Ye Mi

Vertical two-phase flow identification using advanced instrumentation and neural networks

Abstract Most two-phase flow measurements, including void fraction measurements, depend on correct flow regime identification. There are two steps taken towards the successful identification of flow regimes: first, develop a non-intrusive instrument to demonstrate area-averaged void fluctuations and second, develop a non-linear mapping approach to perform objective identification of flow regimes. In this paper, an advanced non-intrusive impedance void-meter provides input signals to neural networks which are used to identify flow regimes. After training, both supervised and self-organizing neural network learning paradigms performed flow regime identification successfully. The methodology presented holds considerable promise for multiphase flow diagnostic and measurement applications.

Flow regime identification methodology with neural networks and two-phase flow models

Vertical two-phase flows often need to be categorized into flow regimes. In each flow regime, flow conditions share similar geometric and hydrodynamic characteristics. Previously, flow regime identification was carried out by flow visualization or instrumental indicators. In this research, to avoid any instrumentation errors and any subjective judgments involved, vertical flow regime identification was performed based on theoretical two-phase flow simulation with supervised and self-organizing neural network systems. Statistics of the two-phase flow impedance were used as input to these systems. They were trained with results from an idealized simulation that was mainly based on Mishima and Ishiis flow regime map, the drift flux model, and the newly developed model of slug flow. These trained systems were verified with impedance signals measured by an impedance void-meter. The results conclusively demonstrate that the neural network systems are appropriate classifiers of vertical flow regimes. The theoretical models and experimental databases used in the simulation are shown to be reliable.

Investigation of vertical slug flow with advanced two-phase flow instrumentation

Abstract Extensive experiments of vertical slug flow were carried out with an electromagnetic flowmeter and an impedance void-meter in an air–water two-phase experimental loop. The basic principles of these instruments in vertical slug flow measurements are discussed. Time series of the liquid velocity and the impedance were separated into two parts corresponding to the Taylor bubble and the liquid slug. Characteristics of slug flow, such as the void fractions, probabilities and lengths of the Taylor bubble and liquid slug, slug unit velocity, area-averaged liquid velocity, and liquid film velocity of the Taylor bubble tail, etc., were obtained. For the first time, the area-averaged liquid velocity of slug flow was revealed by the electromagnetic flowmeter. It is realized that the void fraction of the liquid slug is determined by the turbulent intensity due to the relative liquid motion between the Taylor bubble tail region and its wake region. A correlation of the void fraction of the liquid slug is developed based on experimental results obtained from a test section with 50.8 mm i.d. The results of this study suggest a promising improvement in understanding of vertical slug flow.

Local flow measurements of vertical upward bubbly flow in an annulus

Local measurements of flow parameters were performed for vertical upward bubbly flows in an annulus. The annulus channel consisted of an inner rod with a diameter of 19.1 mm and an outer round tube with an inner diameter of 38.1 mm, and the hydraulic equivalent diameter was 19.1 mm. Double-sensor conductivity probe was used for measuring void fraction, interfacial area concentration, and interfacial velocity, and Laser Doppler anemometer was utilized for measuring liquid velocity and turbulence intensity. A total of 20 data sets for void fraction, interfacial area concentration, and interfacial velocity were acquired consisting of five void fractions, about 0.050, 0.10, 0.15, 0.20, and 0.25, and four superficial liquid velocities, 0.272, 0.516, 1.03, and 2.08 m/s. A total of 8 data sets for liquid velocity and turbulence intensity were acquired consisting of five void fractions, about 0.050, and 0.10, and four superficial liquid velocities, 0.272, 0.516, 1.03, and 2.08 m/s. The constitutive equations for distribution parameter and drift velocity in the drift-flux model, and the semi-theoretical correlation for Sauter mean diameter namely interfacial area concentration, which were proposed previously, were validated by local flow parameters obtained in the experiment using the annulus.

