Large-scale Particle Research Articles

Widefield SERS for High-Throughput Nanoparticle Screening.

Surface-enhanced Raman scattering (SERS) imaging is a powerful technology with unprecedent potential for ultrasensitive chemical analysis. Point-by-point scanning and often excessively long spectral acquisition-times hamper the broad exploitation of the full analytical potential of SERS. Here, we introduce large-scale SERS particle screening (LSSPS), a multiplexed widefield screening approach to particle characterization, which is 500-1000 times faster than typical confocal Raman implementations. Beyond its higher throughput, LSSPS simultaneously quantifies both the sample's Raman and Rayleigh scattering to directly quantify the fraction of SERS-active particles which allows for an unprecedented correlation of SERS activity with particle size. .

Mixing and segregation of spheres of three different sizes on a batch stoker grate: Experiments and discrete element simulation

Mixing and segregation in a tri-disperse granular assembly of polyoxymethylene (POM) spheres induced by the moveable stoking bars of a generic batch grate system are examined. Each particle size class features a separate colour. Stroke bar velocity and stroke length are varied. Different moving modes of the bars are analysed. Optically transparent walls of the grate allow for the localization of the visible particles. Based on the visible particle positions a segregation index is calculated. The initial arrangement of the particles in the experiments, which exhibits small statistical differences introduced by the grate filling procedure, has an influence on the progression of the segregation index. The experiments are compared with discrete element (DEM) simulations employing an in-house DEM code.Experiments are in good general agreement with the simulations. The particle rearrangement during bar movement is characterized by an initially fast mixing on short time-scales and a slow process to reach a final state of segregation. These two processes are influenced by the penetration depth of the bars into the bed and the specific movement mode.Three modes predominantly showed segregation in the direction of bar movement, whereas two modes showed large-scale spatial particle rearrangement. Two moving modes show bridging at the beginning of the experiments, an effect that could be reproduced by the DEM simulations. The influence of the modes and their specific parameters on segregation indices, a mixing rate and a segregation efficiency are discussed in detail.

An Investigation of the Optimum Solar Flux Distribution on a Large-Scale Particle Heating Receiver

Solid particles have been shown to be an effective heat transmission as well as thermal storage medium for falling particle receiver based solar power systems at temperatures up to 1,000°C. The temperature distribution on the surface of the falling particle receiver is critical. High temperatures, thermal shocks, and temperature gradients produce substantial stresses on the receiver due to high, fluctuating, and non-homogeneous solar flux. To this effect, the optimum control of the heliostats’ aiming points is one of the obstacles that must be overcome. The flux distribution on the receiver surface must be carefully managed to avoid dangerous flux peaks or excessive temperature gradients which might result in local hot spots resulting in damage of the receiver’s internal components over time. To overcome this problem, specifying multiple aiming points on the receiver aperture may control the solar flux distribution. In this study both single and multi aiming points strategies are applied by assigning a group of heliostats to a specific aim point on the receiver, resulting in a uniform flux distribution over the receiver surface. Engineering software packages SolarPILOT, SOLTRACE and MATLAB are used in combination to get the optimal flux distribution. The results showed that the flux distribution is improved significantly after employing the multi aiming points strategy at the expense of greater spillage.

Effects of the path history on inertial particle pair dynamics in the dissipation range of homogeneous isotropic turbulence

The relationship between inertial particle pair dynamics in the dissipation range and local turbulence characteristics is investigated using direct numerical simulations in homogeneous isotropic turbulence at a Reynolds number of 110 based on the Taylor microscale. Seventeen million sub-Kolmogorov-sized particles with Stokes numbers of 0.5, 1.0, 1.5, and 2.0 are tracked backward in time to investigate the formation of the relative velocity of inertial particles in the dissipation range. The numerical experiment shows that particle pairs take different paths and sample different underlying flows depending on the intensity of the local turbulence activity at final locations where the distance between particles is much smaller than the Kolmogorov length scale. Taking different paths depending on the intensity of the local turbulence activity is described by the sling time, which is defined as the time when particle pairs are slung out from the underlying flow. Particle pairs with shorter sling time values than the characteristic time scale of particles are detached from the dissipation range of flow and approach each other in a ballistic fashion. In contrast, the large-scale flow guides particle pairs with longer sling time than the characteristic time scales. In particular, particle pairs forming caustics with the same sling time are detached from similar flows regardless of the magnitude of particle relative velocities. Samplings of similar flows at sling moments cause the scaling relationship between relative velocities of particles and sling time. These behaviors of particle pairs, especially for pairs with caustics in relative velocities, are also observed in the range of considered Stokes numbers. The relationship between the intensity of the local turbulence activity and the paths taken by particles highlights the importance of considering a preferential sampling of flow in understanding inertial particle pair dynamics.

