Particle Image Velocimetry Method Research Articles

Gravitational modes of convection in water liquid bridges

The modes of pure buoyant (thermogravitational) convection emerging in a liquid bridge of water (Pr ≅ 6.1), uniformly heated from below and cooled from above are investigated experimentally by means of a microscale facility, a related laser-cut technique (used to illuminate isodense tracers dispersed in the liquid) and a particle image velocimetry method. In particular, the following conditions are examined: aspect ratio (A = length/diameter) in the range 0.3 ≤ A ≤ 0.9, volume ratio 0.7 ≤ S ≤ 1.3, and Rayleigh number spanning the interval from the initial quiescent state up to the development of oscillatory motion. A multitude of patterns is obtained, revealing the coexistence of different branches of steady flows in the space of parameters in the form of multiple solutions. These can evolve into oscillatory states featured by disturbances with the characteristics of standing waves (a kind of rocking motion). The analysis largely relies on a novel approach where the position of the center of the main vortex of buoyant nature established in the liquid bridge is carefully monitored in space. The related trajectory is used to discern the flow spatial degrees of freedom, which are progressively enabled as the temperature difference is increased. It is shown that the effective volume of liquid held by surface tension between the hot and cold walls can have an appreciable impact on the onset of unsteadiness and the related oscillation frequency.

Soft-sensor model for indoor temperature prediction under heating conditions

Temperature information is important in everyday life. However, the local temperature at a certain point cannot be determined directly without using a physical thermometer, which can often disturb activity at that point and consume additional energy. Moreover, it would be costly to equip a room with many thermometers simply to measure local temperature. To overcome this problem, this study developed a model for prediction of local indoor temperature under heating conditions based on the heat transfer process mechanism using the soft-sensing method. To simulate the heating of air conditioning in winter, water at temperature of 30/50 °C was fed into water at 10 °C in an acrylic box. The soft-sensor model was trained and validated with transient temperature data and the multiple linear regression method. The particle image velocimetry method was used to obtain the velocity field. Temperature inhomogeneity in the vertical direction was evident for the feed temperature of 50 °C. The temperature variation in the bottom layer was divided into the dead time, rising, and settling periods, which was caused by variation of flow state. Multiple evaluation indicators suggested that the model performed with acceptable accuracy for different feed temperatures and locations. The bottom layer model at 50 °C required correction for the dead time, and model accuracy was improved by separately modeling the rising and settling periods. This soft sensor could be installed in air-conditioning systems to achieve optimal control, provide user comfort, and save energy.

Anchor chain profiles in sand based on centrifuge model tests

The rapid development of floating wind turbines (FWTs) has brought new challenges to their mooring systems in shallow water of 50-200 m. The sand seabed, which is widely distributed in offshore regions, is often encountered in the service of FWTs and influences the anchor and mooring line design. The anchor chain is primarily adopted as the part interacting with the seabed, and its configuration in the seabed is vital for anchor design. However, a complete understanding of the chain configuration in sand is yet to be achieved. To address this, a group of centrifuge model tests using a model chain were conducted to simulate the chain embedment behavior in carbonate sand. During the tests, the chain position and soil deformation were captured using the Particle Image Velocimetry (PIV) method. A calculation model was adopted to predict the chain configuration in the sand, compare it with the centrifuge test results, and then analyze the differences between them. Finally, suggestions for model improvement were discussed.

Aerodynamic Analysis of Variable Camber-Morphing Airfoils with Substantial Camber Deflections

In recent years, morphing wings have become not only a concept, but an aerodynamic solution for the aviation industry to take a step forward toward future technologies. However, continuously morphing airfoils became an interesting answer to provide green energy solutions. In this paper, the authors conducted experimental research on a continuously camber-morphing airfoil using the Particle Image Velocimetry (PIV) and Computational Fluid Dynamics (CFD) methods. The main objective of this work was to research a variety of morphing airfoils with different camber deflections. An average velocity distribution and turbulence distribution were compared and are discussed. The two-dimensional PIV results were compared to the CFD simulations to validate the numerical method’s accuracy and obtain the aerodynamic coefficient’s trends. A further comparison revealed that morphing airfoils have better aerodynamic performance than conventional airfoils for very low camber deflections and create substantial amounts of drag for significant camber deflections.

