arXiv subjects

Jiarong Hong

Publications and source records attributed to Jiarong Hong.

At least 37 records · Page 2Linked to original sources

arXiv2023

In situ Biological Particle Analyzer based on Digital Inline Holography

Obtaining in situ measurements of biological microparticles is crucial for both scientific research and numerous industrial applications (e.g., early detection of harmful algal blooms, monitoring yeast during fermentation). However, existing methods are limited to offer timely diagnostics of these particles with sufficient accuracy and information. Here, we introduce a novel method for real-time, in situ analysis using machine learning assisted digital inline holography (DIH). Our machine learning model uses a customized YOLO v5 architecture specialized for the detection and classification of small biological particles. We demonstrate the effectiveness of our method in the analysis of 10 plankton species with equivalent high accuracy and significantly reduced processing time compared to previous methods. We also applied our method to differentiate yeast cells under four metabolic states and from two strains. Our results show that the proposed method can accurately detect and differentiate cellular and subcellular features related to metabolic states and strains. This study demonstrates the potential of machine learning driven DIH approach as a sensitive and versatile diagnostic tool for real-time, in situ analysis of both biotic and abiotic particles. This method can be readily deployed in a distributive manner for scientific research and manufacturing on an industrial scale.

physics.bio-ph↗

arXiv2022

Imaging-based 3D Particle Tracking System for Field Characterization of Particle Dynamics in Atmospheric Flows

A particle tracking velocimetry apparatus is presented that is capable of measuring three-dimensional particle trajectories across large volumes, of the order of several meters, during natural snowfall events. Field experiments, aimed at understanding snow settling kinematics in atmospheric flows, were conducted during the 2021/2022 winter season using this apparatus, from which we show preliminary results. An overview of the methodology, wherein we use a UAV-based calibration method, is provided, and analysis is conducted of a select dataset to demonstrate the capabilities of the system for studying inertial particle dynamics in atmospheric flows. A modular camera array is used, designed specifically for handling the challenges of field deployment during snowfall. This imaging system is calibrated using synchronized imaging of a UAV-carried target to enable measurements centered 10 m above the ground within approximately a 4 m x 4 m x 6 m volume. Using the measured Lagrangian particle tracks we present data concerning 3D trajectory curvature and acceleration statistics, as well as clustering behavior using Voronoi analysis. The limitations, as well as potential future developments, of such a system are discussed in the context of applications with other inertial particles.

physics.flu-dyn↗

arXiv2022

Snow particle analyzer for simultaneous measurements of snow density and morphology

The detailed characterization of snow particles is critical for understanding the snow settling behavior and modeling the ground snow accumulation for various applications such as prevention of avalanches and snowmelt-caused floods, etc. In this study, we present a snow particle analyzer for simultaneous measurements of various properties of fresh falling snow, including their concentration, size, shape, type, and density. The analyzer consists of a digital inline holography module for imaging falling snow particles in a sample volume of 88 cm3 and a high-precision scale to measure the weight of the same particles in a synchronized fashion. The holographic images are processed in real-time using a machine learning model and post-processing to determine snow particle concentration, size, shape, and type. Such information is used to obtain the estimated volume, which is subsequently correlated with the weight of snow particles to estimate their density. The performance of the analyzer is assessed using monodispersed spherical glass beads and irregular salt crystals with known density, which shows <5% density measurement errors. In addition, the analyzer was tested in a number of field deployments under different snow and wind conditions. The system is able to achieve measurements of various snow properties at single particle resolution and statistical robustness. The analyzer was also deployed for four hours of operation during a snow event with changing snow and wind conditions, demonstrating its ability for long-term and real-time monitoring of the time-varying snow properties in the field.

physics.ao-ph↗

arXiv2022

Direct measurement of vorticity using tracer particles with internal markers

Current experiment techniques for vorticity measurement suffer from limited spatial and temporal resolution to resolve the small-scale eddy dynamics in turbulence. In this study, we develop a new method for direct vorticity measurement in fluid flows based on digital inline holography (DIH). The DIH system utilizes a collimated laser beam to illuminate the tracers with internal markers and a digital sensor to record the generated holograms. The tracers made of the polydimethylsiloxane (PDMS) prepolymer mixed with internal markers are fabricated using a standard microfluidic droplet generator. A rotation measurement algorithm is developed based on the 3D location reconstruction and tracking of the internal markers and is assessed through synthetic holograms to identify the optimal parameter settings and measurement range (e.g., rotation rate from 0.3 to 0.7 rad/frame under numerical aperture of imaging of 0.25). Our proposed method based on DIH is evaluated by a calibration experiment of single tracer rotation, which yields the same optimal measurement range. Using von Kármán swirling flow setup, we further demonstrate the capability of the approach to simultaneously measure the Lagrangian rotation and translation of multiple tracers. Our method can measure vorticity in a small region on the order of 100 $μ$m or less and can be potentially used to quantify the Kolmogorov-scale vorticity field in turbulent flows.

