Large-scale Particle Research Articles

Experimental investigations on the effects of longitudinal groove fabric for drag reductions on a track cyclist

The previous study has shown that the longitudinal groove fabric can reduce drag forces on a circular cylinder by forcing a drag crisis (Zheng et al. 2021). In this study, the effects of the longitudinal groove fabric are investigated on a full-scale track cycling mannequin. The force measurement results show that the longitudinal groove fabric on the upper arms can achieve a maximum drag reduction of about 7% at a flow speed of 17 m/s, and its control effects depend on flow speeds. Large-scale particle image velocimetry measurements further show that the drag reductions on the upper arm are characterized by diminished streamwise velocity deficits. The control effects also vary on different spanwise locations of the arm, where the flow behaves distinctively. The measurements also reveal the distinct flow dynamics at different heights, i.e., wake interactions and swirling motions, showing the complexity of reducing drag forces from a track cyclist.

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.

A Large-Scale Multiobjective Particle Swarm Optimizer With Enhanced Balance of Convergence and Diversity.

Large-scale multiobjective optimization problems (LSMOPs) continue to be challenging for existing multiobjective evolutionary algorithms (MOEAs). The main difficulties are that: 1) the diversity preservation in both the objective space and the decision space needs to be taken into account when solving LSMOPs and 2) the existing learning structures in current MOEAs usually make the learning operators only coincidentally serve convergence and diversity, leading to difficulties in balancing these two factors. Therefore, balancing convergence and diversity in current MOEAs is difficult. To address these issues, this article proposes a multiobjective particle swarm optimizer with enhanced balance of convergence and diversity (MPSO-EBCD). In MPSO-EBCD, a novel velocity update structure for multiobjective particle swarm optimization is put forward, dividing the convergence, and diversity preservation operations into independent components. Following the proposed update structure, a weighted convergence factor is introduced to serve the convergence strategy, whilst a diversity preservation strategy is built to uniformly distribute the particles in the searched space based on a proposed multidimensional local sparseness degree indicator. By this means, MPSO-EBCD is able to balance convergence and diversity with specific parameters in independent operators. Experimental results on LSMOP benchmarks and a voltage transformer optimization problem demonstrate the competitiveness of the proposed algorithm compared to several state-of-the-art MOEAs.

Design of a high-precision clock distribution and synchronization system

In large-scale nuclear and particle physics experiments, there is a high demand for high precision clock distribution and synchronization. In this paper, the design and testing of a high precision clock distribution and synchronization system for the CSR External Target Experiment (CEE) is presented. To achieve high synchronization precision, a dedicated high-quality clock distribution network is designed, while the timestamp synchronization is achieved over the existing trigger link in CEE with a customized protocol in order to simplify the system structure. Automatic metastability elimination and real-time monitoring of both the timestamps on the front end nodes and the clock electronics status are also implemented. The test results indicate that the period jitter of the clock network is better than 4 ps rms, and all the expected functionalities have been achieved successfully.

Revolutionizing cancer treatment in India: Evaluating the unmet need, economics, and a roadmap for project implementation of particle therapy.

This study aims to quantitatively assess eligible patients and project the demand for particle therapy facilities in India from 2020 to 2040. In addition, an economic analysis evaluates the financial feasibility of implementing this technology. The study also examines the prospective benefits and challenges of adopting this technology in India. Cancer incidence and projected trends were analyzed for pediatric patients using the Global Childhood Cancer microsimulation model and adult patients using the Globocan data. Economic cost evaluation is performed for large-scale combined particle (carbon and proton-three room fixed-beam), large-scale proton (one gantry and two fixed-beam), and small-scale proton (one gantry) facility. By 2040, the estimated number of eligible patients for particle therapy is projected to reach 161,000, including approximately 14,000 pediatric cases. The demand for particle therapy facilities is projected to rise from 81 to 97 in 2020 to 121 to 146 by 2040. The capital expenditure is estimated to be only 3.7 times that of a standard photon linear accelerator over a 30-year period. Notably, the treatment cost can be reduced to USD 400 to 800 per fraction, substantially lower than that in high-income countries (USD 1000 to 3000 per fraction). This study indicates that, in the Indian scenario, all particle therapy models are cost-beneficial and feasible, with large-scale proton therapy being the most suitable. Despite challenges such as limited resources, space, a skilled workforce, referral systems, and patient affordability, it offers substantial benefits. These include the potential to treat many patients and convenient construction and operational costs. An iterative phased implementation strategy can effectively overcome these challenges, paving the way for the successful adoption of particle therapy in India. In India, eligible patients benefiting from high-precision particle therapy technology projected to rise till 2040. Despite high upfront costs, our study finds the long-term feasibility of all particle therapy models, potentially offering a substantial reduction in treatment cost compared to high-income countries. Despite challenges, India can succeed with an iterative phased approach.

