Particle Image Research Articles

Effects of variable valve timing and duration on catalyst heating using optically accessible MPI engine

The effects of intake and exhaust valve timing and duration were investigated using an optically accessible multi-point injection engine under catalytic heating conditions. The target engine features a quartz cylinder and an extended piston with a quartz piston crown. To understand the effects of valve timing and duration on the in-cylinder flow, particle image velocimetry (PIV) was applied to visualize the in-cylinder flow. The acquired images were used to quantitatively evaluate the in-cylinder flow characteristics, and the effects of these flow characteristics on the flame propagation were analyzed through flame visualization experiments. The results indicate that when the intake valve timing was advanced by 20°, the average flow velocity was 59.5% higher, the tumble ratio was 72.2% greater, and the flame propagation velocity was also the fastest. The longest duration of 218° was advantageous for the intake valve duration, and the formation of a tumble flow pattern was important for fuel and air mixing. Finally, in the exhaust valve timing and duration experiment, the exhaust valve closing timing affected the amount of residual gas inside the cylinder. Therefore, the ratio of diffusion flames increased when the exhaust valve timing was advanced or retarded compared to the reference exhaust valve timing.

Controlling the heating wall temperature during melting via electric field

Thermal energy storage technology utilizing phase change materials (PCMs) is extensively applied in thermal management and energy storage. The inclination angle (θ) plays a critical factor in the phase change melting processes in practical implementations. However, the precise experimental velocity fields of liquid PCM and the impact of active regulation technology on the heating wall temperature at various angles remain unclear. Herein, a transparent melting experiment platform was established to measure velocity, and adjust θ and manipulate input electric voltage. Results from Particle Image Velocimetry indicated that the installation angle significantly impacts natural convection in the convection-dominated melting regime. The liquid fraction decreased from 73.5 % (when θ = 90°) to 54.4 % (when θ = 150°) due to reduced thermal energy absorption, as input energy was stored as sensible heat on the copper wall. The electrohydrodynamic (EHD) effect mitigated thermal resistance between the heater and the melt front, increasing melt thickness. EHD flow exhibited limited ability to regulate heating wall temperature under strong natural convection, with a 6.5 % decrease observed under +20 kV conditions. As natural convection rates decreased, the decrease rate of heating wall temperature increased to 10.7 %. The EHD effect demonstrated a heightened capability in enhancing heat transfer melting with increasing θ, leading to an increase in temperature difference from 3.3 °C to 5.3 °C under the +15 kV input voltage. These findings suggest that the electric field is an efficacious method for controlling the heating wall temperature at different inclination angles during the melting process.

Experimental and numerical investigations into cold flow characteristics of multiple micro-mixing jets for hydrogen-rich gas turbines

To explore the flow field of micro-mixing jets, cold flow characteristics of a model Micromix burner were investigated by particle image velocimetry (PIV) system and Large-eddy simulation (LES) model. Results show that LES results are in good agreement with experimental results. In the flow field of multiple micro-mixing jets, the jet velocities of nozzles farther away from the burner center have a high increase and decay rate. When the outlet Reynolds number increases, the Reynolds stress increases first and then decreases in the merging region indicating that the velocity fluctuation disappears in the second jet half, but it has little effect on the flow field structure. Comparing the flow fields of round multiple micro-mixing jets, the merging point and combined point in the elliptical jets flow field move backward. Moreover, the maximum velocity for elliptical jets is also faster than the round jets, which is caused by the high turbulent kinetic energy in the elliptical jet flow field. When the tube spacing increases from 2 to 3 times the tube diameter, positions of the two feature points change linearly. Further, the surrounding jets can decrease the velocity attenuation of the center nozzle and elongate the axial length of the two feature regions.

