Abstract

Applying nanofluids in energy-related technologies and thermal mediums can lead to remarkable enhancement in their efficiency and performance due to their modified thermophysical properties. Among thermophysical properties, thermal conductivity (TC) performs principal role in heat transfer ability of nanofluids. Artificial neural networks (ANNs) have shown promising performance in modeling nanofluids’ TC. In this article, two types of ANNs are used for estimating TC of nanofluids with TiO₂ nanoparticles. In this regard, effective factors including particle size, temperature, volume fraction of solid particles and TC of the base fluids are applied at the input of the model. Based on the comparison between the estimated data and the corresponding actual ones, it is concluded that employing multi-layer perceptron (MLP) is superior compared with group method of data handling (GMDH). In the optimal conditions of the networks, the R-squared value of the models based on both MLP and GMDH was 0.999. Moreover, average absolute relative deviations of the mentioned models were around 0.23% and 0.32%, respectively.

中文翻译：

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用人工神经网络估算含 TiO2 纳米颗粒的纳米流体的热导率

摘要

将纳米流体应用于能源相关技术和热介质可以显着提高其效率和性能，因为它们具有改进的热物理特性。在热物理特性中，热导率 (TC) 在纳米流体的传热能力中起主要作用。人工神经网络 (ANN) 在模拟纳米流体的 TC 方面表现出良好的性能。在本文中，两种类型的人工神经网络用于估计含 TiO ₂的纳米流体的 TC纳米粒子。在这方面，包括颗粒大小、温度、固体颗粒的体积分数和基础流体的 TC 在内的有效因素应用于模型的输入。基于估计数据与相应实际数据之间的比较，得出的结论是，与数据处理组方法（GMDH）相比，采用多层感知器（MLP）具有优越性。在网络的最佳条件下，基于 MLP 和 GMDH 的模型的 R 平方值为 0.999。此外，上述模型的平均绝对相对偏差分别约为 0.23% 和 0.32%。