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.

Liquid film plays a crucial role in void fraction, friction pressure drops, momentum and heat transfer in the two-phase flow. The film thickness measurement experiments of annular flow at four pressure conditions have been conducted using near-infrared sensor. The signal is processed by variational mode decomposition, whose parameters are optimized using the sparrow search algorithm. The envelope spectrum and Pearson correlation coefficient judgment criteria were adopted for signal reconstruction, and the value of the liquid film thickness is obtained. The effect (such as flow rate, pressure, entrainment, etc) of the liquid film thickness are analyzed theoretically. The characterization parameters We _g ″, We _l , Nμ _l and X _mod have been extracted and optimized, and a new average liquid film thickness correlation is proposed. The laboratory results indicate that the mean absolute percentage error of the predictive correlation is 4.35% (current data) and 12.02% (literatures data) respectively.

Gas-liquid two-phase flow non-separation measurement plays a crucial role in industrial production and two-phase flow theory research. In this paper, an intelligent multi-sensing system is designed for horizontal gas-liquid flow measurement, which integrates near-infrared (NIR), differential pressure (DP) and acoustic emission (AE) technology. The interaction and disturbance information of the gas-liquid phase are detection by the AE and NIR sensors, and then transformed into the form and margin factor which reflect the void fraction and flow rate of gas liquid flow respectively. Both of them are input as feature variables into the least absolute shrinkage and selection operator (LASSO) machine learning model for predicting the liquid phase volume fraction, and the best fusion solution is proposed. The Analysis and evaluation have been conducted in several classic correlations, and the Collins correlation is selected to optimize by LASSO optimization algorithm. The prediction accuracy is improved by 7.14% compared to the Collins correlation, and the MAPE for liquid mass flow rate of slug flow was 8.75% and 89.47% of the predictions were within 20% relative error.