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Evaluating the two-phase flow parameters across tube bundles is crucial to the analysis of vibration excitation mechanisms. These parameters include the temporal and local variation of void fraction and phase redistribution. Understanding these two-phase parameters is essential to evaluating the stability threshold of tube bundle configurations. In this work, capacitance sensor probes were designed using finite element analysis to ensure high sensor sensitivity and optimum response. A simulation-based approach was used to calibrate and increase the accuracy of the void fraction measurement. The simulation results were used to scale the normalized capacitance and minimize the sensor uncertainty to ±5%. The sensor and required conditioning circuits were fabricated and tested for measuring the instantaneous void fraction in a horizontal triangular tube bundle array under both static and dynamic two-phase flow conditions. The static calibration of the sensor was able to reduce the uncertainty to ±3% while the sensor conditioning circuit was able to capture instantaneous void fraction signals with frequencies up to 2.5 kHz.

Inlet Gas Void Fraction (IGVF ) and rotating speed are key parameters influencing the flow characteristics of gas-liquid multiphase pumps. To explore the influence of these two factors on the flow characteristics of gas-liquid multiphase rotodynamic pumps, CFD simulations were carried out based on the Euler two-fluid model to analyze the flow characteristics in an axial-flow pump at three different rotating speeds (1450 r/min, 2200 r/min, 2950 r/min) with different IGVF s. The numerical results indicate that the pump efficiency and head are first increased and then decreased with the increase of IGVF . When the IGVF is 5%, the head and efficiency reach their maximum values. This is attributed to the minimum vorticity and turbulence kinetic energy in the impeller passage at IGVF = 5%, and the small vortex range at the hub of the guide vane, thus resulting in the stable two-phase flow state in the impeller and guide vane. At the same IGVF , with the increase of the rotating speed, the head and efficiency of the multiphase pump increase accordingly. This is closely related to the smallest vortex range formed by gas-liquid flow separation at n =2950 r/min, indicating a better flow state.

Purpose The purpose of this study is to investigate the monodisperse cavitation of bubbly mixture flow for water and hydrogen mixture flows through a nozzle having a stenosis on the wall. Design/methodology/approach Two flow regions, namely, quasi-statically stable and quasi-statically unstable increase in the bubble radius, are considered. Different oscillating periods of bubbles in downstream corresponding to various values of Reynolds number are taken into account. The Range–Kutta method is used to tackle nonlinear coupled system of governing equations. Findings It is observed that for the larger values of Reynolds number, the void fraction at the upstream section, even at small values, yields instabilities at the downstream. Consequently, owing to sudden increase in the velocity, the bubbles strike the wall with high speed that eventually remove the existing stenosis. This process can be considered as an effective cardiac surgery for arteries with semi-blockage. Originality/value Original research work and to the best of author’s knowledge, this model is reported for the first time.

Two-phase pipe flow occurs frequently in oil & gas industry, nuclear power plants, and CCUS. Reliable calculations of gas void fraction (or liquid holdup) play a central role in two-phase pipe flow models. In this paper we apply the fractional flow theory to multiphase flow in pipes and present a unified modeling framework for predicting the fluid phase volume fractions over a broad range of pipe flow conditions. Compared to existing methods and correlations, this new framework provides a simple, approximate, and efficient way to estimate the phase volume fraction in two-phase pipe flow without invoking flow patterns. Notably, existing correlations for estimating phase volume fraction can be transformed and expressed under this modeling framework. Different fractional flow models are applicable to different flow conditions, and they demonstrate good agreement against experimental data within 5% errors when compared with an experimental database comprising of 2754 data groups from 14 literature sources, covering various pipe geometries, flow patterns, fluid properties and flow inclinations. The gas void fraction predicted by the framework developed in this work can be used as inputs to reliably model the hydraulic and thermal behaviors of two-phase pipe flows.

Void fraction is one of the most important parameters for the modeling and characterization of two-phase flows. This manuscript presents an overview of void fraction measurement techniques, experimental databases and correlations, in the context of microchannel two-phase flow applications. Void fraction measurement techniques were reviewed and the most suitable techniques for microscale measurements were identified along its main characteristics. An updated void fraction experimental database for small channel diameter was obtained including micro and macrochannel two-phase flow data points. These data have channel diameter ranging from 0.5 to 13.84 mm, horizontal and vertical directions, and fluids such as air-water, R410a, R404a, R134a, R290, R12 and R22 for both diabatic and adiabatic conditions. New published void fraction correlations as well high cited ones were evaluated and compared to this small-diameter void fraction database in order to quantify the prediction error of them. Moreover, a new drift flux correlation for microchannels was also developed, showing that further improvement of available correlations is still possible. The new correlation was able to predict the microchannel database with mean absolute relative error of 9.8%, for 6% of relative improvement compared to the second-best ranked correlation for small diameter channels.

