Abstract The alumina, copper oxide and zinc oxide nanoparticles (40 nm) were used to prepare the distilled water based nanofluids. The particle weight concentration varies in the range of 0.02% to 2%. The thermal conductivities were measured in the range of 20 °C to 90 °C. The input data for the present artificial neural network (ANN) model were nanoparticle weight fraction and nanofluid temperature and the output data was thermal conductivity of the nanofluid. The ANN used one hidden layer and it was optimised by varying number of neurons. The statistical approach has been employed to find out the coefficients in the proposed correlation using ANN validated experimental results. The estimated data obtained by the ANN model is in good agreement with the experiments. The proposed theoretical correlation is able to find out thermal conductivity ratio of nanofluids in a wide range of particle concentrations and temperatures.

中文翻译：

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基于实验研究的纳米流体独特的热导率模型 (ANN)

摘要 采用氧化铝、氧化铜和氧化锌纳米粒子（40 nm）制备了蒸馏水基纳米流体。颗粒重量浓度在 0.02% 到 2% 的范围内变化。在 20°C 至 90°C 的范围内测量热导率。本人工神经网络 (ANN) 模型的输入数据是纳米颗粒重量分数和纳米流体温度，输出数据是纳米流体的热导率。人工神经网络使用一个隐藏层，并通过不同数量的神经元进行优化。统计方法已被用于使用人工神经网络验证的实验结果找出所提出的相关性中的系数。人工神经网络模型得到的估计数据与实验吻合良好。