Natural surface floaters in image-based river surface velocimetry: Insights from a case study

This study focuses on utilizing image techniques for river velocity measurement, with a specific emphasis on natural surface floating patterns. Employing a multi-camera system, we conducted 3D measurements on river surfaces, including surface velocity and water surface reconstruction. A pattern-based tracking approach has been adopted to improve the performance of image measurements on different types of natural floating tracers. The study employs the following approaches: 3D Lagrangian Pattern Tracking Velocimetry (3D-LPTV), 2D Lagrangian Pattern Velocimetry (2D- LPTV), and Large-scale Particle Image Velocimetry (LSPIV), for surface velocity estimation. The outcomes revealed that all three approaches yielded consistent results in terms of averaged velocity. However, the LSPIV method produced about two times higher uncertainty in measured velocities compared to the other methods. A strategy to assess the quality of river surface patterns in velocity estimation is presented. Specifically, the sum of squared interrogation area intensity gradient (SSIAIG) was found to be strongly correlated with measurement uncertainty. Additionally, a term related to the peak sidelobe ratio (PSR) of the cross-correlation map was found as an effective constraint, ensuring the image-tracking process achieves high reliability. The precision of measurements increases corresponding to the increase of image intensity gradient and PSR.

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.

Quantifying and Reducing the Operator Effect in LSPIV Discharge Measurements

AbstractOperator choices, both in acquiring the video and data and in processing them, can be a prominent source of error in image‐based velocimetry methods applied to river discharge measurements. The Large Scale Particle Image Velocimetry (LSPIV) is known to be sensitive to the parameters and computation choices set by the user, but no systematic comparisons with discharge references or intercomparisons have been conducted yet to evaluate this operator effect in LSPIV. In this paper, an analysis of a video gauging intercomparison, the Video Globe Challenge 2020, is proposed to evaluate such operator effect. The analysis is based on the gauging reports of the 15 to 23 participants using the Fudaa‐LSPIV software and intents to identify the most sensitive parameters for the eight videos. The analysis highlighted the significant impact of the time interval, the grid points and the filters on the LSPIV discharge measurements. These parameters are often inter‐dependent and should be correctly set together to strongly reduce the discharge errors. Based on the results, several automated tools were proposed to reduce the operator effect. These tools consist of several parameter assistants to automatically set the orthorectification resolution, the grid and the time interval, and of a sequence of systematic and automatic filters to ensure reliable velocity measurements used for discharge estimation. The application of the assisted LSPIV workflow using the proposed tools leads to significant improvements of the discharge measurements with strong reductions of the inter‐participant variability. On the eight videos, the mean interquartile range of the discharge errors is reduced from 17% to 5% and the mean discharge bias is reduced from −9% to 1% with the assisted LSPIV workflow. The remaining inter‐participant variability is mainly due to the user‐defined surface velocity coefficient α.

On the performance of streamflow gauging using CCTV-integrated LSPIV in diverse hydro-environmental conditions.

Addressing the critical need for precise streamflow measurements in hydro-environmental research, this study evaluates large-scale particle image velocimetry (LSPIV) using cost-effective closed-circuit television (CCTV) cameras, providing a detailed sensitivity analysis in both laboratory and real-world canal settings. In laboratory conditions, a 45° camera angle notably enhanced performance, exhibiting a 12% decrease in MAE and a remarkable 40% reduction in RMSE compared to the performance of orthographic form. Tracer particles further enhanced LSPIV accuracy, decreasing both mean absolute error (MAE) and root mean square error (RMSE) by around 0.05 m/s. Optimal velocity coefficients for the lab ranged between 0.85 and 0.90. Nighttime measurements, using projection-based illumination, showed a minor 3% MAE variation and 0.02 RMSE difference versus daytime. In field experiments, a 45° upstream CCTV camera configuration notably improved LSPIV accuracy, achieving a 3% MAE and 0.055 m/s RMSE. For best results across different turbidity levels, we recommend a velocity coefficient range of 0.84 to 0.88. This study highlights the robustness and cost-efficiency of LSPIV as a transformative method for streamflow gauging, demonstrating its wide applicability in diverse hydro-environmental scenarios.

An integrated electronic tag-based vertical flow assay (e-VFA) with micro-sieve and AlGaN/GaN HEMT sensors for multi-target detection in actual saliva.