Measuring the void fraction of different multiphase flows in various fields such as gas, oil, chemical, and petrochemical industries is very important. Various methods exist for this purpose. Among these methods, the capacitive sensor has been widely used. The thing that affects the performance of capacitance sensors is fluid properties. For instance, density, pressure, and temperature can cause vast errors in the measurement of the void fraction. A routine calibration, which is very grueling, is one approach to tackling this issue. In the present investigation, an artificial neural network (ANN) was modeled to measure the gas percentage of a two-phase flow regardless of the liquid phase type and changes, without having to recalibrate. For this goal, a new combined capacitance-based sensor was designed. This combined sensor was simulated with COMSOL Multiphysics software. Five different liquids were simulated: oil, gasoil, gasoline, crude oil, and water. To estimate the gas percentage of a homogeneous two-phase fluid with a distinct type of liquid, data obtained from COMSOL Multiphysics were used as input to train a multilayer perceptron network (MLP). The proposed neural network was modeled in MATLAB software. Using the new and accurate metering system, the proposed MLP model could predict the void fraction with a mean absolute error (MAE) of 4.919.

The subcooled flow boiling (SFB) of a water-ethanol mixture are relevant in operating heat-dissipating devices, such as smaller catalytic reactors, electronic apparatus, and hybrid electric vehicle battery components. The operative temperature should always be at a sustainable value to evade the failure or breakdown of these heat-dissipating devices. To cool these devices, a water-ethanol mixture is used as a coolant. The forced convective as well as SFB heat transfer coefficients (HTCs) for the water-ethanol mixture are estimated numerically using the volume of fluid method in a rectangular channel with dimensions of 15 mm×15 mm×150 mm. During SFB, the liquid-vapor interaction is examined by solving the bubble void fraction (BVF). For the discretization process, the Crank-Nicholson implicit method (scheme) is used, and the convective equation for the BVF is converted to an algebraic equation. The corrector predictor equation procedure is used for solving the BVF. The thermodynamic and thermophysical parameters related to subcooled boiling are estimated upon the incorporation of the bubble void fraction (α) using the mixture rule. These parameters are then incorporated into the x-momentum equation as well as into the energy equation for finding the fluid temperature, velocity, and pressure drop values. From the estimated values of temperature, subcooled flow boiling HTC is obtained. The estimated values of HTC can predict well compared with that of empirical equations. Moreover, mass flux plays a vital role in the forced convective region, while heat flux has a crucial role in the SFB region for the improvement of HTC.

The prediction of the interfacial area concentration is critical for one-dimensional gas–liquid two-phase flow analyses because the interfacial area concentration governs the mass, momentum, and energy transfer between gas and liquid in actual engineering situations. Introducing a two-group interfacial area transport equation into computational codes is one of the possible options for predicting the interfacial area concentration in transient and developing two-phase flows. The two-group approach treats bubbles in two groups: group-1 for small bubbles and group-2 for large bubbles. Two-group gas velocity is required to solve the two-group interfacial area transport equation. Thus, a one-group gas momentum equation currently used in two-fluid model-based computational codes should be transformed into a two-group gas momentum equation. However, introducing a two-group drift–flux model enables two-group gas velocity prediction via the one-group gas momentum equation without the current computational code structure. The two-group drift–flux model and void fraction covariance play critical roles in modeling the area-averaged relative velocity between the gas and liquid phases, which is critical in modeling the interfacial drag force. First, the framework used to calculate the two-group interfacial area concentration and area-averaged relative velocity via the two-group drift–flux model is described. Second, the paper provides a state-of-the-art review for two-group drift–flux correlations and void fraction covariance correlations developed for adiabatic and boiling two-phase flows in pipes, annuli, and rod bundles.

Gas–liquid two-phase flow widely exits in production and transportation of petroleum industry. Characterizing gas–liquid flow and measuring flow parameters represent challenges of great importance, which contribute to the recognition of flow regime and the optimal design of industrial equipment. In this paper, we propose a novel complex network-based deep learning method for characterizing gas–liquid flow. Firstly, we map the multichannel measurements to multiple limited penetrable visibility graphs (LPVGs) and obtain their degree sequences as the graph representation. Based on the degree distribution, we analyze the complicated flow behavior under different flow structures. Then, we design a dual-input convolutional neural network to fuse the raw signals and the graph representation of LPVGs for the classification of flow structures and measurement of gas void fraction. We implement the model with two parallel branches with the same structure, each corresponding to one input. Each branch consists of a channel-projection convolutional part, a spatial–temporal convolutional part, a dense block and an attention module. The outputs of the two branches are concatenated and fed into several full connected layers for the classification and measurement. At last, our method achieves an accuracy of 95.3% for the classification of flow structures, and a mean squared error of 0.0038 and a mean absolute percent error of 6.3% for the measurement of gas void fraction. Our method provides a promising solution for characterizing gas–liquid flow and measuring flow parameters.

Void fraction covariance has been introduced into the interfacial drag calculation used to close the one-dimensional two-fluid model. A model for void fraction covariance has been developed for large diameter pipes. The newly developed model has been compared with two previously developed models in terms of void fraction prediction accuracy. The effects of these additions on the void fraction prediction uncertainty have been evaluated utilizing a computational tool developed in MATLAB. The results indicate that there are small differences in the void fraction prediction between the models evaluated and the two-fluid model without void fraction covariance. Higher void fractions above 0.7 show the most significant changes. However, the differences in the uncertainty are not significant when compared to the uncertainty in the data used for the comparison. The results highlight a need for additional data for higher void fractions, collected with steam-water systems in large diameter pipes.