Thermal convection in subcooled nucleate boiling and microbubble emission boiling: Insights from schlieren and chronophotography techniques

In this study, the bubble-induced thermal convection in nucleate boiling and microbubble emission boiling (MEB) under highly subcooled conditions is investigated through using high-speed schlieren and chronophotography techniques. The spatio-temporal evolution of thermal plumes in nucleate boiling and MEB is revealed based on schlieren technique and power spectral density (PSD) analysis. The results show that bubble condensation in nucleate boiling generates the most intense thermal plumes, particularly at high heat fluxes. Using chronophotography technique, it is found that the thermal convection in MEB, driven by bubble oscillations, exhibits a wave-like pattern over time, while bubble collapses generate densely striated schlieren patterns. PSD analysis highlights significant differences in the decay profiles, cutoff frequencies, and spectral indices of fluctuations of schlieren grayscale between nucleate boiling and MEB. Comparative analysis with previous particle image velocimetry studies suggests that MEB may facilitate efficient heat transport from the heating surfaces to the cold bulk, thereby preserving the subcooling of the liquid near the heating surface even at very high heat flux. These insights deepen our understanding of the fundamental differences in heat transfer mechanisms between nucleate boiling and MEB.

Hydrodynamics of cruise swimming and turning maneuvers in Euchaeta antarctica.

The hydrodynamic disturbance generated by the adult copepod Euchaeta antarctica during cruise swimming is quantified. Kinematic results are compared to previous results reported for different Euchaeta species. The results reveal a linear relationship between cruise speed and prosome length across Euchaeta species, indicating a size-proportional trend that is indicative of a complicated interaction of species size and environmental factors such as fluid temperature and viscosity. The detailed fluid flow measurements using the tomographic Particle Image Velocimetry (tomo-PIV) technique provide insight into copepod cruise propulsion during turning events in comparison to straight motions. During straight swimming, E. antarctica demonstrates streamlined flow patterns and reduced vorticity in the near-body fluid shear layer, which is beneficial for sustained motion and energy conservation. In contrast, turning maneuvers are characterized by maximum flow velocities reaching 1.5 times greater values than during straight cruising with increased flow field complexity and enhanced vorticity. The viscous dissipation rate generated in the flow disturbance is also greater during turning events, with the total dissipation rate reaching W compared to W during straight cruising. The flow disturbance also generates a hydrodynamic cue that prey may sense in order to avoid the predator E. antarctica. For the adult E. antarctica, the hydrodynamic cue extends to a volume that is 11-13 times larger than the copepod exoskeleton volume during the straight swimming motion and 22-25 times larger during the turn events.

Design, manufacturing, and multi-modal imaging of stereolithography 3D printed flexible intracranial aneurysm phantoms.

Physical vascular phantoms are instrumental in studying intracranial aneurysms and testing relevant imaging tools and training systems to provide improved clinical care. Current vascular phantom production methods have major limitations in capturing the biophysical and morphological characteristics of intracranial aneurysms with good fidelity and multi-modal imaging capacity. With stereolithography (SLA) 3D printing technology becoming more accessible, newer flexible and transparent printing materials with higher precision controls open the door for improving the efficiency and quality of producing anthropomorphic vascular phantoms but have rarely been explored for the application. This technical note intends to report the feasibility of using SLA 3D printing technology to manufacture flexible intracranial aneurysm phantoms with similar scales to the real anatomy, as well as their capacity for multi-modal flow imaging and analysis, including ultrasound flow imaging, high-speed filming, and particle image velocimetry analysis. We designed and 3D-printed two intracranial aneurysm phantoms with an SLA 3D printer using Formlabs Elastic 50A resin. By using a micropump to introduce cyclical flows in the phantoms, we first employed conventional Doppler and vector flow ultrasonography to observe and measure different fluidic properties. Then, a high-speed camera was used to record particles flowing within the phantom, and we further conducted a particle image velocimetry analysis, including the distribution of mean 2D velocity vectors, average velocity magnitudes, and the mean vorticity fields in the phantom for the high-speed imaging data. We successfully 3D-printed flexible intracranial aneurysm phantoms with similar dimensions to the real anatomy. Additionally, we validated the phantoms' ability to allow visualization, measurement, and analysis of flow dynamics based on both real-time ultrasound and optical imaging. Our proof-of-concept study illustrates that SLA 3D printing using commercial elastic resins can significantly contribute towards facilitating the fabrication of flexible intracranial aneurysms phantoms for training, research, and preoperative planning.

