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首页> 外文期刊>Engineering Applications of Computational Fluid Mechanics >Numerical Analysis and Prediction of the Velocity Field in Curved Open Channel Using Artificial Neural Network and Genetic Algorithm
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Numerical Analysis and Prediction of the Velocity Field in Curved Open Channel Using Artificial Neural Network and Genetic Algorithm

机译:基于人工神经网络和遗传算法的弯曲明渠流速场数值分析与预测。

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AbstractThis paper presents numerical analysis and prediction of flow field in a 90° bend using Artificial Neural Networks (ANN) and Genetic Algorithm (GA). Firstly, a 3D Computational Fluid Dynamics (CFD) model is used to investigate the flow patterns and velocity profiles. Numerical simulation in two phases is done using the ANSYS-CFX software and k-ε turbulence model is used to solve turbulence equations. The results show secondary flow and centrifugal force influenced flow pattern and have good agreement with experimental data. Then two similar ANNs are trained based on GA and Back-Error Propagation (BEP) technique for velocity prediction in different sections of bend and their test results are compared with each other and with actual data. Since obtaining experimental data in every point of channel is not easy, ANN is used to obtain the velocity in some sections where experimental data are not available, and the results are compared with CFX’s result.
机译:摘要本文介绍了使用人工神经网络(ANN)和遗传算法(GA)进行的90°弯管流场的数值分析和预测。首先,使用3D计算流体动力学(CFD)模型研究流动模式和速度曲线。使用ANSYS-CFX软件在两个阶段进行了数值模拟,并使用k-ε湍流模型来求解湍流方程。结果表明,二次流和离心力影响了流型,与实验数据吻合良好。然后,基于遗传算法和反向误差传播(BEP)技术对两个相似的人工神经网络进行训练,以预测弯头不同部分的速度,并将它们的测试结果相互比较,并与实际数据进行比较。由于在通道的每个点获取实验数据都不容易,因此使用ANN来获取某些无法获得实验数据的区域的速度,并将结果与​​CFX的结果进行比较。

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