Limited penetrable visibility graph from two-phase flow for investigating flow pattern dynamics

Eyes-open and eyes-closed are two physiological states with different levels of brain activity. To classify EEG in eyes-open and eyes-closed, limited penetrable visibility graph method is applied to analyze EEG. It is a signal process method based on graph theory, which bridges complex networks and EEG time series. Networks topology, average path length and clustering coefficient are used to depict EEG characteristics respectively. The results show that limited penetrable visibility graph is more effective mehod than visibility graph for classifying EEG in eyes-open and eyes-closed. Clustering coefficient obtained by limited penetrable visibility graph is statistically significantly higher in eyes-closed condition and is a valid marker of eyes-closed EEG. There is no obvious difference in average path length. Limited penetrable visibility graph provides a new idea and an effective approach in classifying EEG time series.

As a highly efficient and compact heat exchanger, printed circuit heat exchangers have been successfully used in various industrial applications, including electric energy, petrochemicals, and aerospace. They also have broad development potential. This paper builds a visual experimental platform for a single-layer PCHE microchannels heat exchanger with a structure of rectangular and semi-circular flow channels. It uses a high-speed camera to observe the two-phase flow pattern, interface, and bubble behavior in the single-layer PCHE microchannels. By combining MATLAB image processing technology to analyze the evolution of the gas-liquid flow pattern, bubble shape, and void fraction distribution characteristics in the flow channels, we can also analyze the impact of different PCHE flow channel structures and experimental working conditions parameters on the gas-liquid two-phase flow pattern and pressure drop in the PCHE microchannels. Experimental results show that the flow patterns in rectangular and semicircular cross-section flow channels are mainly bubble flow and slug flow with different lengths. Wave flow will appear when the gas-liquid phase flow rate is relatively large, and bubble merging behavior occurs in the flow channel that causes wave flow. The void fraction distribution in the flow channels of the rectangular and semicircular structures is high in the middle and low on both sides. With an increase in the gas phase flow rate, large-sized bubbles are generated and small-sized bubbles accumulate at the inlet head, leading to partial blockage of the flow channels. This results in a more pronounced difference in the distribution of void fraction. Within the range of experimental flow parameters, different gas phase flow rates are fixed, as the liquid phase flow rate increases, the pressure drop of the flow channels also increases. The pressure drop of the semicircular structure flow channels are smaller under the condition of small gas phase flow, while under big gas phase flow conditions, the pressure drop of the rectangular structure flow channels are small.

Modeling the simultaneous flow of gas and liquid or two-phase gas-liquid flow in pipes is a key aspect in petroleum production. These models can enhance our ability to estimate fluid properties, predict pressure loss, liquid holdup, and flow pattern, and to see the effects of introducing concepts such as heat transfer to the system. Modeling two-phase flow phenomenon also allows visualization of the interaction of one property or parameter to another. The understanding of heat transfer in two-phase gas-liquid flow is important for economic and optimized operations.;This work focuses on the design of a PC-Based Software for modeling the effect of convective heat transfer on flow patterns in two-phase gas-liquid flow in pipes at all inclinations from -90° to +90° from horizontal, with the utilization of a temperature and pressure traverse along the length of the pipe. The implementation of this model in a computer program involves substantial calculations and correlations, some of which require iterative procedures.

Gas-liquid two-phase flows exist in a wide range of applications and enabling technologies. If, however, “understanding” we mean that the phenomenon in two-phase flow system can be predicted in terms of governing parameter, it should be concluded that two-phase flow is poorly understood item, and many questions are still open. A thorough theoretical approach with such microscopic experimental results as three-dimensional and high spatiotemporal measurements is needed to assess the performance of gas-liquid two-phase flow. My study of gas-liquid two-phase flows at microgravity conditions and in small diameter pipe is outlined in this report, especially for which regarding to interfacial area transport and drift flux. Research on two-phase at micro- or reduced-gravity conditions and in small pipe is expected to be conducted more widely in the future, shedding light on two-phase flow phenomena in piping system.

Resonance analyses of a pipeline-riser system conveying gas-liquid two-phase flow with flow-pattern evolution

The purpose of this study is to improve a two-fluid model applicable to gas-liquid two-phase slug flows in small diameter pipes. Experimental data on void fraction and frictional pressure drop were obtained for vertical upward slug flow in 5 and 9 mm i.d. circular pipes, and those on the interfacial friction force were obtained by substituting the above data into an equation derived from the one-dimensional two-fluids model. The test liquids were a Poly-oxy-ethylene lauryl ether water solution, a 72 wt% glycerin water solution and a tap water at 30 °C, while the test gas was air at near atmospheric pressure. In order to study the effects of liquid properties, surface tension of the test liquids against air was varied from 0.042 to 0.071 N/m, viscosity was 0.797 to 19.6 mPa·s, and density was 996 to 1184 kg/m3. The range of volumetric flux of the liquid was 0.1 to 2.0 m/s, and that of the gas was 0.1 to 22 m/s. In addition, the drag coefficient data of the large bubble was determined from the data on the bubble diameter, the respective void fractions of the liquid slug section and the large bubble one, the interfacial friction force and the length ratio of the large bubble to the total of the liquid slug and the large bubble. These data were used to validate representative two-fluid model codes. Since the prediction by the codes did not fit well the present data, a new drag coefficient correlation for the large bubble was proposed using 5 dimensionless parameters. The predictions by the new correlation and familiar ones in literatures have been tested against the present data, and the applicability of the new one has been demonstrated.