Viscous and turbulent stress measurements above and below laboratory wind waves

Abstract The influence of wind stress, wind drift, and wind-wave (microscale) breaking on the coupled air–sea boundary layer is poorly understood. We performed high-resolution planar and stereo velocity measurements within the first micrometers to centimeters above and below surface gravity waves at the University of Miami’s SUSTAIN air–sea interaction facility. A particle image velocimetry (PIV) system was adapted and installed in the large (18 m long, 6 m wide) wind-wave tunnel at a fetch of approximately 10 m. In addition, wave field properties were captured by laser-induced fluorescence (LIF). Experiments were conducted with wind waves and wind over mechanically generated swell. In this work, we focus on rather smooth, young, wind-generated waves. We present instantaneous velocity and vorticity fields above and below the air–water interface for the same wind-wave conditions. Both instantaneous and phase-averaged fields show strong along-wave modulations in viscous stress. For steeper waves, we observe airflow separation and increased negative turbulent stress below crests, accompanied by sporadic drops in viscous stress below zero. We describe the wave-induced modulations of the airflow structure as well as the wind-induced water dynamics and discuss the importance of the viscous stress for the total momentum budget. Graphic abstract

Sensitivity of wavelet-based optical flow velocimetry (wOFV) to common experimental error sources

Abstract The influence of several potential error sources and non-ideal experimental effects on the accuracy of a wavelet-based optical flow velocimetry (wOFV) method when applied to tracer particle images is evaluated using data from a series of synthetic flows. Out-of-plane particle displacements, severe image noise, laser sheet thickness reduction, and image intensity non-uniformity are shown to decrease the accuracy of wOFV in a similar manner to correlation-based particle image velocimetry (PIV). For the error sources tested, wOFV displays a similar or slightly increased sensitivity compared to PIV, but the wOFV results are still more accurate than PIV when the magnitude of the non-ideal effects remain within expected experimental bounds. For the majority of test cases, the results are significantly improved by using image pre-processing filters and the magnitude of improvement is consistent between wOFV and PIV. Flow divergence does not appear to have an appreciable effect on the accuracy of wOFV velocity estimation, even though the underlying fluid transport equation on which wOFV is based implicitly assumes that the motion is divergence-free. This is a significant finding for the broader applicability of planar velocimetry measurements using wOFV. Finally, it is noted that the accuracy of wOFV is not reduced notably in regions of the image between tracer particles, as long as the overall seeding density is not too sparse i.e. below 0.02 particles per pixel. This explicitly demonstrates that wOFV (when applied to particle images) yields an accurate whole field measurement, and not only at or adjacent to the discrete particle locations.

Experimental Validation of the Remote Sensing Method for River Velocity Measurement Using an Open-Source PIV Scheme—Case Study: Antisana River in the Ecuadorian Andes

This study presents the experimental validation of a remote sensing method for river flow velocity measurement, from which discharge is calculated, using Particle Image Velocimetry (PIV) combined with Unmanned Aerial Vehicles (UAVs). The case study focuses on the Antisana River in the Ecuadorian Andes, a region with challenging geography where accurate flow measurement is crucial for hydroelectric projects. The validation results demonstrate that the velocity measurements obtained through PIV closely align with those from standardized traditional methods. Furthermore, integrating technologies such as LiDAR for cross-sectional measurements, along with UAVs, would enable the accurate estimation of discharge in difficult-to-access areas. This approach has the potential to significantly enhance hydrological studies and water resource management in remote regions, especially for hydroelectric projects in the Ecuadorian Andes.

