Bubble Images Research Articles

Experimental study on aeration characteristics of plunging wave impact on a vertical cylinder

The impact force exerted by plunging breakers on structures is most pronounced, with the impact pressure being most sensitive to the changes in air entrainment, posing a threat to the structural safety of offshore wind turbines. However, the current explanations of the high impact force mechanisms in plunging waves are relatively superficial, and the relationship between the air entrainment motion and pressure oscillations remains unclear. According to the experimentally obtained pressure characteristics and the formation of entrapped air, this paper systematically divides the process of plunging wave impact on a vertical cylinder into three stages: breaking wave crest impact, entrained air cavity impact, and plume impact. Employing a bubble image velocimetry platform to visualize flow field characteristics and quantify turbulence intensity, we investigated the connection between gas motion and impact loads. The results indicate that the conversion of gas energy is closely related to the load characteristics, with the maximum impact force occurring near the peak of the energy gradient. The gas movement significantly influences pressure oscillations, which are positively correlated with the gas's physical compression and expansion characteristics. This study has uncovered new insights into the phenomenon of plunging waves impacting wind turbine towers and has enhanced the understanding of its load characteristics.

Bubble boundary R-CNN: A multitask model for segmenting and reconstructing overlapping bubbles

When measuring the dynamic characteristics of bubbles using image-based methods, the images including complex overlapping of bubbles with irregular shape pose a huge challenge to current bubble reconstruction algorithms. Based on the Mask R-CNN architecture, a multitask model “Bubble Boundary R-CNN” is proposed for detection, segmentation and shape reconstruction of overlapping bubbles with high deformation at high gas velocities. By leveraging shared deep features from detection and segmentation, model achieves flexible bubble reconstruction and accurate size distribution extraction from high gas velocity bubble flows. To overcome the limitations of Mask R-CNN and address the challenges posed by high-deformation and overlapping bubbles, a semantic segmentation auxiliary head and the Boundary-preserving Mask Head (BMask Head) are introduced to enhance edge information and extract bubble boundary features. Additionally, an Overlapping Boundary Prediction Network (OBPN) is proposed to reconstruct boundary features that are lost due to bubble occlusion and predict the complete bubble shapes. The optimized model shows its ability to flexibly and accurately reconstruct overlapped and deformed bubbles by varying occlusion rates and circularity. The new model has also proven its reliability when applied to bubble images from a bubble column at gas velocities of up to 0.067 m3/s. In future work, more efforts are needed to further enhance the detection capability of the model and reduce missed and multiple detections at high gas velocity.

Deep learning assisted characterization of bubble behavior in a gas-solid fluidized bed with binary particle mixtures

Gas-solid fluidized beds are widely applied as chemical reactors, and the fluidization quality in the bed is closely related to bubble behavior. Digital image processing is a commonly used non-invasive method for bubble behavior analysis, but it is usually constrained by experimental conditions such as lighting, making identification of bubble and emulsion phases still challenging. Herein, deep learning is applied in this study to optimize traditional digital image processing techniques. By evaluating different deep learning models (FCN, DeepLab V3, U-Net), rapid and accurate identification and segmentation of bubble images can be achieved, and the U-Net model performs best, achieving an identification accuracy of 99.05 %. Further application of U-Net to analyze bubble behavior demonstrates that deep learning methods enable efficient and accurate identification of bubbles and real-time analysis of bubble behavior, highlighting the significant potential application of deep learning in the field of complex hydrodynamics in fluidized beds.

Dynamic mode decomposition based Doppler monitoring of de novo cavitation induced by pulsed HIFU: an in vivo feasibility study

Pulsed high-intensity focused ultrasound (pHIFU) has the capability to induce de novo cavitation bubbles, offering potential applications for enhancing drug delivery and modulating tissue microenvironments. However, imaging and monitoring these cavitation bubbles during the treatment presents a challenge due to their transient nature immediately following pHIFU pulses. A planewave bubble Doppler technique demonstrated its potential, yet this Doppler technique used conventional clutter filter that was originally designed for blood flow imaging. Our recent study introduced a new approach employing dynamic mode decomposition (DMD) to address this in an ex vivo setting. This study demonstrates the feasibility of the application of DMD for in vivo Doppler monitoring of the cavitation bubbles in porcine liver and identifies the candidate monitoring metrics for pHIFU treatment. We propose a fully automated bubble mode identification method using k-means clustering and an image contrast-based algorithm, leading to the generation of DMD-filtered bubble images and corresponding Doppler power maps after each HIFU pulse. These power Doppler maps are then correlated with the extent of tissue damage determined by histological analysis. The results indicate that DMD-enhanced power Doppler map can effectively visualize the bubble distribution with high contrast, and the Doppler power level correlates with the severity of tissue damage by cavitation. Further, the temporal characteristics of the bubble modes, specifically the decay rates derived from DMD, provide information of the bubble dissolution rate, which are correlated with tissue damage level—slower rates imply more severe tissue damage.