Three-dimensional particle image velocimetry measurement through three-dimensional U-Net neural network

This paper proposes a light field (LF) three-dimensional (3D) particle image velocimetry (PIV) method based on a digital refocused algorithm and 3D U-Net neural network for 3D three-component (3D-3C) velocity measurement. A digital refocused algorithm is used to generate a stack of LF-refocused images of tracer particles for establishing the 3D U-Net. The 3D U-Net is then used for the 3D particle field reconstruction. Based on a pair of 3D particle fields, the 3D-3C velocity field is obtained through a 3D cross correlation algorithm. Numerical simulations and experiments are conducted to analyze the accuracy and efficiency of the proposed method. The simulation results show that the elongation along the depth direction and the efficiency of the 3D particle field reconstruction are improved by the 3D U-Net. The 3D U-Net also provides a better correlation coefficient. The experimental results show that the reconstruction time of the proposed method is ∼220 s which is 10 times faster than the LF tomographic PIV. This further demonstrates that the proposed method improves the reconstruction efficiency without affecting the accuracy of velocity measurement.

Marangoni Convection Velocity in Nonlinear Hanging-Droplet Vibration Phenomena

The Marangoni effect involves a mass transfer along an interface between two phases owing to the gradient of the interfacial tension. The flow caused by this phenomenon is called Marangoni convection, a complex phenomenon that involves mass transfer processes, such as surfactant adsorption/desorption processes, solvent dissolution phenomena, and viscous dissipation processes. Therefore, the strength of the convection depends on the various thermodynamic and physical properties of the fluids. In this study, we experimentally investigated the relationship between the Marangoni convection generated inside a hanging oil droplet and the interfacial tension of the oil droplet in an aqueous phase by the particle image velocimetry method. This convection velocity depended on the initial value of the interfacial tension in the oil–water interfacial tension oscillation phenomenon accompanied by the expansion and contraction of the hanging drop. Additionally, the droplet oscillation frequency decreased as the Marangoni convection velocity increased. Furthermore, continuous convection, which is unlike Marangoni convection, was observed within this spontaneously expanding and contracting hanging-droplet system. This buoyant convection was caused by the mutual dissolution of the hanging-droplet oil phase and the surrounding aqueous phase.

A method of applying deep learning based optical flow algorithm to river flow discharge measurement

River flow discharge monitoring is one of the critical tasks performed at hydrological stations. The large-scale particle image velocimetry (LSPIV) method widely used in hydrological stations is often limited by a lack of floating objects and has a high computational complexity. The space-time image velocimetry method is susceptible to noise interference and requires high stability of the flow over time. This paper proposes a flow measurement method based on the recurrent all-pairs field transforms for optical flow (RAFT) algorithm. The 4D correlation volume pyramid in the RAFT network structure can effectively handle changing and complex flow conditions. The convolutional block attention module is introduced into the optical flow update module after the 4D correlation volume pyramid, enhancing the ability to capture complex flow surface information. Additionally, feature extraction adds deformable convolution to expand the receptive field of the flow image, which has better adaptability in non-rigid motion. To validate the effectiveness of the new method (RAFT-D-C), this paper conducts comparative experiments with both existing and new methods. The experimental results show that RAFT-D-C has relative errors of 2.13% and 4.41% for the average flow velocity of two rivers and relative errors of 2.19% and 3.05% for the total discharge, respectively. RAFT-D-C provides improved accuracy compared to other methods and requires less computational run time than the frequently used LSPIV method.

Experimental Investigation on a convex Mode-2 internal solitary wave and its induced flow field

The second mode internal solitary wave (Mode-2 ISW) and its wave-induced flow field structure are investigated by conducted physical experiments in a large stratified fluid flume. The Mode-2 ISW is generated by the method of the gravity collapse of fluid, and the measurements of its wave-flow structure are obtained by the combined method of conductivity probe array and particle image velocimetry (PIV). The flow field structure and its influencing factors were analysed quantitatively. The results reveal the characteristic of the Mode-2 ISW waveform and density field and the effect of the wave amplitude on the flow and shear characteristics of induced flow field.