Movement Characteristics of Droplet Deposition in Flat Spray Nozzle for Agricultural UAVs

At present, research on aerial spraying operations with UAVs mainly focuses on the deposition outcomes of droplets, with insufficient depth in the exploration of the movement process of droplet deposition. The movement characteristics of droplet deposition as the most fundamental factors affecting the effectiveness of pesticide application by UAVs are of great significance for improving droplet deposition. This study takes flat spray nozzles as the research object, uses the Particle Image Velocimetry (PIV) technique to obtain movement data of water droplet deposition under the influence of rotor flow fields, and investigates the variation characteristics of droplet deposition speed under different influencing factors. The results show that the deposition speed and the distribution area of high-speed (>12 m/s) particles increase with the increase of rotor speed, spraying pressure, and nozzle size. When the rotor speed increases from 0 r/min to 1800 r/min, the average increase in maximum droplet deposition speed for nozzle models LU120-02, LU120-03 and LU120-04 is 33.26%, 19.02%, and 7.62%, respectively. The rotor flow field significantly increases the number of high-speed droplets, making the dispersed droplet velocity distribution more concentrated. When the rotor speed is 0, 1000, 1500, and 1800 r/min, the average decay rates of droplet deposition speed are 36.72%, 20.00%, 15.47%, and 13.21%, respectively, indicating that the rotor flow field helps to reduce the decrease in droplet deposition speed, enabling droplets to deposit on the target area at a higher speed, reducing drift risk and evaporation loss. This study’s results are beneficial for revealing the mechanism of droplet deposition movement in aerial spraying by plant protection UAVs, improving the understanding of droplet movement, and providing data support and guidance for precise spraying operations.

Complete Dynamics From Ignition to Stabilization of a Lean Hydrogen Flame With Thickened Flame Model

Abstract In recent years, attention has been paid to hydrogen thanks to its carbon-free nature and its interesting characteristics as an energy vector. Despite the large number of numerical analyses regarding hydrocarbon combustion in all the steady and unsteady processes, few papers that cover all those aspects are available in the literature for hydrogen flames. Therefore, a numerical methodology to explore the complete ignition sequence from the spark release to the flame stabilization is validated on a single-sector hydrogen burner. In this context, a preliminary direct numerical simulation (DNS) investigation of laminar spherical expanding flames is performed using different diffusive transport models to isolate their impact. The present work, carried out within the European project HESTIA, investigates the atmospheric test rig installed at the Norwegian University of Science and Technology operating with a lean, perfectly premixed, hydrogen–air flame stabilized on a conical bluff body. Four simulations are performed adopting the thickened flame model with an energy deposition (ED) strategy to assess the impact of preferential and thermal diffusion, as well as grid resolution, on flame dynamics. Three-dimensional (3D) flame structure visualization coupled with detailed particle image velocimetry (PIV)/OH-planar laser induced fluorescence (OH-PLIF) measurements allows the investigation of the key mechanisms involved during the ignition. The dynamic response of the flame through axial fluctuations once the ignition transient is concluded is reconstructed by the numerical strategy employed. Although the overall behavior is almost unchanged by including or not thermal diffusion effects, their local impact on the flame is evident leading to a better agreement with experimental data.