Vertical flow assay (VFA) is an effective point-of-care (POC) diagnostic tool for widespread application. Nevertheless, the lack of multi-target detection and multi-signal readout capability still remains a challenge. Herein, a brand new VFA scheme for multi-target saliva detection based on electronic tags was proposed, where AlGaN/GaN HEMT sensors modified with different bio-receptors as electronic tags endowed the VFA with multi-target detection capability. In addition, the use of electronic tags instead of optical tags allowed the VFA to simultaneously carry out direct multi-target readouts, which ensure effective POC diagnostics for saliva analysis. Moreover, by integrating a hydrophilically optimized micro-sieve, impurities like sticky filaments, epidermal cells and other large-scale charged particles in saliva were effectively screened, which enabled the direct detection of saliva using AlGaN/GaN HEMT sensors. Glucose, urea, and cortisol were selected to verify the feasibility of the multi-target e-VFA scheme, and the results showed that the limit of detection (LOD) was as low as 100 aM. The linear response was demonstrated in the dynamic range of 100 aM to 100 μM, and the specificity, long-term stability and validity of the actual saliva test were also verified. These results demonstrated that the as-proposed e-VFA has potential for application in saliva detection for simultaneous multi-target detection, and it is expected to achieve the real-time detection of more biological targets in saliva.

Satellite Video Remote Sensing for Flood Model Validation

AbstractSatellite‐based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video‐derived flood extents and velocities. Hydraulic simulations of a flood event with a 5‐year return period (discharge of 722 m3 s−1) were conducted using Hydrologic Engineering Center—River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer‐encoder, convolutional neural network (CNN)‐decoder deep neural network. We evaluate the influence of test‐time augmentation (TTA)—the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non‐contact‐based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network‐based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space‐based video sensors for validating flood models and studying flood dynamics.

Progressive Tree-Based Compression of Large-Scale Particle Data.

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. The improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

Measuring Velocity and Discharge of High Turbidity Rivers Using an Improved Near-Field Remote-Sensing Measurement System

Large-scale particle image velocimetry (LSPIV) is a computer vision-based technique renowned for its precise and efficient measurement of river surface velocity. However, a crucial prerequisite for utilizing LSPIV involves camera calibration. Conventional techniques rely on ground control points, thus restricting their scope of application. This study introduced a near-field remote-sensing measurement system based on LSPIV, capable of accurately measuring river surface velocity sans reliance on ground control points. The system acquires gravity-acceleration data using a triaxial accelerometer and converts this data into a camera pose, thereby facilitating swift camera calibration. This study validates the system through method verification and field measurements. The method verification results indicate that the system’s method for retroactively deriving ground control-point coordinates achieves an accuracy exceeding 90%. Then, field measurements were performed five times to assess the surface velocity of the Datong River. These measured results were analyzed and compared with data collected from the radar wave velocity meter (RWCM) and the LS1206B velocity meter. Finally, a comprehensive sensitivity analysis of each parameter was conducted to identify those significantly impacting the river’s surface velocity. The findings revealed that this system achieved an accuracy exceeding 92% for all river surface velocities measured.

Satellite Video Remote Sensing for Estimation of River Discharge

AbstractWe demonstrate that river discharge can be estimated by deriving water surface velocity estimates from satellite‐derived video imagery when combined with high‐resolution topography of channel geometry. Large Scale Particle Image Velocimetry (LSPIV) was used to map surface velocity from 28 s of 5 Hz satellite video acquired at a 1.2 m nominal ground spacing over the Darling River, Tilpa, Australia, during a 1‐in‐5‐year flood. We stabilized and assessed the uncertainty of the residual motion induced by the satellite platform, enhancing our sub‐pixel motion analysis, and quantified the sensitivity of image extraction rates on computed velocities. In the absence of in situ observations, LSPIV velocity estimates were validated against predictions from a calibrated 2D hydrodynamic model. Despite the confounding influence of selecting a surface velocity depth‐averaging coefficient, inference of discharge was within 0.3%–15% compared with gauging station measurements. These results provide a valuable foundation for refining satellite video LSPIV techniques.

Particle dynamics in vertical vibration-driven immersed granular systems: A study with resolved computational fluid dynamics-discrete element method

Vibration-driven immersed granular systems (VIGSs) are ubiquitous in nature and industry. However, particle dynamics in 3D VIGSs is hard to obtain directly from experiments. The resolved Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) is introduced to study a cylindrical VIGS subjected to vertical vibration focusing on particle dynamics. A Voronoi-weighted Gaussian interpolation (VWGI) method is used to convert the discrete particle information into a continuous field. The VWGI method enables the estimation of the continuous field for granular systems, especially for those with large-scale non-uniformity and heterogeneity particle distribution in local cells. The results show that the periodic variation of the system's kinetic energy is caused by the collision between the lower particles and the vibrating wall, and the particle kinetic energy decreases with height rising. A velocity spatial structure of convection, moving from the cylinder center to the sidewall, is observed in both immersed and dry systems away from the bottom. Vibration-driven particles can exhibit a similar flow structure to natural convection. Compared to the dry system, the convection strength and momentum transfer in the VIGS are higher, while the momentum diffusion is lower. The fluid restrains the particle energy acquisition and enhances the energy dissipation of the “heated” particles, while the formation of the fluid convection benefits the particle convection directionality. This resolved CFD-DEM study with the VWGI method provides useful results of the particle dynamics in VIGSs, which could provide guidance for some practical applications in minerals processing involving vibration-driven immersed granular systems.

