Pipeline liquid–solid two-phase flow is a significantly important multiphase flow phenomenon widely encountered in both industrial and natural settings. The flow regime of liquid–solid two-phase flow in pipelines plays a crucial role as it represents the macroscopic manifestation of the suspension diffusion mechanism and the slip deposition law of solid particles. This paper provides an overview of research related to flow regimes and critical deposition velocity (CDV) in liquid–solid two-phase flow in pipelines. After briefly reviewing pioneering theoretical research in this field, the paper focuses on recent research in flow regime identification and prediction using state-of-the-art experimental methods and techniques. The review also rigorously assesses the reliability and validity of the methods, results, and conclusions related to the establishment of the CDV, identifying the deficiencies in the current research. Drawing on dimensional analysis and Pearson correlation analysis, the particle Reynolds number is introduced to establish a highly accurate correlation for predicting the CDV under conditions of wide volume concentration. The new correlation yields a mean absolute percentage error of 9.23% and a root mean square error of 10.29% within the volume concentration range of 0.7%–50.8%. This paper aims to provide clear guidance to researchers and professionals in related industries, enabling them to conduct more in-depth investigations according to their research interest and enhance their understanding of liquid–solid two-phase flow systems within pipelines.

Gas–liquid two-phase bubbly flow has significant applications across multiple fields, including reactor design and separation processes in chemical engineering, oil well extraction and pipeline transportation in the oil and gas industry, cooling systems in the nuclear industry, and wastewater treatment in environmental engineering. Bubble monitoring is crucial in these applications as it can enhance mass and heat transfer efficiency, improve flow stability, and ensure the safe operation of systems. This study developed an advanced algorithm aimed at precisely detecting and segmenting small bubbles at the gas–liquid interface using semantic segmentation techniques. This technology leverages deep learning models to analyze images, automatically identifying bubbles at the gas–liquid interface and accurately delineating their boundaries. The technique provides precise contours for each bubble, offering essential foundational data for further bubble dynamics analysis. Building on this, the deep learning detection algorithm was combined with the Deep Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) algorithm, tracking algorithm, enabling the system to rapidly and accurately identify and track the movement of the same bubble across consecutive frames.

Wet gas flow is prevalent in various industries. Accurate online measurement of wet gas mass flowrate holds significant importance for energy conservation and economic efficiency. This study presents a novel online measurement scheme for wet gas mass flowrate based on an averaged Pitot tube-vortex integrated sensor. By employing this self-designed dual mode detection integrated sensor, two different types of signals, differential pressure (DP) and vortex frequency, were obtained in the same region as the wet gas flows through. Given the challenges posed by the poor vortex street signal in two-phase flow, we proposed a fusion algorithm rooted in empirical wavelet transform (EWT) and robust local mean decomposition (RLMD) to extract the characteristic frequency. The positive deviation factor F _pd , characterized as the ratio of apparent mass flowrate to actual mass flowrate, was introduced to establish a correlation with the Lockhart-Martinelli parameter, X _LM . This correlation rectifies the phenomenon where the predicted mass flowrate exceeds the true value. Additionally, a dimensionless measurement parameter R , defined as the ratio of the DP of the averaging Pitot tube to the DP of the pipe wall, was used to get the initial value of F _pd , in order to start the iterative correction process. This method enhances the practicability of the F _pd - X _LM correlation and enables online non-separation measurement. Based on the experimental results, the measurement solution achieved relative error (RE) bands of ± 5% for 95% of the samples, with a mean absolute percentage error (MAPE) of 3.36%. The data show that the online wet gas measurement solution proposed in this study is viable and cost-effective.

Sewage is the second water resource. The accurate measurement of sewage flowrate is an essential link in the process of sewage resource utilization. In this article, we present a novel sewage flowrate measurement method based on ultrasonic phased array sensor with multi array elements and multi acoustic beams. The flow field information in the ultrasonic scanning plane is obtained by extending the non-contact single point information detection technology to the non-contact multi-point information detection technology, and then combined with the velocity distribution characteristics of the flow field, the entropy analysis method is used to build high-precision sewage flowrate measurement models, which can be developed into a promising sewage flowrate measurement solution. A series of experiments are carried out on the array ultrasonic sensor detection system designed and built in our laboratory. The experimental results, with the combined uncertainty is 0.069 m3/h, show that this method can accurately estimate the sewage flowrate. The circled entropy-boundary layer model has excellent prediction accuracy in 7–14 m3/h range, whose relative error (RE) is less than 2% and the mean absolute percentage error (MAPE) is 0.62%, which has obvious advantages compared with the accuracy of existing flow measurement technologies in the literatures. This study offers new insights on the application of array ultrasonic sensor and entropy analysis signal processing method in sewage flowrate measurement.

Coordination between ecological suitability and economic development is essential for sustainable urban growth. This study examines the spatiotemporal evolution, spatial differentiation, and driving mechanisms of ecological–economic coupling coordination in the Beijing–Tianjin–Hebei (BTH) region from 2014 to 2023 using four cross-sectional datasets (2014, 2017, 2020, and 2023). A comprehensive evaluation framework integrating natural and socioeconomic indicators was established, combining the entropy weight method, coupling coordination degree (CCD) model, spatial statistics, and the XGBoost-SHAP machine learning framework. Results revealed stepwise improvement in CCD over the decade, with low-coordination zones declining from 82.25% to 43.52%, while medium-coordination zones became the dominant spatial component. A concentric pattern emerged, featuring high-coordination agglomeration in the Beijing–Tianjin–Xiongan core, medium-coordination transition in the peri-Beijing ring, and contiguous low-coordination zones in northern Hebei. Global spatial agglomeration followed a trajectory of decline followed by rebound, whereas local agglomeration shifted from fragmentation toward homogenization. Population density and GDP alternately dominated as core socioeconomic drivers, with natural factors playing stable constraining roles. Both factors exhibited nonlinear threshold effects: population density showed diminishing marginal returns, while GDP displayed continuously strengthening positive effects. This study offers scientific support for high-standard spatial governance and sustainable development in the BTH region and other urban agglomerations.