Research on Flow Field Prediction in a Multi-Swirl Combustor Using Artificial Neural Network

In aero-engine combustion research, the pursuit of cost-effective and rapid methods for acquiring precise flow fields across various operating conditions remains a significant challenge. This study offers novel insights into the rapid modeling of complex multi-swirling flows, introducing flow-field-based analytical methods to evaluate flow topologies, spray dispersion, ignition dynamics, and flame propagation patterns. A data-driven model is proposed to predict the swirling velocity field inside a multi-swirl combustor, using spatial coordinates and air pressure drops as input features. Particle Image Velocimetry (PIV) experiments under different air pressure drops are performed to generate the necessary flow field dataset. A fully connected deep neural network is designed and optimized with a focus on prediction accuracy, training efficiency, and mitigation of over-fitting. The predicted flow characteristics, including swirling jets, shear layers, recirculation zones, and velocity profiles, align closely with the PIV experimental results. This demonstrates the model’s capability to effectively capture the intricate multi-swirling flow structures and the complex relationships between input parameters and the resulting flow field. Furthermore, the trained model shows excellent generalization capability, accurately predicting flow fields under previously unseen operating conditions. Finally, combustion-relevant characteristics, such as ignition and flame propagation, are successfully extracted and analyzed from the predicted flow fields using the proposed deep learning framework.

Experimental study on the aerodynamic characteristics and wake flow feature of triangular prisms with various round corner radius

In this work, particle image velocimetry (PIV) and wind pressure measurements are employed in the wind tunnel to investigate triangular prisms with different rounded corner radius. The effect of rounded corner radius on wind pressure distribution, aerodynamic force, and wake flow characteristics are analyzed. Results indicate that the surface wind pressure can be effectively reduced through filleting treatment, with an average drag coefficient decrease of 52.7% as the corner shape transitions from sharp to round. The continuous increase of the radius of the rounded corner causes an accretion of the fluctuating lift coefficient, and a slow decrease of the drag coefficient. The modification of the corner radius has a limited impact on the vortex shedding frequency, but the shedding vortex tend to gather the leeward side. Additionally, statistical analysis of wake flow shows that an increment in the rounded corner radius leads to a significant reduction in the wake recirculation region, amounting to 20.16%. Concurrently, the peak values of turbulent kinetic energy and Reynolds stress exhibit a pattern of initial increase followed by a decrease. On the other hand, results of proper orthogonal decomposition suggest that the spatial distribution of vortex structures becomes increasingly compact with the enlargement of the rounded corner radius.

Self-correction of the optical distortion effect of thermal plumes in particle image velocimetry

Optical distortion caused by changes in the refractive index of fluid flow is a common issue in flow visualization using techniques, such as particle image velocimetry (PIV). In thermally driven convection, this distortion can severely interfere with PIV results due to the ubiquitous density and, therefore, refractive index heterogeneity in the fluid. The distortion also varies spatially and temporally, adding to the challenge. We propose a composite filter, the shadow-affected PIV region filter, which combines a series of conventional image filters to address this issue, focusing on optical distortion of thermal plumes in laminar flow. We verify the effectiveness of the filter using both synthetic particle images created from ray tracing and real particle images from the laboratory. For the first time, we effectively mitigate the optical distortion from plumes while preserving the in-plane plume velocity and overall flow pattern, with the PIV data alone. Our filter is efficient and does not require additional measurements, expensive ray tracing, or a large dataset to begin with. It can be extended to separate the flow field and the effect of optical distortion in other fluid experiments when the two components are visually distinct. Additionally, this filter can serve as a baseline algorithm for comparison when developing more advanced methods like neural networks.

A calibration-informed deep learning model for three-dimensional particle reconstruction of volumetric particle image velocimetry

Accurately reconstructing three-dimensional particle fields is essential in fluid velocity measurement research. This study addresses the limitations of current three-dimensional (3D) particle reconstruction methods, such as computational efficiency, precision at high particle density, and particle morphology issues, by introducing a calibration-informed deep learning model named the calibrated pixel to voxel convolutional neural network (CPV-CNN) for 3D Particle Reconstruction. This innovative neural network framework employs a unique Gaussian attention mechanism that bridges pixels and voxels, enabling the mapping of pixel features from two-dimensional (2D) particle images to 3D voxel features. This approach eliminates the need for an initial particle field for particle reconstruction, while significantly enhancing reconstruction efficiency. Additionally, the neural network incorporates camera calibration parameters and the physical coordinates of the reconstructed domain, thereby improving the model's generalization capability and flexibility. Numerical experiments demonstrate that CPV-CNN delivers superior results in terms of accuracy, generalization, and robustness in 3D particle reconstruction. The reconstructed particles exhibit favorable morphology, without the elongation issues commonly observed with conventional methods. This achievement illustrates a practical particle reconstruction algorithm based on artificial intelligence (AI) techniques and represents an important step toward developing an end-to-end AI-based particle reconstruction method in the future.