A New Bubble Image Model Based on the Recognition of Bubble Flow

AbstractIn this study, a new ellipse‐fitting algorithm is proposed to achieve the reconstruction of bubble shapes in bubbly flow captured by a high‐speed camera in the gas–liquid two‐phase column reactor. Bubble flow patterns and geometric parameters in the experimental images are recognized and identified successfully, represented by means of the topological parameters. Three logical steps are carried out in detail. First, the area threshold and the circularity factors are established to identify the bubbles whether belonging to a single bubble or not. The overlapping bubbles in images can be separated from single bubbles based on a watershed segmentation algorithm. Second, a single bubble image and an overlapping bubble image are combined into one image. After that, statistical analysis for the size distributions and ellipse area bubbles is performed for further analysis and discussion. The advantage of this algorithm is that it can make use of a set of major and minor axes of an ellipse to capture the ellipse parameters more effectively. Simulation results are well agreed with experimental measurements. Moreover, it can be used to detect many ellipse‐like bubbles that are dispersed in high‐speed camera images, indicating that it is a better strategy for the recognition and identification of bubbly turbulent flow accurately.

Direct Reconstruction of Wet Foam from Sparse‐View, Dynamic X‐Ray CT Scans

X‐ray imaging of wet foam dynamics with a high temporal resolution (e.g., 3D videos with a 10 Hz frame rate) requires fast rotation of either the foam sample or the X‐ray gantry. This, however, strongly limits the number of X‐ray projections per rotation that can be acquired. As a result, conventional computed tomography reconstruction methods generate 3D images with severe undersampling artefacts, complicating subsequent foam analysis. Herein, BubSub, a novel tomographic reconstruction approach that reconstructs a 4D (3D plus time) dynamic image of wet foam bubbles from sparse‐view X‐ray projections by leveraging prior knowledge about the evolving foam structure, is introduced. BubSub adapts a collection of subdivision surfaces with spherical topology to represent liquid–gas interfaces of foam bubbles. Estimation of bubble positions and shapes at each time point is achieved by minimizing the projection distance in relation to the measured projections. BubSub operates efficiently with minimal memory usage, exhibits robustness against noise, and provides accurate reconstructions, even when the available projections are limited, as evidenced by various experiments using both simulated and real wet foam X‐ray data.

Development of multi-view 3D reconstruction system for bubble flow measurement

This work introduces the development of bubble measurement method utilizing a three-dimensional (3D) reconstruction technique from multi-view images. Multiple synchronized cameras were positioned around a water container, and calibration was performed to obtain external and internal camera parameters. Images of bubbles emerging from a nozzle were captured, and a machine learning technique was used to extract bubble silhouettes as foreground probability distributions, enabling the extraction of bubbles from images obtained with a simple lighting setup. These distributions were then projected onto a 3D voxel space using the visual hull method. It was confirmed that the method can successfully capture bubble generation, detachment, and rise behaviors, offering insights into understanding bubble dynamics and potential applications in 3D computational fluid dynamics validation.

Investigation of Improved Energy Dissipation in Stepped Spillways Applying Bubble Image Velocimetry

This study investigates skimming flow regimes, two-phase air–water flow conditions, and simple measures to improve energy dissipation in stepped spillways. Experiments were conducted using two different scale physical models, 1:50 and 1:17, within separate rectangular flumes to define scale effects. Flow patterns were analyzed using the Bubble Image Velocimetry (BIV) technique, which tracks air bubbles. The introduction of splitters resulted in a 7% increase in relative energy dissipation. Additionally, the length of inception was reduced to Li/ks = 10, thereby decreasing the potential for subsequent cavitation. Beyond the BIV experiments, two experiments were conducted on the large-scale model using Acoustic Doppler Velocimetry (ADV), with and without splitters, to examine the impact of splitters on the velocity profile above the crest. In the experiment with splitters, the vertical velocity vector (v) contributed to turbulence by changing direction, thereby reducing average velocities both in front of and behind the ogee crest. This led to a reduction in energy on the downstream side of the spillway. Although the small-scale model appears unsuitable for studying two-phase flow, the change in relative energy dissipation from the baseline to the splitter configuration was practically identical for both scale models, thereby supporting the findings of the large-scale model.

