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首页> 外文期刊>Case Studies in Thermal Engineering >Addition of MWCNT-Al2O3 nanopowders to water- ethylene glycol (EG) base fluid for enhancing the thermal characteristics: Design an optimum feed-forward neural network
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Addition of MWCNT-Al2O3 nanopowders to water- ethylene glycol (EG) base fluid for enhancing the thermal characteristics: Design an optimum feed-forward neural network

机译：加入MWCNT-AL2O3纳米粉粉至含水 - 乙二醇（例如）基础流体，用于增强热特性：设计最佳前馈神经网络

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Prediction the thermal conductivity of nanofluids has been subject of many researches. Artificial Neural Networks are used to obtain thermal conductivity of NAnofluids because not only this method is fast and acurate but also it can reduce the Lab costs. To predict the thermal conductivity of water- EG/MWCNT-Al2O3 hybrid nanofluid (knf ) a feed-forward neural network with different neuron numbers has been tested and the best network based on the performance is selected. The Levenberg Marquardt algorithm is used for training the network, which is one of the best algorithms in machine learning. Also, using a fitting method, a surface is used to illustrate the behavior of nanofluids based on the volume fraction of nanoparticles (?) and temperature (T). ? = 0, 0.001, 0.002, 0.004, 0.008, 0.0016 and T = 25, 30, 35, 40, 45, 50 °C are used.. The obtained results show that the ANN and Fitting results are close to the experimental datapoints, and both methods can predict knf accurately. As the results of these methods are very close, but the ANN method is better in predicting the behavior of this nanofluid.
机译：预测纳米流体的导热率是许多研究的主题。人工神经网络用于获得纳米流体的导热率，因为不仅该方法是快速和刺曲的，而且还可以降低实验室成本。为了预测水 - 例如/ MWCNT-AL2O3杂交纳米流体（KNF）的导热率已经测试了具有不同神经元数的前馈神经网络，并且选择了基于性能的最佳网络。 Levenberg Marquardt算法用于培训网络，这是机器学习中最好的算法之一。此外，使用配合方法，表面用于说明基于纳米颗粒（α）和温度（T）的体积分数的纳米流体的行为。还使用= 0，0.001,0.001，0.004,0.008,00016和T = 25,30,35,40,45,50°C。获得的结果表明，ANN和拟合结果接近实验数据点，以及两种方法都可以准确地预测KNF。随着这些方法的结果非常接近，但是ANN方法更好地预测该纳米流体的行为。

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