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Oxygen bubble accumulation on the anodic side of a polymer exchange membrane water electrolyzer (PEMWE) may cause a decrease in performance. To understand the behavior of these bubbles, a deep-learning-based bubble flow recognition tool dedicated to a PEMWE is developed. Combining the transparent side of a single PEMWE cell with a high-resolution high-speed camera allows us to acquire images of the two-phase flow in the channels. From these images, a deep learning vision system using a fine-tuned YOLO V7 model is applied to detect oxygen bubbles. The tool achieved a high mean average precision of 70%, confirmed the main observations in the literature, and provided exciting insights into the characteristics of two-phase flow regimes. In fact, increasing the water flow rate from 0.05 to 0.4 L/min decreases the bubble coverage (by around 32%) and the mean single-bubble area. In addition, increasing the current density from 0.3 to 1.4 A/cm2 leads to an increase in bubble coverage (by around 40%) and bubble amount.

Owing to different flow conditions—for example, Poiseuille and Couette flow—one could expect different deformations of large-sized bubbles; however, bubble dynamics has been mostly investigated in channel flow. Consequently, an intermediate flow condition shared by both the channel and ship surface is needed for large-sized bubbles, although it can be difficult to simultaneously generate turbulent Couette flow in the channel and measure the shear stress on the ship’s surface experimentally. In this study, large-sized bubbles in turbulent Couette flow were investigated numerically to determine their common characteristics under such flow conditions. The interIsoFoam solver from OpenFOAM—which can directly capture the interface via the geometric volume of fluid method—was used to conduct the simulations of the gas–liquid interface problem. The turbulent Couette flow was driven by top wall velocity condition with an initial perturbation, and three different bubble sizes with Weber numbers in the range of 200–300 were chosen to determine the characteristics of large-sized bubbles. By monitoring the results according to bubble size, we could determine bubble characteristics that were distinguishable from those in turbulent Poiseuille flow. Consequently, bubble deformation was dominated by the velocity gradient and shear rate, which was greater than that during single-phase flow from the liquid-film region. These results allowed us to generalize the deformation mechanism of large-sized bubbles in turbulent Couette flow into five categories—namely, the initial shape, deformation on the front side, change of the center of gravity, pinch-off/breaking of the ligament, and deformation to a stable shape.Graphical abstract

Numerical simulations are performed to investigate the breakup of air bubble in flow focusing configuration; the CLSVOF (coupled level set with volume of fluid) method is employed to track the interface, which allows a better identification of the liquid–gas interface via a function called level set. The CFD simulations showed that the velocity ratio, the interfacial tension, the outer channel diameter, the continuous phase viscosity, the orifice width and length play an important role in the determination of the air bubble’s size and shape. However, at low capillary number, increasing the flow velocity ratio gives a smaller bubble size in shorter time, while the increase in interfacial tension leads to a bigger bubble. Moreover, the carrier fluid is found to slightly affect the bubbling mechanism, while the smallest bubbles were obtained with the smallest orifice size. In addition, three breakup regimes are observed in this device: disc-bubble (DB), elongated bubble (EB) and the slug bubble (SB) regime flows. This work also demonstrates that the CLSVOF is an effective method to simulate the bubbles breakup in flow focusing geometry. In addition, a comparison of our computational simulations with available experimental results reveals reasonably good agreement.Graphic abstract

An in-depth analysis of the two-phase flow in a hydraulic braking pipeline can reveal its evolution process pertinent for designing and maintaining the hydraulic system. In this study, a high-speed camera examined the two-phase flow pattern and bubble characteristics in a hydraulic braking pipeline. Bubble flow pattern recognition, bubble segmentation, and bubble tracking were performed to analyze the bubble movement, including its behavior, distribution, velocity, and acceleration. The results indicate that the gas–liquid two-phase flow patterns in the hydraulic braking pipeline include bubbly, slug, plug, annular, and transient flows. Experiments reveal that bubbly flow is the most frequent, followed by slug, plug, and transient flows. However, plug and transient flows are unstable, while annular flow occurs at a wheel speed of 200 r/min. Bubbles predominantly appear in the upper section of the pipeline. Furthermore, large bubbles travel faster than small bubbles, whereas slug flow bubbles exhibit higher velocities than those in plug or transient flows.

This work introduces the use of machine vision in the massive bubble recognition process, which supports the validation of boiling models involving bubble dynamics, as well as nucleation frequency, active site density and size of the bubbles. The two algorithms presented are meant to be run employing quite standard images of the bubbling process, recorded in general-purpose boiling facilities. The recognition routines are easily adaptable to other facilities if a minimum number of precautions are taken in the setup and in the treatment of the information. Both the side and front projections of subcooled flow-boiling phenomenon over a plain plate are covered. Once all of the intended bubbles have been located in space and time, the proper post-process of the recorded data become capable of tracking each of the recognized bubbles, sketching their trajectories and size evolution, locating the nucleation sites, computing their diameters, and so on. After validating the algorithm’s output against the human eye and data from other researchers, machine vision systems have been demonstrated to be a very valuable option to successfully perform the recognition process, even though the optical analysis of bubbles has not been set as the main goal of the experimental facility.