On the existence and formation of multi-scale particle streaks in turbulent channel flows

Direct numerical simulations of particle-laden turbulent channel flows at friction Reynolds number $Re_\tau$ from $600$ to $2000$ have been performed to examine the near-wall particle streaks. Different from the well-observed small-scale particle streaks in near-wall turbulence of low $Re_\tau$ , the present results show large-scale particle streaks through the computational domain formed for relatively high-inertia particles at high $Re_\tau$ . Transferred by large-scale sweep and ejection events ( $Q^-$ ), these high-inertia particles preferentially accumulate in near-wall regions beneath the large-scale low-speed flow streaks observed in the logarithmic region. The corresponding Stokes numbers are associated with the lifetime of large-scale $Q^-$ structures, which increases as the Reynolds number grows. The small-scale particle streaks with a typical Stokes number $St_\nu \approx 30$ are mainly driven by the $Q^-$ structures in the buffer layer, whose lifetime is approximately $30$ in viscous time unit. Therefore, we propose a new structure-based Stokes number normalized by the lifetime of $Q^-$ structures of different scales. The relevant flow scales that control the formation of the large-scale particle streaks are parameterized by the structure-based Stokes number. The small-scale (large-scale) particle streaks are most prominent when the buffer-layer (large-scale) structure-based Stokes number approaches unity. The present findings reveal that formation of near-wall particle streaks is governed by the $Q^-$ structures of different scales, and the particles with different inertia respond efficiently to the $Q^-$ structures of corresponding scales with respect to the particle translational motion.

Spectral characterisation of inertial particle clustering in turbulence

Clustering of inertial particles is important for many types of astrophysical and geophysical turbulence, but it has been studied predominately for incompressible flows. Here, we study compressible flows and compare clustering in both compressively (irrotationally) and vortically (solenoidally) forced turbulence. Vortically and compressively forced flows are driven stochastically either by solenoidal waves or by circular expansion waves, respectively. For compressively forced flows, the power spectrum of the density of inertial particles is a useful tool for displaying particle clustering relative to the fluid density enhancement. Power spectra are shown to be particularly sensitive for studying large-scale particle clustering, while conventional tools such as radial distribution functions are more suitable for studying small-scale clustering. Our primary finding is that particle clustering through shock interaction is particularly prominent in turbulence driven by spherical expansion waves. It manifests itself through a double-peaked distribution of spectral power as a function of Stokes number. The two peaks are associated with two distinct clustering mechanisms; shock interaction for smaller Stokes numbers and the centrifugal sling effect for larger values. The clustering of inertial particles is associated with the formation of caustics. Such caustics can only be captured in the Lagrangian description, which allows us to assess the relative importance of caustics in vortically and compressively forced turbulence. We show that the statistical noise resulting from the limited number of particles in the Lagrangian description can be removed from the particle power spectra, allowing us a more detailed comparison of the residual spectra. We focus on the Epstein drag law relevant for rarefied gases, but show that our findings apply also to the usual Stokes drag.

Local flow convergence, bed scour, and aquatic habitat formation during floods around wooden training structures placed on sand-gravel bars

