Two-phase flow is commonly encountered in various engineering systems. Momentum fluctuation in two-phase flow can create undesirable and destructive vibrations. These vibrations are known as flow-induced vibrations, which are a fundamental phenomenon in fluid–structure interactions, and have been the center of this type of research in the past few decades. Flow-induced vibrations due to the multiphase flow are a complex phenomenon and its understanding is still immature. Various accidents related to flow-induced vibrations have been reported in heat exchangers and piping systems and it is very important to develop a deeper understanding of flow-induced vibrations in multiphase flow. The present review article aims to discuss the literature related to flow-induced vibrations, with special focus on factors affecting flow-induced vibrations in internal two-phase flow. Various factors affecting the magnitude and dominant frequency of forces are narrated and the correlations previously developed to estimate these quantities are discussed. Dimensionless forces are extracted from the literature and plotted against Weber number, to provide a database for comparison and to serve as a validation tool for any studies conducted using computational fluid dynamics. Furthermore, some important literature on flow-induced vibrations under different conditions is presented in tabular form to better understand these findings. Finally, some concluding remarks and comments on future research prospects and challenges are outlined.

Two phase flow commonly occurs in industrial pipelines, heat exchangers and nuclear power plants. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The first step to successfully design, analyze, and operate gas-liquid system is flow regime identification. Flow regime identification is of huge importance to the effective operation of facilities for the handling and transportation of multiphase fluids, and it represents one of the most significant challenges in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for identification of flow regime using dynamic pressure signals and deep learning techniques. Three different flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure readings were collected at a strategic location and were converted to scalograms to be used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures performed effectively in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe.

Gas–liquid two-phase flow is very common in industrial pipelines. Flow regime identification is the first step to design, analyze, and operate the gas–liquid system successfully. The purpose of this study is to develop a methodology for identification of a two-phase flow regime using post signal processing techniques, namely Fast Fourier Transform (FFT) and Probabilistic Density Function (PDF). Three different flow regimes were simulated in a 6 m horizontal pipe with a 0.050 m inner diameter. A Level-Set (LS) method coupled with Volume of Fluid (VOF) method is used to model the air–water interface. After validation of the numerical method, dynamic pressure readings were collected with the intent to identify the associated flow regimes by post-processing of these signals. It was concluded that dynamic pressure signals of different flow regimes show different characteristics (like dominant frequency, FFT amplitude, PDF location and PDF magnitude) in the time and frequency domains. These characteristics can be potentially used as differentiating factors to distinguish different flow regimes. This research is limited to stratified, slug, and annular flow in the horizontal pipe. This paper uses a new approach to identify the flow regime in a horizontal pipe by Fast Fourier Transform and Probability Density Function of dynamic pressure readings obtained by using numerical simulation.

Two-phase flow is of great importance in various industrial processes. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems and it holds great significance in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for identification of flow regime using dynamic pressure signals and deep learning techniques. Stratified, slug and annular flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure signals were collected at a strategic location. These signals were converted to scalograms and used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures were effective in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This study can be extended by collecting data over broader range of flow parameters and different geometries.

U-bends are frequently employed in industries to change the direction of fluid flow. Flow Induced Vibrations (FIV) are very prominent due to internal two-phase flow and vary with U-bend orientation. Potential possibilities of spatial orientations of a U-bend make it impractical to predict these vibrations using an experimental approach. This study investigates the two-phase flow-induced vibrations for 0°, 30°, 60° and 90° upward flow in a U-bend using CFD. In-plane and out-of-plane directional vibrational data was gathered for these four orientations of U-bend. For constant superficial velocities with void fraction of 0.5, impact of orientation on slug frequencies was observed with Fast Fourier Transformation. In-plane (x-axis) forces applied at the outlet bend significantly increase 39% compared to the corresponding forces at the inlet bend. Forces at the outlet bend along the y-axis (out-of-plane) were reduced by 26%, but the forces along the z-axis show a more moderate decrease of 7%. This study provides significant data essential to forming a correlation of two-phase flow-induced vibrations with multiple upward flow U-bend orientations.