Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks

Azadeh Gholami; Hossein Bonakdari; Amir Hossein Zaji; Ali Akbar Akhtari

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Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks

机译：使用计算流体力学和人工神经网络模拟明渠弯管特性

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The ability to simulate flow characteristics is one of the most important issues in the design and application of open channel bends. Three-dimensional computational fluid dynamics (CFD) and multi-layer feed-forward artificial neural networks (MLFF-ANNs) are used and compared in modeling the flow depth and velocity field in sharp bends. CFD is modeled in two phases, water and air, using the volume of fluid method. The backpropagation algorithm is applied in the training process of the ANN model. An experimental study of a 90° curved channel is undertaken to verify and compare the efficiency of the CFD and ANN models. The results show that both CFD and ANN methods can be successfully applied to the modeling of open channel bend characteristics. However, the ANN method performs significantly better than CFD.

机译：模拟流动特性的能力是明渠弯头设计和应用中最重要的问题之一。使用三维计算流体动力学（CFD）和多层前馈人工神经网络（MLFF-ANN）进行比较，以对急弯中的流动深度和速度场进行建模。 CFD使用流体体积法在水和空气两个阶段建模。反向传播算法被应用于神经网络模型的训练过程中。进行了90°弯曲通道的实验研究，以验证和比较CFD和ANN模型的效率。结果表明，CFD和ANN方法都可以成功地应用于明渠弯曲特性的建模。但是，ANN方法的性能明显优于CFD。

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《Engineering Applications of Computational Fluid Mechanics》 |2015年第1期|共15页
作者
Azadeh Gholami; Hossein Bonakdari; Amir Hossein Zaji; Ali Akbar Akhtari;
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中图分类工程力学;
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Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks

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