High-resolution velocity determination from particle images via neural networks with optical flow velocimetry regularization

Particle image velocimetry (PIV) and optical flow velocimetry (OFV) are important velocity measurement methods in the field of fluid dynamics. Nevertheless, the conventional cross correlation-based PIV method is beset by diminished resolution, while the OFV method exhibits computational sluggishness and susceptibility to noise. These constraints have somewhat delimited the applicability of PIV and OFV techniques. Recent attempts have introduced deep learning-based methods for analyzing PIV images, offering high-resolution velocity fields with computational efficiency, but their accuracy needs improvement. This study proposes four neural networks based on the well-established FlowNetS. They incorporate two distinct velocity constraints, namely, first-order velocity smoothing regularization and second-order grad (curl)–grad (div) regularization. In the networks, these constraints are used either independently or in combination with optical flow conservation (OFC). The performances of the networks have been assessed on six different flow configurations, and the results show that the network with the second-order regularization markedly outperforms the original network across all flows, demonstrating an enhanced capacity to capture larger-scale vortices. The network with the first-order regularization also exhibits superior performance compared to the original network except in the case of cylinder flow. Unexpectedly, the introduction of the OFC constraints results in a decline in network performance. This anomaly may stem from the network's inherent capability to capture optical flow features, rendering the OFC constraint less effective in providing guidance. In summary, this study underscores the substantial potential of neural networks incorporated with judicious physical constraints in PIV applications, enabling the determination of high-resolution, high-accuracy flow fields.

Microdischarge dynamics of volume DBD under the natural convection airflow

The dielectric barrier discharge (DBD) at ambient air conditions exhibits discrete structure and contains thin microdischarge plasma filaments. Understanding the formation, self-interaction, and dynamics of such filaments is crucial towards the generation of uniform diffuse-like DBD in air, and also for the study of the memory effects and self-organized complex patterns. In this paper, the impact of natural convective flow, driven by the temperature gradient between self-heated discharge cell electrodes and ambient air, on the collective dynamics of microdischarges was studied in parallel-plate volume dielectric barrier discharge for the different geometrical arrangements. The horizontal arrangement corresponds to the parallel direction of discharge propagation and convective flow, while in the vertical arrangement buoyancy flow transverse to the discharge propagation column. For the horizontal arrangement, the randomly directed motion of microdischarges was observed. While at the vertical arrangement of the discharge cell, the buoyancy flow initiates the directed motion of the microdischarges following the gas flow. The continuous self-heating of the barrier electrodes during the DBD operation leads to a larger thermal gradient and increasing in the microdischarge channels. The larger thermal gradient results in a more pronounced directed motion of microdischarges with a higher velocity. The velocity of convective flow in the discharge gap was estimated by the simulation and compared with the mean velocity of MD channels obtained by the particle image velocimetry method.

The Implementation of Micro/Nanobubbles (MNBs) Technology to Treat Basin Water as The Primary Water Source for Hydroponics in Greenhouse

The greenhouse plays a pivotal role in creating an ideal environment for hydroponic cultivation. The greenhouse has utilized rainwater and basin water as a source of raw water for hydroponic farming. Presently, the water quality of Leuwi Padjadjaran basin fails to meet the standards required for hydroponics due to its turbidity, sediment content, discoloration, pH levels exceeding 7, and low dissolved oxygen (DO) concentration of 2.2 mg/l. The micro/nanobubbles (MNBs) technology stands as a viable method for water treatment owing to its capacity to bind impurities via radical OH. The application of MNBs for the treatment of basin water involves the use of a hydrodynamic cavitation MNBs generator with a dual-chamber rotating flow nozzle. The parameters evaluated in this research encompass DO concentration, MNBs stability, microbubble size, and the visual response to MNBs application. MNBs treatment was conducted with three different gases: air, oxygen, and ozone. Microbubbles were measured using the particle image velocimetry (PIV) method. The DO concentration reaches 21.6 mg/l when employing oxygen-based MNBs. On the third day post-generation, MNBs stability still maintains DO concentrations above the initial levels. Thus it can be used as hydroponic raw water. Keywords: DO concentration, Greenhouse, Micro/nanobubbles, Water treatment.