BubSAM: Bubble segmentation and shape reconstruction based on Segment Anything Model of bubbly flow

AbstractAccurate detection and analysis of bubble size and shape in bubbly flow are critical to understanding mass and heat transfer processes. Convolutional neural networks have limitations in different bubble images due to their dependence on large amounts of labeled data. A new foundational Segment Anything Model (SAM) recently attracts lots of attention for its zero‐shot segmentation performance. Herein, we developed a novel image processing method named bubSAM, which achieves efficient and accurate bubble segmentation and shape reconstruction based on SAM. The segmentation performance of bubSAM is 30% higher than that of SAM, and its accuracy reaches 90% under different bubbly flow conditions. The accuracy of bubble shape reconstruction (BSR) algorithm in bubSAM is about 30% higher than that of typical ellipse fitting method, thus better restoring the geometric shape of bubbles. BubSAM can provide great support for understanding gas–liquid multiphase flow and design of industrial multiphase reactors.

Bubble image segmentation and dynamic feature extraction in gas–liquid two-phase transient flow

Bubble image segmentation and dynamic feature extraction in gas–liquid two-phase transient flow

Realizing enhanced lithotripsy efficiency using 700 W peak power thulium-doped fiber laser

Despite its widespread use in laser lithotripsy, the Ho:YAG solid-state laser suffers from limited parameter combinations, poor beam quality, and low photoelectric conversion efficiency. The development of 2.0 μm fiber laser technology has positioned the thulium-doped fiber laser (TFL) as a superior alternative to the Ho:YAG laser in lithotripsy. The TFL excels because of its high-water absorption coefficient, all-fiber structure and pump modulation flexibility. However, the peak power reported for TFL in lithotripsy remains at most 500 W. This study presented a quasi-continuous wave (QCW) TFL featuring a wide tunable range of pulse width and pulse repetition rates, and a peak power of 700 W to realize enhanced ablation efficiency. A high-speed camera was used to acquire the laser-induced cavitation bubble images for the analysis of the interaction mechanism of a high peak power QCW TFL pulse with water and stones. While the synergistic action of photothermal and photomechanical effects constituted the mechanism of TFL lithotripsy, the laser-induced bubble channel was a prerequisite for TFL noncontact effective lithotripsy, with bubble length and width markedly influencing stone ablation rates. Ablation experiments on uric acid and calcium oxalate monohydrate stones conducted with various parameter combinations at a peak power of 700 W yielded rates of 8.9 mg/s for uric acid stones and 7.5 mg/s for calcium oxalate monohydrate stones. A comparison with prior findings confirmed that a high peak power TFL could notably improve stone ablation rates.

Deep learning-based bubble detection with swin transformer

ABSTRACT We developed a deep learning-based bubble detector with a Shifted window Transformer (Swin Transformer) to detect and segment individual bubbles among overlapping bubbles. To verify the performance of the detector, we calculated its average precision (AP) with different number of training images. The mask AP increased with the increase in the number of training images when there were less than 50 images. It was observed that the AP for the Swin Transformer and ResNet were almost the same when there were more than 50 images; however, when few training images were used, the AP of the Swin Transformer were higher than that of the ResNet. Furthermore, for the increase in void fraction, the AP of the Swin Transformer showed a decrease similar to that in the case of the ResNet; however, for few training images, the AP of the Swin Transformer was higher than that of the ResNet in all void fractions. Moreover, we confirmed the detector trained with experimental and synthetic bubble images was able to segment overlapping bubbles and deformed bubbles in a bubbly flow experiment. Thus, we verified that the new bubble detector with Swin Transformer provided higher AP than the detector with ResNet for fewer training images.

Imaging of foam concrete air bubbles with an alternative method of combined digital holographic microscopy

Five different foam concretes were synthesized and examined. A new hybrid optical sensor, called combined digital holographic microscopy (CDHM), was proposed by combining microscopic fringe projection profilometry and lateral shearing digital holographic microscopy to detect the pore radii of produced foamed concretes. It was applied in addition to SEM and has not been applied to foam concretes before. Thanks to the proposed method, it was revealed that the measured CDHM radii contained a relative error of less than 6% compared to the SEM radii. The pore radii increased as the % of foaming agent used in the samples increased. Accordingly, the sample densities decreased and thermal insulation properties enhanced. Two-layer quantum chemical calculations performed at the ONIOM (M06-2X/6-31+G(d,p):UFF) theoretical level showed that thermodynamic stability of foam concretes increased as the % of foaming agent used, or more precisely, the pore radius, increased. The CDHM method provides results close to SEM and has superior features such as being more cost-effective, cleaner and faster. For this reason, it is thought that the proposed method will lead to future studies in terms of measuring pore radii as an alternative to SEM.Graphical The combined digital holographic microscopy (CDHM) method is proposed as an alternative to SEM with a relative error of less than 6% in determining the pore radius of foam concretes.

