Void fraction and flow regime in adiabatic upward two-phase flow in large diameter vertical pipes

Flow regimes identification of air water counter current flow in vertical annulus using differential pressure signals and machine learning

This study investigates the gas-liquid two-phase counter-current flow through a vertical annulus, a phenomenon prevalent across numerous industrial fields. The presence of an inner pipe and varying degrees of eccentricity between the inner and outer pipes often blur the clear demarcation of flow regime boundaries. To address this, we designed a vertical annulus with adjustable eccentricity (outer and inner diameters of 125 mm and 75 mm, respectively). We conducted gas-liquid counter-current flow experiments under specific conditions: gas superficial velocity ranging from 0.06 to 5.04 m/s, liquid superficial velocity from 0.01 to 0.25 m/s, and five levels of eccentricity (e = 0, 0.25, 0.5, 0.75, 1). We collected differential pressure data at two distinct height distances (DP1: 50 mm and DP2: 1000 mm). We used vectors, composed of both the probability density functions (PDFs) of the differential pressure signals and the power spectral density (PSD) reduced via Principal Component Analysis, as features. Using the CFDP clustering algorithm-based on local density-we clustered the flow regimes of the experimental data, thereby achieving an objective and consistent identification of the flow regime of gas-liquid two-phase counter-current flow in a vertical annulus. Our analysis reveals that for DP1, the main differences in the PSD of various flow regimes occur within the 0.5-1 Hz range. Among the three flow regimes involved, the slug flow exhibits the highest power intensity, followed by the bubbly flow, with the churn flow having the least. In terms of differential pressure distribution, the bubbly and churn flows have a concentrated distribution, while the slug flow is more dispersed. For DP2, the PSD differences primarily exist within the 0.5-2 Hz range. The churn flow has the highest power intensity, followed by the slug flow, with the bubbly flow being the weakest. Here, the bubbly flow's differential pressure distribution is concentrated, while the slug and churn flows are more dispersed. Based on the results of the flow regime classification, we generated a flow regime map and analyzed the influence of annulus eccentricity on the flow regime. We found that in most cases, pipe eccentricity does not significantly affect the flow regime. However, in the transition region-such as the bubbly to slug flow transition zone-flows with medium eccentricity values (e = 0.5, 0.75) are more likely to transition to slug flow. We compared the visual recognition results of flow regimes with the clustering results. 4.04% of the total samples showed different results from visual recognition and clustering, primarily located in the flow regime transition area. Since visually distinguishing flow regimes in these areas is typically challenging, our methodology offers an objective classification approach for gas-liquid two-phase counter-current flow in a vertical annulus.

Free-Phase Gas Detection in Groundwater Wells via Water Pressure and Continuous Field Parameters

Detection of free-phase gas (FPG) in groundwater wells is critical for accurate assessment of dissolved gas concentrations and the occurrence of FPG in the subsurface, with consequent implications for understanding groundwater contamination and greenhouse gas emissions. However, identifying FPG is challenging during routine groundwater monitoring and there is poor agreement on the best approach to detect the occurrence of FPG in groundwater. In this study, laboratory experiments in a water column were designed to mimic nonflowing and flowing conditions in a groundwater well to evaluate how the presence of FPG affects water pressure and commonly used continuous field parameters. The laboratory results were extrapolated to interpret field data at an abandoned exploration well with episodic release of free-gas CO₂ . The FPG effect on water pressure varied between flowing and nonflowing wells, and depending on whether the FPG was above or below the sensor. Electrical conductivity values were decreased and/or behaved erratically when FPG was present in the water column. Findings from this study have shown that the combined measurement of water pressure, electrical conductivity, and total dissolved gas pressure can provide information about the occurrence of FPG in groundwater wells. Measurement of these parameters at different depths can also provide information about relative depths and amounts of FPG within the well water column. This approach can be used for long-term monitoring of groundwater gases, managing gas-locking in production wells with gassy groundwater, and measuring fugitive greenhouse gas emissions from groundwater wells.

Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows

While investigating multiphase flows experimentally, the spatiotemporal variation in the interfacial shape between different phases must be measured to analyze the transport phenomena. For this, numerous image processing techniques have been proposed, showing good performance. However, they require trial-and-error optimization of thresholding parameters, which are not universal for all experimental conditions; thus, their accuracy is highly dependent on human experience, and the overall processing cost is high. Motivated by the remarkable improvements in deep learning-based image processing, we trained the Mask R-CNN to develop an automated bubble detection and mask extraction tool that works universally in gas–liquid two-phase flows. The training dataset was rigorously optimized to improve the model performance and delay overfitting with a finite amount of data. The range of detectable bubble size (particularly smaller bubbles) could be extended using a customized weighted loss function. Validation with different bubbly flows yields promising results, with AP₅₀ reaching 98%. Even while testing with bubble-swarm flows not included in the training set, the model detects more than 95% of the bubbles, which is equivalent or superior to conventional image processing methods. The pure processing speed for mask extraction is more than twice as fast as conventional approaches, even without counting the time required for tedious threshold parameter tuning. The present bubble detection and mask extraction tool is available online (https://github.com/ywflow/BubMask).

Influence of Two-Phase Crossflow for Void Prediction in Bundles Using Thermal-Hydraulic System Codes

The previous study, where the void fraction predictability of three different thermal-hydraulic system codes was assessed against PSBT (PWR Subchannel and Bundle Test) benchmark data, indicated a general overprediction tendency of all system codes, especially in bundles. Because all codes have been utilized for best-estimate analyses, it is necessary to conduct further assessments in order to find the root cause of the overprediction. A further assessment has been performed using two thermal-hydraulic system codes, TRACE V5.0 patch 5 and MARS-KS 1.4, and the assessment has been carried out for both one- and multi-dimensional components. The results indicate that there is no significant difference in the predictability of the void fraction between one- and multi-dimensional components. In addition, it is found that the turbulent mixing model implemented for the multi-dimensional component of MARS-KS does not play an important role in the prediction of void distribution. Meanwhile, TRACE reveals a significant overprediction due to much less crossflow calculation compared to MARS-KS. By conducting an additional analysis with the modified one-dimensional models, it is clearly confirmed that crossflow significantly affects the void distribution. Therefore, it is concluded that the model for the thermal hydraulic mixing by crossflow in each system code should be improved in order to predict the void distribution in bundles appropriately.

Gas Void Fraction Measurement of Gas-Liquid Two-Phase CO2 Flow Using Laser Attenuation Technique

The carbon capture and storage (CCS) system has the potential to reduce CO₂ emissions from traditional energy industries. In order to monitor and control the CCS process, it is essential to achieve an accurate measurement of the gas void fraction in a two-phase CO₂ flow in transportation pipelines. This paper presents a novel instrumentation system based on the laser attenuation technique for the gas void fraction measurement of the two-phase CO₂ flow. The system includes an infrared laser source and a photodiode sensor array. Experiments were conducted on the horizontal and vertical test sections. Two Coriolis mass flowmeters are respectively installed on the single-phase pipelines to obtain the reference gas void fraction. The experimental results obtained show that the proposed method is effective. In the horizontal test section, the relative errors of the stratified flow are within ±8.3%, while those of the bubble flow are within ±10.6%. In the vertical test section, the proposed method performs slightly less well, with relative errors under ±12.2%. The obtained results show that the measurement system is capable of providing an accurate measurement of the gas void fraction of the two-phase CO₂ flow and a useful reference for other industrial applications.