Hydraulic performance of bottom intake velocity caps using PIV and OpenFOAM methods

The objective of this investigation is to obtain a more profound understanding of the effective parameters of the velocity caps for bottom intake systems, utilizing particle image velocimetry (PIV) and OpenFOAM. Observations indicate a higher probability of surface vortex formation in square types compared to circular ones, with the vortex being formed downstream of the caps. Additionally, the flow pattern reveals that the flow whirls in a more favorable path into the circular caps as opposed to the square ones. Through both experimental and numerical comparisons of three shapes (rhombus, square, and circle), it becomes evident that the circular type outperforms the other types in terms of discharges through the intake, showing an improvement of about 8%. The results indicate that flow depth and height of the velocity caps are positively effective parameters for the flow rate, with respective influences of 90% and 30%. In contrast, the interaction between the flow and caps intensifies with an increase in the distance of the intake opening from the bed, which plays a negative influence on the flow rate. Enhancing the number of blades in caps proves to be the optimal approach for generating a smoother flow with minimal impact on the flow rate. Numerical simulations show a 50% reduction in cap height leads to a significant 33% decrease in flow rate. Additionally, rotating the square cap by 45° into a rhombus aligned with the flow direction results in a 7% discharge flow rate increase.

Surface Current Measurement Using Large Scale Particle Image Velocimetry (LSPIV) in Ender Estuary, Cirebon

The Large-Scale Particle Image Velocimetry (LSPIV) method is an image- based technique that uses a non-intrusive approach or avoids direct physical contact with the water being studied to measure the velocity of water flow. The present study aims to identify surface velocity using LSPIV captured by Unmanned Aerial Vehicle (UAV) in Ender estuary, Cirebon. Field observation for surface velocity was conducted at three locations during flood and ebb tides. The surface current results of LSPIV compared with in situ velocity measurement by a current meter. The results of the LSPIV method are strongly influenced by the data acquisition process such as lighting, interrogation area, and video recording angle. Based on comparing the surface flow between results of LSPIV and observation data, the coefficient correlation (R) is 0.84 for velocity magnitude and R = 0.92 for flow direction. The Root Mean Square Error (RMSE) of 0.024 m/s for velocity magnitude and 4.81 degree for flow direction, and the Mean Absolute Error (MAE) range from 0.002 to 0.059 m/s (1 to 12 degree) for the velocity magnitude and the flow direction, respectively. Furthermore, the Mean Absolute Percentage Error (MAPE) value of 10.41% (8.84%) for magnitude and direction, respectively. Therefore, LSPIV was applied successfully to obtain surface velocity measurements.

Investigation of the limiting factors of shoulder joint complex motion in college baseball players: motion analysis of the humeral head and rotator cuff using ultrasound

BackgroundThe relationship between lower mobility, as measured by the elbow forward translation motion (T-motion) test, a new indicator of shoulder joint complex movement that measures elbow position when both dorsal hands are placed on the iliac crest while in a sitting position, and the parameters calculated by ultrasonography is unknown. The purpose of this study was to investigate the limiting factors of T-motion through motion analysis of the humeral head and rotator cuff muscles using ultrasonography in college baseball players. MethodsThirteen college baseball players participated in this cross-sectional study. The shortest distance from the posterior edge of the glenoid to the humeral head was measured in the static and T-motion positions, and the difference was calculated as the humeral head translation. The velocity of the infraspinatus was calculated during shoulder internal/external rotation using the particle image velocimetry method. These parameters were compared between the throwing and nonthrowing sides to examine the limiting factors of T-motion. ResultsThis study indicated moderate-to-good reliability for the parameters calculated by ultrasonography. The mean anterior translation distance was significantly greater on the throwing side than on the nonthrowing side (r=0.56, p=.015). The mean velocity of infraspinatus during internal rotation was significantly lower on the throwing side than on the nonthrowing side (r=0.51, p=.028). ConclusionIncreased anterior translation of the humeral head and decreased the velocity of infraspinatus are likely correlated with reduced T-motion mobility in college baseball players. These methods showed potential for physical therapy assessment and intervention to prevent shoulder dysfunction.

