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首页> 外文期刊>Physica Scripta: An International Journal for Experimental and Theoretical Physics >Modeling electrical properties of nanofluids using artificial neural network
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Modeling electrical properties of nanofluids using artificial neural network

机译：使用人工神经网络建模纳米流体的电性能

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摘要

This paper presents a theoretical study of the electrical properties of two different samples of nanofluids (MgO and Si-TiO2 nanoparticles in ethylene glycol EG as the base fluid) using an artificial neural network (ANN) model. Experimental data were extracted from previous experimental studies and used as inputs. A learning ANN method was applied based on the back propagation technique. The optimal network structure, which produces the most acceptable performance, was attained. Electrical conductivity and permittivity in terms of nanoparticle concentration, temperature and frequency were simulated and predicted using the ANN model. A new nonlinear equation describing the behavior of electrical properties of nanofluids was obtained. The simulation results of the ANN model are highly accurate in comparison with experimental data. Predictions of values that are not involved in the experimental data range were carried out and provide excellent results. The mean squared error and regression coefficient were also determined. Their values support the success of the ANN model. The main purpose of the paper is to show the ability and power of an ANN model in estimating and predicting electrical properties of nanofluids. According to this research, the ANN model can be utilized as an efficient tool to predict the electrical properties of nanofluids. It can also achieve great links between practical and theoretical branches.
机译：本文介绍了使用人工神经网络（ANN）模型的两种不同样品的纳米流体（MgO和Si-TiO2纳米颗粒中的乙二醇中的Si-TiO2纳米颗粒中的两种不同样品的理论研究。从先前的实验研究中提取实验数据并用作输入。基于后传播技术应用了学习ANN方法。获得了最佳的网络结构，其产生最可接受的性能。在纳米颗粒浓度，温度和频率方面，使用ANN模型进行电导率和介电常数。获得了描述纳米流体的电性能行为的新非线性方程。与实验数据相比，ANN模型的仿真结果非常准确。进行了不参与实验数据范围的值的预测，并提供了优异的结果。还确定了平均平方误差和回归系数。他们的价值观支持ANN模型的成功。本文的主要目的是展示ANN模型在估计和预测纳米流体的电性能方面的能力和力量。根据该研究，ANN模型可用作预测纳米流体的电性能的有效工具。它还可以在实际和理论分支之间实现很大的联系。

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