layers Tansig and Logsig and with convergence coefficients of 0.9980, 0.9449 and 1 respectively for TC, viscosity, density and TE coefficient. For viscosity and density, both RSM and ANN models show high accuracy, but the MLP model for TC and RSM model for TE coefficient had higher accuracy. Graphical Abstract: ga1 Highlights: RSM is applied to find the optimal density and thermal expansion of nanofluid. New correlations of thermophysical properties has been developed by RSM. MLP is employed to predict thermophysical properties of nanofluid. Increasing the weight fraction of nanoparticles increases the Thermal Conductivity. Most optimum case is at 60 °C and 0% weight fraction with density of 826.61 (Kg/m 3 )