Abstract Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: An experimental study and a neural network simulating
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Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: An experimental study and a neural network simulating

机译:温度,浓度和剪切速率影响TiO2-MWCNT / 10W40杂交纳米杂交纳米流变的动力学行为特征:实验研究与神经网络模拟

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Abstract In this article, rheological behavior of TiO2-MWCNT (45–55%)/10w40 hybrid nano-oil was studied experimentally. The nano- oils were tested at temperature ranges of 5–55°C and in shear rates up to 11,997s?1. With respect to viscosity, shear stress and shear rate variations it was cleared that either of the base oil and nano-oil were non-Newtonian fluids. New equations which were based on thickness of the fluid were presented for different temperature values, R-squared values were between 0.9221 and 0.9998 (the precise of correlation changes depend on temperature). Also to predict the nano-oil behavior, neural network method was utilized. an artificial neural network (MLP type) were used to predict the viscosity in terms of temperature, solid volume fraction and shear stress. to compare the prediction precise of neural network and correlation the results of these two were compared with together. ANN showed more accurate results in comparison with correlation results. R2 and (MSE) were 0.9979 and 0.000016 respectively for the ANN. Highlights ?
机译:<![cdata [ 抽象 在本文中,实验研究了TiO2-MWCNT(45-55%)/ 10W40杂种纳米油的流变行为。在5-55 ℃的温度范围内测试纳米油,剪切速率高达11,997 s ?1 。关于粘度,剪切应力和剪切速度变化清楚地清除了基础油和纳米油中的任一个是非牛油液。基于流体厚度的新方程式呈现出不同的温度值,R角值在0.9221和0.9998之间(相关性变化的精确取决于温度)。另外,为了预测纳米油行为,利用神经网络方法。人工神经网络(MLP型)用于预测温度,固体体积分数和剪切应力方面的粘度。为了比较神经网络的预测精度和相关性将这两者的结果与在一起。随着相关结果,ANN显示更准确的结果。 R 2 和(MSE)分别为ANN分别为0.9979和0.000016。 突出显示

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