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Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al_2O_3-water nanofluids using experimental data

机译:人工神经网络和非线性回归在使用实验数据预测Al_2O_3-水纳米流体导热系数模型中的适用性

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摘要

In the present study, the thermal conductivity of Al_2O_3-water nanofluid at different temperatures and solid volume fractions has been modeled by artificial neural network (ANN) and correlation using experimental data. The thermal conductivity of the nanofluids at different fluid temperatures, ranging from 26 to 55 ℃ is employed as training data for ANN. Furthermore, based on the experimental data and using artificial neural network, a correlation for modeling the thermal conductivity of the nanofluid in terms of temperature and solid volume fraction is proposed. The results show that the proposed correlation has good ability for predicting the thermal conductivity of the nanofluids. On the other hand, the ANN model shows excellent agreement with the results of the experimental data.
机译:在本研究中,已通过人工神经网络(ANN)并利用实验数据对Al_2O_3-水纳米流体在不同温度和固体体积分数下的热导率进行了建模。将纳米流体在26至55℃的不同流体温度下的热导率用作ANN的训练数据。此外,基于实验数据并使用人工神经网络,提出了一种用于根据温度和固体体积分数对纳米流体的热导率进行建模的相关性。结果表明,所提出的相关性具有良好的预测纳米流体热导率的能力。另一方面,人工神经网络模型与实验数据的结果显示出极好的一致性。

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