Experimental study on flow characteristics of ash band in a cyclone separator using HPIV techniques

In a reversed cyclone separator, the ash bands were frequently observed on the wall surface, which were formed by the separated particles and rotated down. Nevertheless, the flow characteristics of the ash bands are rarely measured and discussed in the previous literatures. In this study, the flow characteristics of the ash bands were experimentally investigated by use of the High-speed Particle Image Velocimetry (HPIV) method. The experiments were conducted on a ϕ160mm cyclone at inlet velocity of 12, 16, 20 m/s and inlet concentration of 30, 50, 70, 100 g/m3. The test powders were catalyst and iron powder. The ash band's geometric characteristics, including width, slope angle and pitch, were extracted through advanced image analysis algorithms. Results revealed that the slope angle and pitch of the ash band were different in the cylindrical section and cone section. The width decreased as the ash band moved downwardly. Additionally, the ash band's flow parameters were measured. As the particle movement changed from suspension pattern in the separation space to sliding pattern on the wall surface along the radial, the particle velocities decreased. And the particle velocities further decreased as ash band forming. Particle velocities exhibited non-uniform and fluctuating behavior within the ash band. Notably, the velocities were lower in the central region of the ash band than that at the outer layer. Meanwhile, the particle concentration gradually increased, leading to a particle concentration in the ash band being 450 times higher than the inlet concentration. Along the axial, the ash band averaged velocity initially increased in the cylindrical section and then decreased in the cone section, varied with the inlet velocity, inlet concentration and particle properties such as particle size and density. This study revealed mechanisms behind particle aggregation in cyclone separator and analyzed the dynamic flow characteristics of ash band in detail.

Tracking plume-regolith interactions in near-vacuum conditions

An experiment to track and measure the transient phenomenon of plume-liberated regolith in near-vacuum conditions was performed in a dedicated plume-regolith facility housed at the University of Glasgow. This facility with a total volume of around 82 m3 can simulate a soft or hard landing event on “extraterrestrial” sub-atmospheric pressures. Particle image velocimetry method was used to estimate the ejection velocity and ejected angle of regolith particles, and its limitations are discussed. Glass microspheres that are matched with the size of the Lunar and Martian moon “Phobos” surface regoliths are used as simulants. With an exit Mach number of 6.6, a heated convergent–divergent nozzle represents the lander nozzle. Preliminary results capture ejecta development up to 30 ms from plume impingement. Flow visualization reveals the initial moments of plume boundary growth and regolith ejection. The vector images indicate a triangular-shaped sheet of particles sweeping from the regolith bed at a positive inclination with a local maximum velocity close to 100 m/s. The low-density “Phobos” simulant advances at a higher speed, reaches higher elevations, and covers a larger spatial area compared to a higher-density “Lunar” simulant. Observation of the crater formation reveals the difference in cohesive forces between the selected simulants. A higher inclination of particle ejection of more than 50° adjacent to the jet indicates particle entrainment originating from the interior of the crater. Stream traces reveal the deflection of ejected particles upon impingement on the lander surface at close proximity.

Microchannel‐Confined Chinese Ink‐Based Highly Stretchable Liquid‐State Strain Sensor for Early Warning of Road Collapse

AbstractStretchable strain sensors are widely used in the fields of wearable devices, soft robotics, healthcare monitoring, and more. Despite tremendous efforts, strain sensors capable of detecting large‐scale soil deformation for urban geological hazard prevention have not yet been demonstrated. In this paper, a soft strain sensor of a highly stretchable Ecoflex‐0030 elastomeric matrix with microchannel‐confined environmentally benign conductive Chinese ink as the liquid‐state strain‐sensitive material that can reliably detect a wide range of working strain up to 300% is demonstrated. The sensor exhibits a negligible hysteresis error of 2.1%, with a considerably good gauge factor of 1.95. The sensor performance is evaluated under both creep and cyclic loading tests, indicating superior stability (0.65% fluctuation), and repeatability (0.28% variation in 1000 cycles), even under a lower underground environment temperature of 18°C. As a proof‐of‐concept demonstration, a liquid‐state strain sensor array with a strain amplification mechanism capable of accurately monitoring the formation of various underground cavities and providing timely demanded information for early warning of catastrophic road collapse is demonstrated and verified by the computer vision‐based particle image velocimetry (PIV) method.

Meta-Lens Particle Image Velocimetry.

