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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.

Precise measurement of flow velocity in microfluidic channels is of importance in microfluidic applications, such as quantitative chemical analysis, sample preparation and drug synthesis. However, simple approaches for quickly and precisely measuring the flow velocity in microchannels are still lacking. Herein, we propose a deep neural networks assisted scalar imaging velocimetry (DNN-SIV) for quick and precise extraction of fluid velocity in a shallow microfluidic channel with a high aspect ratio, which is a basic geometry for cell culture, from a dye concentration field with spatiotemporal gradients. DNN-SIV is built on physics-informed neural networks and residual neural networks that integrate data of scalar field and physics laws to determine the height-averaged velocity. The underlying enforcing physics laws are derived from the Navier–Stokes equation and the scalar transport equation. Apart from this, dynamic concentration boundary condition is adopted to improve the velocity measurement of laminar flow with small Reynolds number in microchannels. The proposed DNN-SIV is validated and analyzed by numerical simulations. Compared to integral minimization algorithm used in conventional SIV, DNN-SIV is robust to noise in the measured scalar field and more efficiently allowing real-time flow visualization. Furthermore, the fundamental significance of rational construction of concentration field in microchannels is also underscored. The proposed DNN-SIV in this paper is agnostic to initial and boundary conditions that can be a promising velocity measurement approach for many potential applications in microfluidic chips, although its performance remains to be experimentally validated. [ABSTRACT FROM AUTHOR]

There are few methods to provide high-resolution in-situ characterization of flow in aquifers and reservoirs. We present a method that has the potential to quantify lateral and vertical (magnitude and direction) components of flow with spatial resolution of about one meter and temporal resolution of about one day. A fiber optic distributed temperature sensor is used with a novel heating system. Temperatures before heating may be used to evaluate background geothermal gradient and vertical profile of thermal diffusivity. The innovation presented is the use of variable energy application along the well, in this case concentrated heating at equally-spaced (2 m) localized areas (0.5 m). Relative to uniform warming this offers greater opportunity to estimate water movement, reduces required heating power, and increases practical length that can be heated. Numerical simulations are presented which illustrate expected behaviors. We estimate relative advection rates near the well using the times at which various locations diverge from a heating trajectory expected for pure conduction in the absence of advection. The concept is demonstrated in a grouted 600 m borehole with 300 heated patches, though evidence of vertical water movement was not seen. [ABSTRACT FROM AUTHOR]

There are few methods to provide high-resolution in-situ characterization of flow in aquifers and reservoirs. We present a method that has the potential to quantify lateral and vertical (magnitude and direction) components of flow with spatial resolution of about one meter and temporal resolution of about one day. A fiber optic distributed temperature sensor is used with a novel heating system. Temperatures before heating may be used to evaluate background geothermal gradient and vertical profile of thermal diffusivity. The innovation presented is the use of variable energy application along the well, in this case concentrated heating at equally-spaced (2 m) localized areas (0.5 m). Relative to uniform warming this offers greater opportunity to estimate water movement, reduces required heating power, and increases practical length that can be heated. Numerical simulations are presented which illustrate expected behaviors. We estimate relative advection rates near the well using the times at which various locations diverge from a heating trajectory expected for pure conduction in the absence of advection. The concept is demonstrated in a grouted 600 m borehole with 300 heated patches, though evidence of vertical water movement was not seen.