基于A-RAFT模型的垂直管道输送测速方法

Vertical pipeline velocity measurement method based on A-RAFT model

流速作为流动特性的重要参数之一,在垂直管道提升效率研究中有着重要地位.以管道固液两相流为研究对象,研究管道测速的方法.结合深度学习技术,提出了基于注意力机制的光流全场递归匹配模型(A-RAFT),提高了网络对速度场突变区域的估计能力;构建了一个虚实结合的数据集,用于训练神经网络模型.对新提出的模型与数据集进行评估,结果显示:该模型在合成的图像上实现了高精度的速度场计算,与现有其他模型相比,估计误差减小了15.6%;开展了垂直管道固体颗粒输送的模拟实验,本模型在实验中所采集的真实流场数据上同样展现了准确的估计性能,该模型对颗粒速度的测量平均相对误差低于5%.上述实验结果充分证明了该方法在速度场中有较高的估计精度,模型有较强的泛化能力.这一研究能够为能源开采、隧道掘进、污水处理以及长距离管道运输等领域中的固液两相流特性分析提供新思路.

As one of the important parameters of flow characteristics,flow velocity occupies an important position in the study of vertical pipeline lifting efficiency.To more accurately measure flow velocity and reveal the flow dynamics of vertical pipeline conveying systems,we focus on the solid–liquid two-phase flow in pipelines.In this paper,we study the method of pipeline velocity measurement and reveal the flow characteristics of the pipeline system.First,we use a high-speed camera to transform the flow velocity measurement into a computer vision problem,and combine the computer vision problem with deep learning technology to propose an A-RAFT(attention-based recurrent all-pairs field transforms)neural network model based on the attention mechanism.The model uses a convolutional layer to extract feature information and reduces the computational load through a pooling layer.Additionally,we introduce a correlation layer to perform inter-correlation operations on the feature information and calculate pixel displacement.In this process,the attention mechanism focuses on regions with flow velocity changes,enhancing the ability of the network to estimate velocity field variations.This helps the model better select and focus on key features in the input data,providing more accurate feature information for matching.Consequently,the estimation accuracy of the model is improved,particularly for the boundary regions of solid particles in solid–liquid two-phase flow.The model also effectively estimates flow rates for particles of varying shapes and sizes,with enhanced overall performance and accuracy.In addition,this paper constructs a combined real and virtual dataset for training the neural network model.The dataset is based on nine types of classical single-phase flow field data,and real particle texture information is fused into the dataset through real experiments to enhance data diversity.This dataset effectively simulates the optical flow changes of the pixels in the front and back frames in real experiments.The proposed model is evaluated with this dataset,and the results show that the model achieves high-precision velocity field computation on synthetic images,and the estimation error is 15.6%lower than those of other existing models.In the simulation experiments of solid particle transportation in vertical pipelines,the proposed model demonstrates accurate estimation performance on the collected real flow field data,with relative errors of lower than 5%for the measurement of particle velocities.These errors are derived from comparisons with the true values.The results validate the method in terms of both estimation accuracy and the generalization ability of the model.This study can provide new insights for solid–liquid two-phase flow characterization in energy extraction,tunneling,wastewater treatment,and long-distance pipeline transportation.