Fluid flow behavior is visualized through particle image velocimetry (PIV) for understanding and studying experimental fluid dynamics. However, traditional PIV methods require multiple cameras and conventional lens systems for image acquisition to resolve multi-dimensional velocity fields. In turn, it introduces complexity to the entire system. Meta-lenses are advanced flat optical devices composed of artificial nanoantenna arrays. It can manipulate the wavefront of light with the advantages of ultrathin, compact, and no spherical aberration. Meta-lenses offer novel functionalities and promise to replace traditional optical imaging systems. Here, a binocular meta-lens PIV technique is proposed, where a pair of GaN meta-lenses are fabricated on one substrate and integrated with a imagingsensor to form a compact binocular PIV system. The meta-lens weigh only 116mg, much lighter than commercial lenses. The3Dvelocity field can be obtained by the binocular disparity and particle image displacement information of fluid flow. The measurement error of vortex-ring diameter is ≈1.25% experimentally validates via a Reynolds-number (Re) 2000 vortex-ring. This work demonstrates a new development trend for the PIV technique for rejuvenating traditional flow diagnostic tools toward a more compact, easy-to-deploy technique. It enables further miniaturization and low-power systems for portable, field-use, and space-constrained PIV applications.

Experimental and Numerical Investigation of the Control of the Flow Structure on Surface Modified Airfoils

In this study, experimental and numerical flow analysis was performed on three different blade profiles with a chord length of 165 mm using passive flow control method. The first of the airfoil is the standard NACA 0018 profile. The second airfoil type has a NACA 0018 profile with a gap in the suction surface. The last airfoil is the NACA 0018 profile which is 66% of the trailing edge cut from the chord length. All airfoil profiles were analyzed at the Reynolds number, Re=2x104, and angles of attack α=0o, 5o, 10o, 12o and 14o in both experiment and numerical studies. The experiments were carried out using the Particle Image Velocimetry (PIV) method in a closed-loop open water channel, and the time-averaged velocity vectors, streamlines, and vorticity contours of the flow field were obtained. Subsequently, numerical analyses were performed using the ANSYS Fluent package program, one of the Computational Fluid Dynamics (CFD) programs used frequently in the literature. The streamlines and pressure contours of the airfoil profiles have been compared visually at the same Re and different angles of attack. In addition, according to the angle of attack of the airfoil profiles, lift coefficient CL, drag coefficient CD, and the ratio of lift coefficient to drag coefficient CL/CD graphs were presented. It has been shown that the gap on the airfoil at high attack angles caused changes in lift (up to 0.7) and drag (up to 0.15). These features can allow these models to be used for different purposes in the aerodynamics field.

High level GPU-accelerated 2D PIV framework in Python

The Particle Image Velocimetry (PIV) method is widely used for optical measurment of flow velocity fields. This paper demonstrates the possibilities of using high-level libraries for GPU-accelerated PIV data analysis in Python. The Torch PIV library for the analysis of 2D PIV experiments based on the deep learning framework PyTorch with CUDA support was developed. The library implements a multi pass cross-correlation FFT PIV algorithm with an interrogation window shift. The chosen implementation does not require compilation from the user, has a compact codebase, is able to run both on the CPU and the GPU depending on the user choice, and also it is as flexible as the Python module. In this work, the performance of the CPU version of the developed method was compared with existing open source implementations. It is shown that the main functions of the developed module can be executed on the GPU at the speed of CUDA implementations. The developed library is tested on synthetic images and experimental data. Program SummaryProgram title: TorchPIV.CPC Library link to program files: https://doi.org/10.17632/yw43vjc36h.1.Developer's repository link: https://github.com/NikNazarov/TorchPIV.Licensing provisions: MIT license.Programming language: Python.Nature of problem: PIV experiments often require analyzing a large number of images in order to determine the statistical characteristics of the flow. GPUs are actively used in this field to speed up the data analysis process. Open-source software solutions to this problem usually require build and are difficult to integrate into the analysis pipelines.Solution method: The main feature of the developed module is its flexibility and simple distribution. The module is cross-platform, and installation does not require compilation from the user. The developed library can be imported as an ordinary Python module, at the same time it allows to get a significant performance gain when analyzing PIV experiments by using NVIDIA GPUs. Implementation in pure Python allows the module to serve as a backend for more complex experimental data processing systems. The core library of this method is one of the most reliable and widespread in the field of machine learning.