Riverine ponds, which are formed and sustained through sediment erosion and deposition, are key habitats for enhancing biodiversity in river reaches. The objective of this study was to understand the roles of traditional river-training wooden structures called “seigyu” on the formation of ponds on nonvegetated bars. Here, the spatial and temporal patterns of the flow and bedform coupled with pond formations for several flood events were assessed. The surface flow patterns were monitored by an unmanned aerial vehicle (UAV) and evaluated by large-scale particle image velocimetry (LSPIV); the maximum flow velocities were 1.3 m s−1 and 1.9 m s−1 during floods when seigyu units were partially and fully submerged, respectively. Although the overall mean flow velocity was greater for the latter events, the spatial variation in flow velocity and dissipation rate of turbulent kinetic energy (TKE) around seigyu was greater for the former events. Such flow patterns affected both bed formation and ecological habitats; ponds were formed at locations beside and behind seigyu, where the flow converged and bed scouring occurred during floods. The frequency and size of ponds around seigyu increased in the early half of the season, and they decreased in the other half when floods were greater in magnitude, which suggests that the bed scouring effect of seigyu was greater in flood stages with partial than fully submergence. Although the bar ponds lack shade to temper the effects of incident light and atmospheric conditions, the ponds displayed smaller daily oscillations in temperature than did the main river, probably due to hyporheic water supply to the ponds. Because many aquatic species cannot tolerate extremely high temperatures in summer, the generation and maintenance of deeper ponds by scouring with sufficient water exchange with the hyporheic zone can be key to enhancing colonization by various aquatic species.

A high precision phase measurement system implemented in FPGA with phase interpolator.

High precision timing distribution is crucial to many large scale cosmology and particle physics experiments. Besides the space and energy information, the accurate timing provides an extra dimension for physics event reconstruction. In the timing distribution system, accurate clock phase measurement is an indispensable tool to monitor the phase drift and to achieve accurate phase adjustment. This paper introduces a novel phase measurement method implemented in the Xilinx Field Programmable Gate Array (FPGA). It uses the dedicated phase interpolator in the multi-gigabit transceiver. A design based on this method is implemented within the Kintex Ultrascale series FPGA. The preliminary test result shows that a sub-picosecond level precision is achieved. With this system, the nonlinearity of the phase adjustment in the Xilinx transceiver is measured.

Strengthen resilience to rivers flooding by the drag reduction technique

Urban agglomerations face the risk of overflowing rivers due to intense urbanization in flood-prone areas and the climate change effects. Despite the important protective measures deployed to reduce the fluvial flooding risk, additional efforts are still needed. This work aims to propose a new complementary non-structural protection measure, used to reduce the river flooding risk. The study is part of the NABRAPOL (NEBRASKA POLYMER) project, which aims to improve knowledge of the drag reduction effect by adding polymers in open-channel flows. The addition of polymers, even in limited concentrations, allows high friction to decrease with the typical Manning coefficient reduced up to 45%. An application case on a real watercourse is presented in this article. Two measurement campaigns are carried out on a river along 30 km. Experimental devices are deployed, and non-intrusive hydraulic measuring instruments are installed at the study field. Surface velocities are evaluated by the Large-Scale Particle Image Velocimetry (LSPIV) technique, and water depth is measured using ultrasonic radar sensors over the river. Measurement results show that the addition of 20 ppm of polymers in the flows results in a marked drag reduction by decreasing the water depth to 18% of its initial depth. The drag reduction technique by addition of small concentrations of polymers can be considered as a new and effective method to reinforce the measures already deployed in the flood risk management strategy since it allows the water depth to be decreased thus avoid overflowing rivers in the extreme flooding event.

Numerical Simulation and Analysis of Large-scale Particle Fluid System Based on CFD-DPM Method

Numerical Simulation and Analysis of Large-scale Particle Fluid System Based on CFD-DPM Method

비압축성 입자법 내 압력해법에 관한 GPU 가속화에 대한 연구

In the Moving Particle Simulation (MPS) method, which is a gridless particle method with simple algorithm for solving Navier-Stokes equation, insufficient number of particles in the vicinity of boundary has a considerable influence on the numerical calculation, which causes directly the flow field to become unstable and inaccurate. In particular, the Poisson Equation for pressure is solved iteratively at every time step to obtain an approximate solution. This gratly incereases the amount of computation when performing large-scale particle simulations, which requires high-performance computing. The main objective of this study is to develop a GPU(Graphics Processing Unit)-accelerated MPS code using CUDA(Compute Unified Device Architecture) language to solve the Poisson equation. Furthermore in order to compare the performance of the computational speed, a 3-dimensional calculation of broken-dam break problem has been carried out using the GPU-accelerated MPS method. It can be seen that the performance of the speed ratio in the GPU-based code is improved by about 10 times compared to that of the CPU-based code.

