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, $\text{Si}{\text{O}}_2$, $\text{A}{\text{l}}_2{\text{O}}_3$ 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.

增强流体的导热系数（TC）使它们更适合热应用。在这方面，由于其改性的TC，建议纳米流体实现改善的热传递。基础流体的TC，颗粒的体积分数和平均直径以及温度是影响纳米流体TC的主要因素。本文采用多元自适应回归样条（MARS）和数据处理组方法（GMDH）这两种方法来预测包含SiC，Ag，CuO，$\text{硅}{\text{Ø}}_2$， $\text{一种}{\text{升}}_2{\text{Ø}}_3$和MgO颗粒。通过模型预测的数据与实验值的比较表明，GMDH对这些纳米流体的TC建模具有较高的置信度。的${\mathrm{[R}}^2$使用MARS和GMDH确定的建模值分别为0.9745和0.9332。此外，输入的重要性按体积分数，固相TC，温度和颗粒尺寸排名。