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首页> 外文期刊>Heat transfer >Study of flow field, heat transfer, and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics-artificial neural network hybrid method
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Study of flow field, heat transfer, and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics-artificial neural network hybrid method

机译：利用计算流体动力学 - 人工神经网络杂交方法研究纳米流体湍流自然对流流域，传热和熵生成的研究

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摘要

In this study, the turbulent natural convection of Ag-water nanofluid in a tall, inclined enclosure has been investigated. The main objective of this study is finding the optimized angle of the enclosure with operational boundary condition in cooling from ceiling utilizing the computational fluid dynamics-artificial neural network (CFD-ANN) hybrid method, which has not been noticed in previous studies. To achieve this, we proposed two approaches. First, the simulations have been done with a deviation angle of 0 to 90° by using water and Ag-water nanofluid. And second, a new prediction approach is proposed based on radial basis function artificial neural networks (RBF-ANN) to predict the mean Nusselt number and entropy generation with the variation of Rayleigh numbers, deviation angles, and volume fractions as inputs. The results from the first approach indicate that the Rayleigh number has a considerable function in the determination of optimized angle. The results from the second approach, which used the first approach simulation results as training data set, could predict the mean Nusselt number and entropy generation with 1.4577e~(-022) and 1.552e~(-015) mean square error, respectively. Moreover, a new set of data for Rayleigh numbers, deviation angles, and volume fractions were used to test the performance of the prediction model, which shows promising and superior prospects for RBF-ANN.
机译：在这项研究中，研究了高倾斜外壳中Ag水纳米流体的湍流自然对流。本研究的主要目的是利用计算流体动力学 - 人工神经网络（CFD-ANN）混合方法，在天花板冷却中找到了具有操作边界条件的优化角度，该方法在先前的研究中尚未注意到。为实现这一目标，我们提出了两种方法。首先，通过使用水和Ag-水纳米流体，使用0至90°的偏差角来进行模拟。其次，基于径向基函数人工神经网络（RBF-ANN）提出了一种新的预测方法，以预测瑞利数，偏差角和体积分数的变化作为输入的平均良好的营养数和熵生成。第一方法的结果表明瑞利数在确定优化角度时具有相当大的功能。第二种方法的结果，其使用第一接近模拟结果作为训练数据集，可以预测平均营养数和熵生成，分别为1.4577e〜（-022）和1.552e〜（-015）均方误差。此外，用于瑞利数，偏差角和体积分数的一组新数据来测试预测模型的性能，这对于RBF-ANN表示有前途和优越的前景。

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