Spatial Breach Analysis due to Overtopping Flow Depending on the Degree of Compaction for Noncohesive Embankments

This study aimed to an experiment to analyze the spatial levee breach mechanisms due to overtopping according to the degree of compaction. A levee experiment was performed using four different degree of compaction implemented by adjusting the levee compaction thickness and number of compactions, and the effects of the degree of compaction on levee breaching were observed. Under the low degree of compaction, the scale of the levee breach over time was relatively large, and the breach discharge was also significant. This study also observed the change in velocity of the levee crest area through Large Scale Particle Image Velocimetry (LSPIV) analysis of the velocity field at each levee breach stage; in particular, the location where the maximum velocity occurred in the crest area differed according to the degree of compaction. The relationship between the degree of compaction and peak breach discharge shown in experimental results suggests the degree of compaction can directly affect the breach of the levee and the flooding velocity at the low-land area. The clear correlations between the degree of compaction and breach discharge and breach size over time presented in this study can provide detailed information for the design standards and maintenance of the levee.

A data-driven deep learning model of radiative heat transfer in dense granular systems

Radiative heat transfer has attracted increasing research interest in various dense granular systems, including large-scale nuclear pebble-bed reactors, fusion breeder blankets, packed beds, metal powder beds and granular lunar regolith simulants. The optimised ray tracing method (ORTM) with a Sobol quasi-random sequence is proposed to calculate the obstructed view factor between spheres, and it converges much faster than that with the traditional pseudorandom number sampling method. Based on discrete particle packing of nuclear pebble bed HTR-PM and random particle generation, a large obstructed view factor dataset of thermal radiation is generated by parallel CUDA computing for training a deep learning regression model to discuss the packing-property linkage for radiative heat transfer in the large-scale particle bed. Compared with other approaches, the deep residual neural network (ResNet) with the swish activation function applied in this paper is found to be the best neural network architecture for view factor prediction without vanishing gradients or model degradation problems during training. The testing error of the trained deep learning model is <1.0 × 10-4 for over 99.96% view-factor cases in nuclear pebble beds, and it has proven to be 7.3 × 105 times faster than the traditional numerical method in view-factor calculations. This makes it feasible to explore the local and macroscopic thermal radiation behaviours in large-scale pebble beds filled with up to 1.0 × 107 spheres.

Predictive modeling and analysis of runout distance of physical mudflows based on a discrete element method

ABSTRACT Both visualization simulation and numerical simulation play important roles in mudflow simulation. However, the computational bottlenecks and the correctness of mudflow simulation supporting a large-scale particle system are still key problems to be solved in modeling of mudflow dynamics. In this article, we propose a discrete element method that uses many-core graphics processing units to accelerate the simulation of such a depositional process and to predict runout distances for physical mudflows. A model constructed using this method was verified for accuracy and applicability by a comparative analysis of physical mudflow flume tests and corresponding simulation tests. Our results showed that the runout distance prediction model, constructed using the discrete element method, can simulate the depositional processes of physical mudflows (supporting the simulation of tens of millions of particles), and can simulate their runout distance and deposit morphologies. Because the heterogeneous CPU–GPU computing approach can effectively improve the simulation accuracy by using more mudflow particles, the error in the simulation of the runout distance of the physical mudflows was within a small and acceptable range. Thus, the model can be used for further studies on the prediction of risks associated with mudflows.

A particle swarm optimization approach for large-scale many-objective software architecture recovery

The software systems with a well-documented architecture are easy to understand and evolve. However, in most cases, either the documented architecture is unavailable or get eroded badly. In these cases, to understand and evolve the software systems, developers often need to recover the architectural components from the system implementation code. Recently, a variety of heuristic-based multi-objective optimization algorithms (MoOAs) for software architecture recovery (SAR) have been introduced. Most of the existing SAR approaches are designed by adopting the traditional MoOAs. However, the performance of such approaches degrades drastically with large-scale many-objective SAR (LSMaO-SAR). To address the challenges the MoOAs caused by the LSMaO-SAR, we introduce a large-scale many-objective particle swarm optimization (LSM-PSO) by customizing the framework of the PSO algorithm. For this, we adopt various strategies such as Balance Fitness Evaluation (BFE), Quality Indicator (QI) based fitness evaluation, Fuzzy-Pareto dominance (FPD), and Two-archive external storage, and incorporate into the PSO model. To test the effectiveness of the LSM-PSO, it is applied over five software projects and compared with the existing SAR approaches. The results show that the proposed LSM-PSO outperforms the existing optimization-based SAR approaches.

