Viscosity prediction by computational method and artificial neural network approach: The case of six refrigerants
Viscosity prediction by computational method and artificial neural network approach: The case of six refrigerants
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DOI：
10.1016/j.supflu.2013.04.017
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发表时间：
2013-09-01
影响因子：
3.9
通讯作者：
Ghaderi, Noushin
中科院分区：
文献类型：
--
作者：
Ghaderi, Forouzan;Ghaderi, Amir Hosein;Ghaderi, Noushin
流体粘度计算有一些计算模型。然而，这些模型在有限密度下都是可靠的。在这个比较研究中，评估了两种方法在所有密度范围内的粘度预测。我们确定了每个模型的有效性，并展示了它们的优缺点。采用基于Chapman-Enskog和Rainwater-Friend理论的计算模型计算了六种制冷剂的粘度。然后将多层感知器前馈人工神经网络（ANN）用于粘度预测，最后对计算模型和人工神经网络两种预测方法进行了比较。从低密度到高密度计算黏度的方法尚无定论。结果表明，计算模型在低、中密度下的预测精度与人工神经网络方法相当。然而，人工神经网络在高密度下具有很好的精度，而当密度大于8时，计算方法就失败了。爱思唯尔B.V.版权所有
There are some computational models for fluids viscosity calculation. However, each of these models is reliable in confined density. In this comparative study two methods are evaluated for viscosity prediction in all range of density. We determine the effectiveness of each of the models and we demonstrate the strengths and weaknesses of them. Viscosity of the six refrigerants is calculated by some computational models based on Chapman-Enskog and Rainwater-Friend theories. Then a feed forward artificial neural network (ANN) with multilayer perceptrons is used to viscosity prediction and finally two methods (computational models and artificial neural network) are comparing. It is concluded that there is no opinion by computational methods to calculate viscosity from low to high density. The results show that prediction accuracy of computational models in low and moderate densities is good as ANN method. However artificial neural network has very good accuracy in high densities while computational method is defeated when the density is more than 8. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.