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Experimental study for predicting the specific heat of water based Cu‐Al2O3 hybrid nanofluid using artificial neural network and proposing new correlation
International Journal of Energy Research ( IF 4.6 ) Pub Date : 2020-05-02 , DOI: 10.1002/er.5417
A. Batur Çolak 1 , Oğuzhan Yıldız 1 , Mustafa Bayrak 1 , Bekir S. Tezekici 2
Affiliation  

In this study, an artificial neural network model has been created in order to estimate the specific heat of Cu‐Al2O3/water hybrid nanofluid based on temperature (T) and volume concentration (φ). Specific heat values of the Cu‐Al2O3/water hybrid nanofluid prepared in five‐volume concentration were measured experimentally in the 20°C to 65°C temperature range. The dataset was reserved into three primary parts, with the inclusion of 901 (70%) for the training, 257 (20%) for the test and 129 (10%) for the validation. As a result of comparison with experimental values, it is concluded that this model predicts specific heat with R‐value of 0.99994 and an average relative error of approximately 5.84e‐9. In addition, a mathematical correlation has been developed to estimate the specific heat of the Cu‐Al2O3/water hybrid nanofluid. The data acquired from the mathematical correlation, developed, were in great correlation with all the experimental values with an average deviation of −0.005%. This result has revealed that the developed mathematical correlation is an ideal design for estimating the specific heat of the Cu‐Al2O3/water hybrid nanofluid.

中文翻译:

利用人工神经网络预测水基Cu‐Al2O3杂化纳米流体比热的实验研究并提出新的相关性

在这项研究中,已经建立了一个人工神经网络模型,以便根据温度(T)和体积浓度(φ)估算Cu‐Al 2 O 3 /水杂化纳米流体的比热。Cu‐Al 2 O 3的比热值在20°C至65°C温度范围内通过实验测量了以五倍浓度制备的水/水杂化纳米流体。该数据集被保留为三个主要部分,其中包括用于训练的901(70%),用于测试的257(20%)和用于验证的129(10%)。与实验值进行比较的结果是,可以得出结论,该模型预测的比热R值为0.99994,平均相对误差约为5.84e-9。此外,已经开发出一种数学相关性来估算Cu‐Al 2 O 3的比热/水混合纳米流体。从数学相关性获得的数据与所有实验值均具有高度相关性,平均偏差为-0.005%。该结果表明,已开发的数学相关性是估算Cu‐Al 2 O 3 /水杂化纳米流体比热的理想设计。
更新日期:2020-05-02
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