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R2 = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface.

Four-dimensional particle tracking velocimetry measurements of unsteady three-dimensional vortex onset and progression for 5415 straight ahead, static drift, and pure sway

Large-scale towed system four-dimensional particle tracking velocimetry (4DPTV) implementation and measurements are presented for towing tank tests for a naval combatant 5415 ship model for straight ahead, static drift β = 10°, and pure sway βmax = 10° conditions. The results, for the first time, provide instantaneous volumetric flow field data around the ship model including near the hull surface, and a complete description of the sonar dome 3D unsteady vortex separation onset and progression due to the 4DPTV larger measurement volume and higher data rates in comparison with previous results using tomographic particle image velocimetry (TPIV); thereby, providing the essential data required for the assessment of current hybrid Reynolds-averaged Navier–Stokes/large eddy simulation turbulence modeling capabilities and guidance for the necessary modeling improvements. The 4DPTV system is summarized and compared with the previous TPIV, including the camera calibration procedures, trigger systems, and synchronization with the sway motion. Analysis procedures, including data reduction procedures and the 3D vortex core detection and voxel labeling techniques, are described. The identification of the complex vortex separations and breakdown is aided using complementary detached eddy simulations. The vortex–vortex interaction process is visualized from instantaneous volumetric datasets. The pros and cons of the 4DPTV vs TPIV, the comparison between the second- and third-generation vortex visualization technique, and the statistical convergence error analysis of sonar dome port vortex for β = 10° are discussed. Plans for increased spatial resolution of the 4DPTV system and additional data reduction techniques for detailed turbulence analysis are also discussed.

The regularised inertial Dean–Kawasaki equation: discontinuous Galerkin approximation and modelling for low-density regime

The Regularised Inertial Dean–Kawasaki model (RIDK) – introduced by the authors and J. Zimmer in earlier works – is a nonlinear stochastic PDE capturing fluctuations around the meanfield limit for large-scale particle systems in both particle density and momentum density. We focus on the following two aspects. Firstly, we set up a Discontinuous Galerkin (DG) discretisation scheme for the RIDK model: we provide suitable definitions of numerical fluxes at the interface of the mesh elements which are consistent with the wave-type nature of the RIDK model and grant stability of the simulations, and we quantify the rate of convergence in mean square to the continuous RIDK model. Secondly, we introduce modifications of the RIDK model in order to preserve positivity of the density (such a feature only holds in a “high-probability sense” for the original RIDK model). By means of numerical simulations, we show that the modifications lead to physically realistic and positive density profiles. In one case, subject to additional regularity constraints, we also prove positivity. Finally, we present an application of our methodology to a system of diffusing and reacting particles. Our Python code is available in open-source format.

Large-scale volumetric particle tracking using a single camera: analysis of the scalability and accuracy of glare-point particle tracking

Large-scale volumetric particle tracking using a single camera: analysis of the scalability and accuracy of glare-point particle tracking

Evidence that superstructures comprise self-similar coherent motions in high Reynolds number boundary layers

We present experimental evidence that the superstructures in turbulent boundary layers comprise smaller, geometrically self-similar coherent motions. The evidence comes from identifying and analysing instantaneous superstructures from large-scale particle image velocimetry datasets acquired at high Reynolds numbers, capable of capturing streamwise elongated motions extending up to 12 times the boundary layer thickness. Given the challenge in identifying the constituent motions of the superstructures based on streamwise velocity signatures, a new approach is adopted that analyses the wall-normal velocity fluctuations within these very long motions, which reveals the constituent motions unambiguously. The conditional streamwise energy spectra of the Reynolds shear stress and the wall-normal fluctuations, corresponding exclusively to the superstructure region, are found to exhibit the well-known distance-from-the-wall scaling in the intermediate-scale range. It suggests that geometrically self-similar motions are the constituent motions of these very-large-scale structures. Investigation of the spatial organization of the wall-normal momentum-carrying eddies, within the superstructures, also lends empirical support to the concatenation hypothesis for the formation of these structures. The association between the superstructures and self-similar motions is reaffirmed on comparing the vertical coherence of the Reynolds-shear-stress-carrying motions, by computing conditionally averaged two-point correlations, which are found to match with the mean correlations. The mean vertical coherence of these motions, investigated for the log region across three decades of Reynolds numbers, exhibits a unique distance-from-the-wall scaling invariant with Reynolds number. The findings support modelling of these dynamically significant motions via data-driven coherent structure-based models.