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首页> 外文期刊>European Physical Journal Plus >Application of an artificial neural network to predict the entrance length of three-dimensional magnetohydrodynamics channel flow
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Application of an artificial neural network to predict the entrance length of three-dimensional magnetohydrodynamics channel flow

机译：人工神经网络的应用预测三维磁流体动力学通道流量的入射长度

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In this paper, a feed-forward, back-propagation neural network was employed for modeling the three-dimension magnetohydrodynamics (MHD) developing fluid flow. The aim of the study is to obtain a correlation for calculating the entrance length by applying an artificial neural network (ANN). To collect the data for training ANN, the numerical finite volume method (FVM) was conducted. The data were collected including Reynolds number (Re) ranging from 500 to 1000 and Hartmann number (Ha) ranging from 4 to 14 for a three-dimensional rectangular channel with four different aspect ratios (AR in a three-layer ANN for modeling the flow and computing the entrance length as a function of AR , Re and Ha. The results obtained from the ANN, FVM and the proposed correlations were compared and it was observed that the variation of the entrance length was different for each AR . Thus, two correlations were proposed with the different range of the AR and Ha. The contours and vectors of the velocity along the channel direction and for different cross-sections were illustrated. In addition, the effect of AR and Ha on the entrance length and pressure loss was presented.
机译：本文采用了前馈回传播神经网络，用于建模三维磁流体动力学（MHD）显影流体流动。该研究的目的是通过应用人工神经网络（ANN）来获得计算入射长度的相关性。要收集培训ANN数据，进行了数值有限体积法（FVM）。收集数据，包括从500到1000和hartmann编号（ha）的reynolds编号（Re），范围为4至14，对于三维矩形通道，具有四个不同的纵横比（用于建模流的三层ANN中的AR并将入口长度计算为AR，RE和HA的函数。比较了从ANN，FVM和所提出的相关结果获得的结果，观察到每个AR的入射长度的变化不同。因此，两个相关性用不同的AR和HA的范围提出。示出了沿沟道方向和不同横截面的速度的轮廓和载体。此外，还提出了AR和HA对入口长度和压力损失的影响。

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