Investigation of turbulent boundary layer flows with adverse pressure gradient by means of 3D Lagrangian particle tracking with Shake-The-Box

A large-scale 3D Lagrangian particle tracking (LPT) investigation of a turbulent boundary layer (TBL) flow developing across different pressure gradient regions is presented in this study. Three high-speed multi-camera imaging systems, LED illumination and helium-filled soap bubbles (HFSB) tracers have been adopted to produce time-resolved sequences of particle images over a large volume encompassing approximately 3 m in the streamwise direction, 0:8 m in the spanwise direction and 0:25 m in the wall-normal direction. Individual tracers have been reconstructed and tracked within the imaged volume by means of the Shake-The-Box algorithm (STB, Schanz et al. (2016)); the FlowFit data assimilation algorithm (Gesemann et al. (2016)) has been used to evaluate the spatial velocity gradients and to interpolate the scattered LPT results onto a regular grid. Thanks to the large size of the investigated volume and to the time-resolved nature of the recorded images, the entire spatial extent of the large-scale coherent motions within the logarithmic region of the TBL (i.e. superstructures) could be captured and their dynamics investigated during their development over several boundary layer thickness in the streamwise direction, from the zero pressure gradient region (ZPG) to the adverse pressure gradient region (APG). Two free-stream velocities were investigated, namely 7 and 14m=s, corresponding to Ret ~ 3,000 and 5,000 respectively. The results confirm the location and scale of the elongated high- and low-momentum structures in the logarithmic region, as well as their meandering in the spanwise direction. Two-point correlation statistics show that the width and spacing of the superstructures are not affected by the transition from the ZPG to the APG region. The analysis of the instantaneous flow realizations from both a Lagrangian and Eulerian perspective indicates the presence of significant fluid particle elements exchange across the interfaces of the large-scale structures.

Large-scale 3D flow investigations around a cyclically breathing thermal manikin in a 12 m³ room using HFSB and STB

Exhalation of small aerosol droplets and their transport, dispersion and (local) accumulation in closed rooms have been identified as the main pathway for indirect or airborne respiratory virus transmission from person to person, e.g. for SARS-CoV 2 or measles (Morawska and Cao 2020). Understanding airborne transport mechanisms of viruses via small bio-aerosol particles inside closed populated rooms is an important key factor for optimizing various mitigation strategies (Morawska et al. 2020), which can play an important role for damping the infection dynamics of any future and the ongoing present pandemic scenario, which unfortunately, is still threatening due to the spreading of several SARS-CoV2 variants of concern, e.g. delta (Kupferschmidt and Wadman 2021). Therefore, a large-scale 3D Lagrangian Particle Tracking experiment using up to 3 million long lived and nearly neutrally buoyant helium-filled soap bubbles (HFSB) with a mean diameter of ~ 370 µm as passive tracers in a 12 m³ generic test room has been performed, which allows to fully resolve the Lagrangian transport properties and flow field inside the whole room around a cyclically breathing thermal manikin (Lange et al. 2012) with and without mouth-nose-masks and shields applied. Six high-resolution CMOS streaming cameras, a large array of powerful pulsed LEDs have been used and the Shake-The-Box (STB) (Schanz et al. 2016) Lagrangian particle tracking algorithm has been applied in this experimental study of internal flows in order to gain insight into the complex transient and turbulent aerosol particle transport and dispersion processes around seated breathing persons.

Large Scale Infrared-Based Remote Sensing of Turbulence Metrics in Surface Waters: Going Beyond Mean Flow

In recent years field-scale applications of image-based velocimetry methods, often referred to as large scale particle image velocimetry (LSPIV), have been increasingly deployed. These velocimetry measurements have several advantages—they allow high resolution, non-contact measurement of surface velocity over a large two dimensional area, from which the bulk flow can be inferred. However, visiblelight LSPIV methods can have significant limitations. The water surface often lacks natural features that can be tracked in the visible and generally requires seeding with tracer particles, which creates concerns regarding the fidelity with which tracer particles track the flow, and introduces challenges in achieving sufficient and uniform seeding density, in particular in regions with appreciable velocity accelerations such as turbulence. In LSPIV, image collection is generally limited to daylight hours, and can suffer from non-uniformity of illumination across the camera’s field of view. Due to these issues LSPIV often requires spatio-temporal averaging, and as a result is generally able to extracting the mean, but not the instantaneous, velocity field, and hence is often not a suitable tool for calculating turbulence metrics of the flow.