Design and Optimization of a Magnetic Field Generator for Magnetic Particle Imaging with Soft Magnetic Materials

Design and Optimization of a Magnetic Field Generator for Magnetic Particle Imaging with Soft Magnetic Materials

Theoretical Research on Constructing Gradient Field in Magnetic Particle Imaging System With Superconductor

Theoretical Research on Constructing Gradient Field in Magnetic Particle Imaging System With Superconductor

Image of the Kerr–Newman Black Hole Surrounded by a Thin Accretion Disk

The image of a Kerr–Newman (KN) black hole (BH) surrounded by a thin accretion disk is derived. By employing elliptic integrals and ray-tracing methods, we analyze photon trajectories around the KN BH. At low observation inclination angles, the secondary image of particles is embedded within the primary image. However, as the inclination increases, the primary and secondary images separate, forming a hat-like structure. The spin and charge of the BH, along with the observer’s inclination angle, affect the image’s asymmetry and the distortion of the inner shadow. To investigate the redshift distribution on the accretion disk, we extended the inner boundary of the accretion disk to the event horizon. The results show that the redshift distribution is significantly influenced by the observation inclination angle. Furthermore, we conducted a detailed analysis of the KN BH image using fisheye camera ray-tracing techniques and found that the optical appearance and intensity distribution of the BH vary at different observation frequencies (specifically at 230 GHz and 86 GHz). We also examined differences in intensity distribution for prograde and retrograde accretion disk scenarios. Comparing observational at the two frequencies, we found that both the total intensity and peak intensity at 86 GHz are higher than those at 230 GHz.

A study on the internal vortex structure within a single-blade pump via numerical methods and particle image velocimetry experiments

This study investigates the performance and internal flow characteristics of a single-blade centrifugal pump through a comprehensive analysis integrating performance tests, Particle Image Velocimetry (PIV) tests, and numerical simulations. Across the operational speed range of 1470–2940 rpm, the predicted pump (head-flow rate curve)performance error between the numerical simulation results and the test results of the pump underrated flow conditions is found to be only 3.4%, demonstrating the high accuracy of the selected numerical method in predicting both performance metrics and internal flow structures. Key findings highlight the presence of significant circumferential and axial secondary flows in the region where the blade wraps back, resulting in the formation of high-intensity vortex structures and subsequent energy dissipation. The interaction of the blade's trailing jet with the volute casing's tongue induces periodic changes in the intensity and spatial size of these vortex structures. Furthermore, pronounced reverse vortex pairs are identified within the volute casing, attributed to axial non-uniformity in impeller outflow. These vortex pairs are associated with the preferential accumulation of solid particles, potentially contributing to wear in the volute casing of single-blade centrifugal pumps. The revelation of the evolution characteristics of vortex structure in single-blade centrifugal pumps provides theoretical support for reducing pressure pulsation and radial force of single-blade centrifugal and provides a potential solution to the technical difficulties of low efficiency and poor stability of single-blade centrifugal pumps. Overall, this study provides valuable insights into optimizing the design and operational efficiency of single-blade centrifugal pumps by elucidating their complex flow dynamics and performance characteristics under varying operational conditions.

Correlation of 2D and 3D Particle Properties with Simulated Particle Imaging Dataset

Correlation of 2D and 3D Particle Properties with Simulated Particle Imaging Dataset