This study investigates the impact of varying degrees of bubble flow on radon migration at the water–air interface. An apparatus was designed to monitor the change in the activity concentration of radon transferred from water to air at different levels of bubble flow, and high-speed cameras were used to capture the bubble flow. The optical method determined the captured images’ bubble size and average flow velocity. A mathematical model was used to estimate and experimentally measure the activity concentration of radon in air, resulting in the determination of the optimal radon transfer velocity coefficient ( K ). Based on the experimental and fitted results, empirical equations for the variation of the radon transfer velocity coefficient under different levels of bubble flow were derived. These formulas show that the radon transfer velocity coefficient does not always increase with increasing levels of bubble flow but increases to an upper limit and then stops.

In order to improve the accuracy and efficiency of flow pattern recognition and to solve the problem of the real-time monitoring of flow patterns, which is difficult to achieve with traditional visual recognition methods, this study introduced a flow pattern recognition method based on a convolutional neural network (CNN), which can recognize the flow pattern under different pressure and flow conditions. Firstly, the complex gas–liquid distribution and its velocity field in the annulus were investigated using a computational fluid dynamics (CFDs) simulation, and the gas–liquid distribution and velocity vectors in the annulus were obtained to clarify the complexity of the flow patterns in the annulus. Subsequently, a sequence model containing three convolutional layers and two fully connected layers was developed, which employed a CNN architecture, and the model was compiled using the Adam optimizer and the sparse classification cross entropy as a loss function. A total of 450 images of different flow patterns were utilized for training, and the trained model recognized slug and annular flows with probabilities of 0.93 and 0.99, respectively, confirming the high accuracy of the model in recognizing annulus flow patterns, and providing an effective method for flow pattern recognition.

Global thresholding parameters for the semantic segmentation of bubbles from experimental bubble image shadowgraph were implemented. Traditional image processing algorithms for experimental visualization of multiphase flows require very rigorous and time-consuming trial by error of applying thresholding to be able to obtain the bubble statistics. More so, due to the varying flow conditions and lighting system during experimentation, it is impossible to apply a global threshold for in the post-processing the results of visualized flows. BIMSNet (modified U-Net architecture) was trained with bubble shadowgraph images obtained from experiments with varying flows and lightning conditions and developed global threshold parameters (binarization threshold) to semantically segment clustered bubbles with irregular shapes. The variation of pixel intensity of the sequence of images was taken into consideration in training the network. The average dice coefficient score (accuracy) of the network on the validation dataset was 99.3% with a 1.2% loss. Evaluation of the trained network on the test dataset gave an average precision and dice coefficient score of 99.73%, respectively. The detection of bubbles with the trained model when compared with the local average adaptive threshold image extraction process yields a higher bubble detection rate with less amount of misdetection and eliminates the trial-by-error method of obtaining the threshold limits for the binarization of images when post-processing images. Graphical abstract

This research investigates the cavitation wear phenomena in a direct-acting pressure relief valve (PRV), where the poppet of the PRV is subjected to unseat at over-pressure conditions of the hydraulic system. The poppet unseat results in to sudden fall in system pressure and hence, the expansion and burst of pre-existing tiny air bubbles and thus the cavitation. To reduce the occurrence of cavitation, three different poppet shapes i.e. chamfered conical, blunt, and spherical heads is proposed for replacement over the existing conical head. To investigate the effect of different poppet shapes, the CFD simulation of the simplified 2-D fluid flow geometry is considered and the same is validated through experiments. Further, the validated model is used to investigate the cavitation parameters during the PRV operation at different opening conditions. The results showed that the blunt head PRV poppet offers comparatively lesser cavitation intensity for hydraulic systems subjected to frequent over-pressure conditions. Graphic abstract

AbstractTwo-phase gas–liquid flows are prevalent in many industries and understanding their behaviour would have significant impact on the efficiency of the systems in which they occur. However, information on two-phase gas–liquid flows in 90∘ bends is limited in the literature and their flow behaviour is not fully understood. One technique that could assist researchers in exploring flow behaviour is visualisation. Accordingly, in this study a two-phase flow experimental investigation was carried out in a large pipe of diameter 150 mm, using water and air at different superficial velocities in order to visualise the effect of 90∘ bend on two-phase flow behaviour. As optical methods are not suitable for visualising dense bubbly flows due to overlapping of bubbles, in this study, bubble size distribution and void fraction results were obtained using wire-mesh sensors before and after the bend. The results were then post-processed to visualise the flow field. The instantaneous visualisation of flow shows that gas hold-up migrates from the bottom to top wall of the pipe at the bend when the liquid superficial velocity increases for a fixed superficial gas velocity. An increase in superficial gas velocity shows insignificant influence on the gas hold-up at locations beyond the bend for the investigated conditions. This may be due to the centrifugal force imparted by the bend and hence needs further investigation. Bubble size distribution results before and after the bend indicate that the bend has influence on bubble breakup and coalescence.Graphical Abstract