Characterization and source analysis of water-soluble inorganic ionic species in PM2.5 during a wintertime particle pollution episode in Nanjing, China

Water-soluble inorganic ions (WSIIs) are major components of PM2.5, and play a prominent role in atmospheric acidification. In January 2019, a large-scale persistent particle pollution episode occurred in the Yangtze River Delta (YRD) region of China. High time resolution on-line monitoring of WSIIs in PM2.5 were conducted at the Atmospheric Environment Vertical Observation Station of Nanjing University by using In-situ Gas and Aerosol Compositions Monitor. From January 1 to 13, the mean concentration of WSIIs was 76.7 ± 48.2 μg·m−3. WSIIs accounted for 64.2% of PM2.5 concentration. Sulfate, nitrate and ammonium (SNA) were the major components of WSIIs, and accounted for 25.5%, 45.6% and 26.3%, respectively. The values of NO3−/SO42− increased from 1.37 to 2.86, indicating that the contribution of mobile sources to fine particle pollution was significantly enhanced than that of stationary sources. The main factors affecting the values of sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) were gaseous precursor concentrations, relative humidity and air temperature. The values of SOR and NOR exceeded 0.1, implying the secondary transformation processes played critical roles during this pollution episode. To investigate the potential source areas of WSIIs in PM2.5, the Principal Component Analysis - Multiple Linear Regression (PCA-MLR) analysis and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were used. By using PCA-MLR, it was found that the main pollution sources in Nanjing were secondary transformation (58%), manmade dust (26%) and biomass combustion (16%). Based on the backward trajectory calculation, the potential source contribution function (PSCF) analysis and the concentration weighted trajectory (CWT) analysis, it was revealed that the air pollutants mainly came from the local sources in and around Nanjing. With the aggravation of pollution, the contribution of regional transport to PM2.5 concentrations decreased and the contribution of local emissions increased. Among the main components of WSIIs in Nanjing, NH4+ was mainly caused by agricultural activities, and generally came from Jiangsu, eastern Henan, southern Shandong, and northeastern Anhui. SO42− was mainly derived from coal burning in the nearby industrial areas or from the north through the long-distance transmission. NO3− mainly concentrated in the local areas, which was greatly affected by human activities. The weighted concentration weighted trajectory (WCWT) peak values of NH4+ and SO42− were about 10 μg·m−3, while that of NO3− was more than 20 μg·m−3, which further indicated that the local emission of NO3− was the main potential source. The findings can contribute to understand the behavior of WSIIs and their roles in the wintertime haze event, reveal potential source areas for the corresponding components, and enlighten beneficial suggestions for particle pollution prevention and control in high polluted areas.

Large-Scale Particle Image Velocimetry to Measure Streamflow from Videos Recorded from Unmanned Aerial Vehicle and Fixed Imaging System

The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. In this study, measured surface velocities collected under low flow and high flow conditions in the Houlong River, Taiwan, using large-scale particle image velocimetry (LSPIV) captured by an unmanned aerial vehicle (UAV) and a terrestrial fixed station were analyzed and compared. Under low flow conditions, the mean absolute errors of the measured surface velocities using LSPIV from a UAV with shooting heights of 9, 12, and 15 m fell within 0.055 ± 0.015 m/s, which was lower than that obtained using LSPIV on video recorded from a terrestrial fixed station (i.e., 0.34 m/s). The mean absolute errors obtained using LSPIV derived from UAV aerial photography at a flight height of 12 m without seeding particles and with different seeding particle densities were slightly different, and fell within the range of 0.095 ± 0.025 m/s. Under high flow conditions, the mean absolute errors associated with using LSPIV derived from terrestrial fixed photography and LSPIV derived from a UAV with flight heights of 32, 62, and 112 m were 0.46 m/s and 0.49 m/s, 0.27 m, and 0.97 m/s, respectively. A UAV flight height of 62 m yielded the best measured surface velocity result. Moreover, we also demonstrated that the optimal appropriate interrogation area and image acquisition time interval using LSPIV with a UAV were 16 × 16 pixels and 1/8 s, respectively. These two parameters should be carefully adopted to accurately measure the surface velocity of rivers.