Modeling thermal conductivity of ethylene glycol-based nanofluids using multivariate adaptive regression splines and group method of data handling artificial neural network

Abstract

Augmenting the thermal conductivity (TC) of fluids makes them more favorable for thermal applications. In this regard, nanofluids are suggested for achieving improved heat transfer owing to their modified TC. The TC of the base fluid, the volume fraction and mean diameter of particles, and the temperature are the main elements influencing the TC of nanofluids. In this article, two approaches, namely multivariate adaptive regression splines (MARS) and group method of data handling (GMDH), are applied for forecasting the TC of ethylene glycol-based nanofluids containing SiC, Ag, CuO, $\textrm{Si}{\textrm{O}_2} $, $\textrm{A}{\textrm{l}_2}{\textrm{O}_3} $ and MgO particles. Comparison of the data forecast by the models with experimental values shows a higher level of confidence in GMDH for modeling the TC of these nanofluids. The ${R^2} $ values determined using MARS and GMDH for modeling are 0.9745 and 0.9332, respectively. Moreover, the importance of the inputs is ranked as volume fraction, TC of the solid phase, temperature and particle dimensions.

Keywords

• nanofluid
• gmdh
• mars
• thermal conductivity
• artificial neural network