Experimental study of oil-water two-phase flow patterns in a vertical large diameter pipe

The void fraction wave is a special physical phenomenon in a gas-liquid two-phase flow system. Understanding the propagation of the void fraction wave is of great significance for uncovering the physical mechanisms in both flow pattern transition and the fluid velocity measurement. In this study, detrended cross-correlation analysis (DCCA) is used to investigate the multi-scale cross-correlation characteristics of the coupled ARFIMA processes. It is found that the DCCA can effectively reveal the multi-scale cross-correlation dynamical behaviors of complex system. Then, we carry out the experimental test in a vertical gas-liquid two-phase flow pipe with small inner diameter. The DCCA is used to detect the cross-correlation characteristics of the void fraction wave on multiple time scales, and the growth rate of the cross-correlation level for the void fraction wave is observed on low time scales. Additionally, the spatial attenuation factor (SAF) of the void fraction wave is calculated to investigate the instability of the wave propagation. The SAF is close to zero under the transitional flow patterns, which means that the void fraction wave is in a stable propagating state. For bubble flows, the void fraction wave presents the attenuation characteristics, whilst the void fraction wave shows the amplification characteristics under the slug and churn flow patterns. Interestingly, the instability behaviors of the void fraction wave are always associated with its multi-scale cross-correlation characteristics. Specifically, the increasing rate of the wave cross-correlation level on low scales is much higher for transitional flow patterns, which is corresponding to the stable propagating characteristic of the void fraction wave. However, when the void fraction wave exhibits attenuation or amplification characteristics under other flow patterns, the increasing rate of the wave cross-correlation level on low scales is much lower.

Identification of gas-liquid two-phase flow patterns in dust scrubber based on wavelet energy entropy and recurrence analysis characteristics

Interfacial area transport for bubbly-to-slug transition flows in small diameter pipes

Flow regime behavior in upwards oil-water flow has been studied in a large diameter, inclinable, multiphase flow loop. With the pipe vertical, bubble flow occurred for all conditions tested. When the pipe was inclined by 5 or 15° from vertical, a new flow regime, countercurrent bubble flow, was observed over a wide range of flow conditions. Theoretical flow regime maps developed for gas-liquid flow did not properly predict oil-water flow regime behavior.

Recently, due to an increase in production demand in nuclear and oil and gas industries, the requirement to migrate toward larger pipe sizes for future developments has become essential. However, it is interesting to note that almost all the research on two-phase gas–liquid flow in vertical pipe upflow is based on small-diameter pipes (D ≤ 100 mm), and the experimental work on the two-phase gas–liquid flow in large-diameter (D > 100 mm) vertical pipes is scarce. Under the above circumstances, the application of modeling tools/correlations based on small-diameter pipes in predicting flow behavior (flow pattern, void fraction, and pressure gradient) poses severe challenges in terms of accuracy. The results presented in this article are motivated by the need to introduce the research work done to the industries where the data pertaining to large-diameter vertical pipes are scarce and there is a lack of understanding of two-phase gas-liquid flow behavior in large-diameter (D > 100 mm) vertical pipes.The unique aspect of the results presented here is that the experimental data have been generated for a 254-mm inner diameter vertical pipe that forms an excellent basis for the assessment of modeling tools/correlations. This article (i) presents the results of a systematic investigation of the flow patterns in large-diameter vertical pipes and identifies the transition between subsequent flow patterns, (ii) compares it directly with the existing large- (150 mm) and small-diameter data (28 mm and 32 mm) in the same air–water superficial velocity range, (iii) exemplifies that the existing available empirical correlations/models/codes are significantly in error when applied to large-diameter vertical pipes for predictions, and last (iv) assesses the predictive capability of a well-known commercial multiphase flow simulator.

Based on some typical complex network analysis methods and machine learning techniques, a general multiple-measures composited strategy-guided epilepsy diagnosis analysis framework is proposed in this brief paper. Five typical network analysis methods for biology time series analysis are utilized for real applications, including the classical visibility graph (VG), horizontal visibility graph (HVG), the limited penetrable visibility graph (LPVG), the modified frequency degree method (MFDM), and the quantity graph (QG). By using the aforementioned typical transformation methods, the EEG signal sets to be classified are transferred into graph network object sets. The main network features and related indicators are calculated and extracted as features for classification tasks. Some key features are selected via variance analysis, and the eXtreme Gradient Boosting (XGBOOST) machine learning algorithm is used for related binary and five-class classification tasks for electroencephalographic time series. Numerical experiments demonstrate that, through ten-fold cross-validation on the entire dataset, the classification accuracy for two-class classification consistently reaches 97.8% (with a specificity of 97.5%), while for five-class classification, the accuracy stably reaches 82.4% (with a specificity of 95.6%). Therefore, our classification framework can be effectively used to assist hospital doctors and medical specialists in diagnosing related diseases, especially to help accelerate the treatment of epilepsy patients.

Image processing techniques for the measurement of two-phase bubbly pipe flows using particle image and tracking velocimetry (PIV/PTV)

Bubble growth, departure and the following flow pattern evolution during flow boiling in a mini-tube