A neurofuzzy methodology for impedance-based multiphase flow identification

Abstract A neurofuzzy methodology for flow identification based on signals obtained from an impedance void meter is presented. The methodology combines the filtering and interpolative capabilities of neural networks with the representational advantages of fuzzy systems for the purpose of mapping idiosyncratic area-averaged impedance measurements to multiphase flow regimes. It has been shown that electrical signals representing the conductance of the intervening medium can be used to infer crucial flow parameters, and that area-averaged signals contain sufficient information about flow regime and the structure of its two-phase constituents. The neurofuzzy approach is a promising means for reconstructing the visual imagery of flow in a process, analogous to tomography, and holds considerable promise for multiphase flow diagnostic and measurement applications in the nuclear as well as in the petroleum, biomedical, and food-processing industries.

Interfacial area transport of vertical upward bubbly two-phase flow in an annulus

In relation to the development of the interfacial area transport equation in a subcooled boiling flow, the one-dimensional interfacial area transport equation was evaluated by the data taken in the hydrodynamic separate effect tests without phase change for an adiabatic air-water bubbly flow in a vertical annulus. The annulus channel consisted of an inner rod with a diameter of 19.1 mm and an outer round tube with an inner diameter of 38.1 mm, and the hydraulic equivalent diameter was 19.1 mm. Twenty data sets consisting of five void fractions, about 0.050, 0.10, 0.15, 0.20, and 0.25, and four superficial liquid velocities, 0.272, 0.516, 1.03, and 2.08 m/s were used for the evaluation of the one-dimensional interfacial area transport equation. The one-dimensional interfacial area transport equation agreed with the data with an average relative deviation of ±8.96 %. Sensitivity analysis was also performed to investigate the effect of the initial bubble size on the interfacial area transport. It was shown that the dominant mechanism of the interfacial area transport was strongly dependent of the initial bubble size.

Experimental study on interfacial area transport in vertical upward bubbly two-phase flow in an annulus

Forced convection subcooled water boiling experiments were conducted in a vertical annular channel. A high-speed digital video camera was applied to record the dynamics of the subcooled boiling process. The flow visualization results show that the bubble departure frequency generally increases as the heat flux increases. For some cases, the departure frequency may reach a limit around 1000 bubbles/second. In addition, bubble lift-off diameter, bubble growth rate and bubble velocity after bubble lift-off were determined by analyzing the images. The experimental data obtained from this study can be used in modeling the bubble departure frequency, bubble lift-off diameter, and bubble dynamics in forced convection subcooled boiling.

Hybrid fuzzy-neural flow identification methodology

In many engineering systems non-intrusive correct identification of flow regime is of great importance to performance and safety. Undesirable flow regimes may cause valves and pumps to wear, pipe leaks, even hazardous or catastrophic incidents. A non-intrusive methodology for flow identification in a two-phase flow loop is presented. The methodology relies on a fuzzy-neural hybrid system processing and interpreting impedance based measurements. The results suggest that fuzzy-neural classifiers are appropriate tools for flow regime identification.

Flow Regime Identification of Co-Current Downward Two-Phase Flow With Neural Network Approach

Flow regime identification for an adiabatic vertical co-current downward air-water two-phase flow in the 25.4 mm ID and the 50.8 mm ID round tubes was performed by employing an impedance void meter coupled with the neural network classification approach. This approach minimizes the subjective judgment in determining the flow regimes. The signals obtained by an impedance void meter were applied to train the self-organizing neural network to categorize these impedance signals into a certain number of groups. The characteristic parameters set into the neural network classification included the mean, standard deviation and skewness of impedance signals in the present experiment. The classification categories adopted in the present investigation were four widely accepted flow regimes, viz. bubbly, slug, churn-turbulent, and annular flows. These four flow regimes were recognized based upon the conventional flow visualization approach by a high-speed motion analyzer. The resulting flow regime maps classified by the neural network were compared with the results obtained through the flow visualization method, and consequently the efficiency of the neural network classification for flow regime identification was demonstrated.Copyright

Erratum to “Modeling of bubble-layer thickness for formulation of one-dimensional interfacial area transport equation in subcooled boiling two-phase flow” [International Journal of Heat and Mass Transfer 46 (2003) 1409–1423]

[Extract] The publishers regret that the following corrections were omitted in the published version.