Overtopping Flow Velocity Characterisation Of Focused Waves On Promenades Using The Bubble Image Velocimetry Technique

This study characterises the flow velocity of individual extreme waves that overtop promenades using the bubble image velocimetry (BIV) technique (Ryu et al., 2005). Experimental tests were carried out in the small-scale wave flume CIEMito, at the Marine Engineering Laboratory (LIM) of the Universitat Politècnica de Catalunya – BarcelonaTech (UPC), and the obtained images were post-processed to calculate the flow velocities. The ultimate objective of the experimental campaign is to develop more precise models for forecasting wave overtopping of structures with an emergent toe, commonly found on sandy beaches and frequently utilized as promenades or waterfronts in most urbanized coastal environments. The NewWave theory (Tromans et al., 1991) was used to simulate the extreme individual wave overtopping in a real random sea state. The NewWave theory establishes a correlation between the expected form of a large wave in a linear sea state and the bulk characteristics of the sea state. Using focused wave groups instead of long-duration irregular wave time series offers several benefits. It improves the ability to repeat experiments and enhances measurement capabilities by providing greater temporal resolution in models used to investigate significant wave interactions (Hofland et al., 2014). Additionally, due to the compactness of focused wave groups, wave absorption becomes unnecessary. The research identifies two distinct case studies with varying wave forcing, tidal regimes and coastal layouts. This work presents a case study of a typical Mediterranean Sea configuration, which is a micro-tidal environment with steep and relatively short foreshores and pocket beaches. The study aims to characterize the overtopping flow velocity on the selected structure, and the feasibility of non-intrusive measurements such as a BIV technique has been investigated. The work includes preliminary results.

Detecting irradiation defects in materials: A machine learning approach to analyze helium bubble images

Additive manufacturing technology has received significant attention in the field of nuclear materials due to its potential to improve the radiation resistance of materials and components. In our research, we propose to use 304 L stainless steel fabricated by Selective Laser Melting (SLM) as a replacement for traditional casting due to its superior performance and optimized manufacturing technique. During testing of its radiation resistance, we found that analyzing helium bubbles captured by transmission electron microscopy (TEM) is crucial for understanding and predicting material behavior under irradiation. However, this task typically involves manual counting, which is both time-consuming and prone to errors due to the fatigue of human annotators. To address this challenge, we present a novel machine learning model for automatic helium bubbles detection and counting through images. Our model leverages a fusion of traditional computer vision techniques with cutting-edge machine learning methods to achieve superior detection of helium bubbles in nuclear materials. This approach is based on two core designs: a novel generative model to generate training data and a gradient convergence layer to limit the range of parameters. Experiments show that our model outperforms previous state-of-the-art unsupervised detectors, and achieves highly accurate helium bubble segmentation in a few-shot scenario, with an F1 score typically exceeding 90 % and an average size error less than 0.1 nm. Our model achieves over 100 times more efficiency than manual counting, which takes approximately 20 s per image. Our work demonstrates a successful collaboration between the fields of material characterization and artificial intelligence (AI). By leveraging the respective strengths of material science and machine learning, we have achieved surprising results that could have a significant impact on the development of new materials and components for nuclear applications.

Data-driven diagnostics of boiling heat transfer on flat heaters from non-intrusive visualization

Rankine-based power generation cycles require the use of boiling heat exchangers for liquid–vapor phase change. However, operating boilers at too high heat fluxes can lead to heater meltdown due to onset of the boiling crisis. Despite recent advances in increasing this critical heat flux, the lack of fast and accurate diagnostics hinders boiling heat transfer safety at high heat fluxes. Here we show that neural networks fed on direct observation of bubble dynamics can be used to measure heat flux during nucleate boiling. The boiling heat transfer data was obtained with a horizontally oriented, circular flat heater with saturated water as the heat transfer fluid. A deep convolutional neural network (CNN) was trained using frames from videos acquired from 15 different heat dissipation values on the boiling curve to learn how these images correlate to the underlying heat flux. The CNN predicted heat fluxes with a mean square error of 14.6 W/cm2. Furthermore, the CNN presented high sensitivity to pixel intensity in image regions relevant to boiling heat transfer physics (e.g. bubble boundaries, contextual clues about bubble image distortion). Understanding this approach may allow future studies in unstable and critical boiling regimes that require fast transient heat flux diagnostics and cannot rely on slower thermohydraulic measurements.

