Artificial Neural Network Trained, One Dimensional FEM Model to Predict Two Phase Flow Characteristics in Mini/Micro Channels

Pradeep Hegde; M.Z. Abdullah; A.Y. Hassan; K.N. Seetharamu

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Artificial Neural Network Trained, One Dimensional FEM Model to Predict Two Phase Flow Characteristics in Mini/Micro Channels

机译：经过人工神经网络训练的一维有限元模型，可预测微通道/微通道中的两相流特性

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A one dimensional Finite element method (FEM) model is used to analyze two phase flow in mini/micro channels. The model is verified by comparing the results with those obtained by popular mini/micro channel correlations. Flow parameters such as the pressure drop, two phase friction multiplier void fraction and slip ratio are determined for different inlet pressures and different coolant mass fluxes. The results obtained from the model are subsequently used to train the artificial neural network (ANN). The trained artificial neural network can directly yield the two phase flow parameters and thus the iterative process is avoided.

机译：一维有限元方法（FEM）模型用于分析微型/微型通道中的两相流。通过将结果与通过流行的迷你/微通道相关性获得的结果进行比较来验证模型。针对不同的入口压力和不同的冷却剂质量通量，确定了流量参数，例如压降，两相摩擦系数空隙率和滑移率。从模型中获得的结果随后用于训练人工神经网络（ANN）。经过训练的人工神经网络可以直接得出两相流参数，从而避免了迭代过程。

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来源
《International Journal of Heat Exchangers》 |2007年第1期|p.1-14|共14页
作者
Pradeep Hegde; M.Z. Abdullah; A.Y. Hassan; K.N. Seetharamu;
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作者单位

P.E.S. Institute of Technology, Bangalore INDIA;

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原文格式 PDF
正文语种 eng
中图分类热力工程、热机;
关键词
neural networks; one dimensional FEM model; two phase flow; two phase friction multiplier; void fraction;

机译：神经网络;一维有限元模型;两相流;两相摩擦系数;空隙率;

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Artificial Neural Network Trained, One Dimensional FEM Model to Predict Two Phase Flow Characteristics in Mini/Micro Channels

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