基于机器学习-CFD的明渠流速场预测模型

Prediction model for open channel velocity field based on machine learning-CFD

快速准确地预测渠道的断面流速场,对明渠的设计、维护和提高灌溉效率具有重要意义,而对于水位变化速度快、幅度大的渠道,实时预测其流速场更为困难。以都江堰人民渠总干渠为例,基于机器学习-CFD提出了一种构建实时流速场预测模型的新方法:首先,使用计算流体力学(CFD)对目标明渠断面进行模拟;然后,建立机器学习模型(SaDE-ELM),在SaDE-ELM中构建一个全连接的3层三输入一输出的神经网络,其隐藏层节点的参数通过流速场数据特征在策略库中自适应选择进化策略的差分进化算法进行计算,使用Moore-Penrose广义逆来计算该网络输出层权重;最后,使用CFD断面模拟数据训练SaDE-ELM。该SaDE-ELM模型训练完成后,只要输入该明渠的水位和断面任意点的位置坐标,即可输出该点的流速,以此得到明渠整个断面的流速场。结果表明,该模型的预测结果符合明渠流速分布的一般规律,且具有较高精度,可供类似工程参考。

Fast and accurate prediction of the cross-section velocity field of a channel is of great significance for the design and maintenance of the open channel and the improvement of irrigation efficiency.However,it is more difficult to predict the velocity field in real time for channels with fast and large water level changes.Taking the main canal of People s Canal in Dujiangyan Irrigation area as an example,a new method for real-time velocity field prediction was proposed.Firstly,the Computational Fluid Dynamics(CFD)was used to simulate the cross section flow filed of the target open channel.Then,a machine learning model SaDE-ELM was established,and a fully connected three-layer and three-input and one-output neural network was constructed by SaDE-ELM model.The parameters of the hidden layer nodes were calculated by the differential evolution algorithm that adaptively selects the evolutionary strategy in the strategy library according to the characteristics of the flow field data,and the Moore-Penrose generalized inverse was used to calculate the weight of the output layer of the network.Finally,SaDE-ELM model was trained by CFD cross-section simulation data.After the training is completed,as long as the water level of the open channel and the position coordinates of any point in the section are input,the velocity of the point can be output,and the velocity field of the whole section of the open channel can be obtained.The application practice shows that the prediction results of the model conform to the general law of flow velocity distribution in open channels with high accuracy,which can be used as a reference for similar projects.