Experimental study on flow kinematics of breaking wave impinging and overtopping on a deck structure

The present study experimentally investigates the flow kinematics due to breaking waves impinging and overtopping on a simplified deck structure. Several breaking-wave impinging scenarios in relation to the model deck are conducted. The resulting flow velocities in the aerated regions are measured using bubble image velocimetry (BIV). A wave focusing method is employed to generate plunging breakers. Repeated measurements are performed to obtain mean flow and turbulence characteristics. Two distinguished flow behaviors are observed among all seven scenarios. For the cases of a fully developed plunging breaker interacting with the deck structure, the maximum horizontal velocities above and below the structure are both around 1.2C, with C being the phase speed of the breaking wave; for the cases of a non-fully-developed plunging breaker impingement, the flow velocity below the deck is surprisingly large, with a value of 1.8C below the deck against 1.2C above the deck. Furthermore, the use of a dam-break flow to model the overtopping horizontal velocities on the deck is performed with satisfactory agreements for all the impinging scenarios. Moreover, the temporal and spatial varying prediction equation proposed by Ryu et al. (2007b) is used to model overtopping flow velocities, showing the applicability of this simplified methodology.

Improvement of contrast ratio between cavitation bubbles and tissue by frequency filtering in triplet pulse ultrasound imaging

Bubble-enhanced high-intensity focused ultrasound treatment requires selective imaging of cavitation bubbles to ensure their localization to the target tissue. Previous studies have proposed the ultrasound imaging of cavitation bubbles with a filtering method, which makes bubble extraction by a triplet pulse sequence more selective. The envelope component as well as harmonic components are generated during the nonlinear propagation of an ultrasonic imaging pulse, but the triplet pulse sequence can significantly reduce only the harmonic components, and its selectivity is degraded by the residual envelope component. In this study, the effects of nonlinear propagation at various intensities of imaging pulse and the frequency filtering to obtain better selectivity are investigated for the triplet pulse sequence with a filtering method. The result shows that the imaging pulse intensity is an important parameter that determines the strength of the effect of nonlinear propagation and that the optimal frequency filtering changes according to it.

Artificial neural network (ANN) modelling to estimate bubble size from macroscopic image and object features

Bubble size measurements in aerated systems such as froth flotation cells are critical for controlling gas dispersion. Commonly, bubbles are measured by obtaining representative photographs, which are then analyzed using segmentation and identification software tools. Recent developments have focused on enhancing these segmentation tools. However, the main challenges around complex bubble cluster segmentation remain unresolved, while the tools to tackle these challenges have become increasingly complex and computationally expensive. In this work, we propose an alternative solution, circumventing the need for image segmentation and bubble identification. An Artificial Neural Network (ANN) was trained to estimate the Sauter mean bubble size (D<sub>32</sub>) based on macroscopic image features obtained with simple and inexpensive image analysis. The results showed excellent prediction accuracy, with a correlation coefficient, R, over 0.998 in the testing stage, and without bias in its error distribution. This machine learning tool paves the way for robust and fast estimation of bubble size under complex bubble images, without the need of image segmentation.

The effects of secondary cavitation position on the velocity of a laser-induced microjet extracted using explainable artificial intelligence

The control of the velocity of a high-speed laser-induced microjet is crucial in applications such as needle-free injection. Previous studies have indicated that the jet velocity is heavily influenced by the volumes of secondary cavitation bubbles generated through laser absorption. However, there has been a lack of investigation of the relationship between the positions of secondary cavitation bubbles and the jet velocity. In this study, we investigate the effects of secondary cavitation on the jet velocity of laser-induced microjets extracted using explainable artificial intelligence (XAI). An XAI is used to classify the jet velocity from images of secondary cavitation and to extract features from the images through visualization of the classification process. For this purpose, we run 1000 experiments and collect the corresponding images. The XAI model, which is a feedforward neural network (FNN), is trained to classify the jet velocity from the images of secondary cavitation bubbles. After achieving a high classification accuracy, we analyze the classification process of the FNN. The predictions of the FNN, when considering the secondary cavitation positions, show a higher correlation with the jet velocity than the results considering only secondary cavitation volumes. Further investigation suggested that secondary cavitation that occurs closer to the laser focus position has a higher acceleration effect. These results suggest that the velocity of a high-speed microjet is also affected by the secondary cavitation position.