physics.flu-dyn↗

arXiv2021

Characterization of atmospheric coherent structures and their impact on a utility-scale wind turbine

Atmospheric turbulent velocity fluctuations are known to increase wind turbine structural loading and accelerate wake recovery, but the impact of vortical coherent structures in the atmosphere on wind turbines has not yet been evaluated. The current study uses flow imaging with natural snowfall with a field of view spanning the inflow and near wake. Vortical coherent structures with diameters on the order of 1 m are identified and characterized in the flow approaching a 2.5 MW wind turbine in the region spanning the bottom blade tip elevation to hub height. Their impact on turbine structural loading, power generation, and wake behaviour are evaluated. Long coherent structure packets (larger than 200 m) are shown to increase fluctuating stresses on the turbine support tower. Large inflow vortices interact with the turbine blades, leading to deviations from the expected power generation. The sign of these deviations is related to the rotation direction of the vortices, with rotation in the same direction as the circulation on the blades leading to periods of power surplus, and opposite rotation causing power deficit. Periods of power deficit coincide with wake contraction events. These findings highlight the importance of considering coherent structure properties when making turbine design and siting decisions.

physics.flu-dyn↗

arXiv2021

Probing into gas leakage characteristics of ventilated supercavity through bubbly wake measurement

The stability of ventilated supercavitation is strongly influenced by gas leakage characteristics of the cavity. Here we conduct a systematic investigation of such characteristics under different closure conditions including re-entrant jet (RJ), quad vortex (QV), twin vortex (TV), and pulsating twin vortex (PTV). Using high-speed digital inline holography (DIH), all the individual bubbles shed from the cavity are imaged downstream and are used to quantify the instantaneous gas leakage from the cavity. In general, the supercavity gas leakage exhibits significant fluctuations under all closure types with the instantaneous leakage rate spiking up to 20 times of the ventilation input under RJ and QV closures. However, the magnitude and occurrence rate of such excessive gas leakage vary substantially across different closures, tunnel speeds, and ventilation conditions. Particularly, as the supercavity transitions from RJ, to QV, TV, and PTV with increasing ventilation or decreasing tunnel speed, the relative excessive gas leakage decreases sharply from RJ to QV, plateaus from QV to TV, and drops again from TV to PTV. Correspondingly, the occurrence frequency of such excessive leakage first exhibits a double peak distribution under RJ, migrates to a single peak mode under QV and TV, and eventually transitions to a distribution with a broadened peak at a higher frequency under PTV. These trends can be explained by the flow instabilities associated with three distinct gas leakage mechanisms. Subsequently, two metrics are introduced to quantify the relative change of ventilation needed to compensate for the change of extra gas loss and the predictability of the occurrence of excessive gas leakage, respectively. Based on these metrics, we suggest that the supercavity operating under TV closure with moderate ventilation is optimal for ventilation-based controls of supercavity stability.

physics.flu-dyn↗

arXiv2021

Wind Farm Icing Loss Forecast Pertinent to Winter Extremes

The 2021 Texas power crisis has highlighted the vulnerability of the power system under wind extremes, particularly with the increasing penetration of energy resources that depend on weather conditions (e.g., wind energy). The current wind power forecast models do not effectively consider the impact of such extreme weather events. In the present study, we provide a fast and robust statistical model developed using ten years of utility-scale turbine data at the Eolos Wind Energy Research Station to forecast the icing losses under such weather conditions. This model covers different cold climate impacts, including precipitation icing, frost contamination, and low-temperature effect. This model has been assessed using three large-scale (larger than 100 MW) wind farm data involving turbines with different capacities and from different manufacturers across multiple geographic regions. Notably, the model has been used to predict the wind power losses in entire Texas (larger than 91% of total wind installation in Texas) during 2021 Texas power crisis. The proposed model can be easily integrated into the existing wind farm and power grid operations, allowing the power system operators can develop more appropriate and pinpointed plans to balance the severe and sudden energy deficits and increase the system integrity under winter extremes.

physics.data-an↗

arXiv2021

Data-driven yaw misalignment correction for utility-scale wind turbines

In recent years, wind turbine yaw misalignment that tends to degrade the turbine power production and impact the blade fatigue loads raises more attention along with the rapid development of large-scale wind turbines. The state-of-the-art correction methods require additional instruments such as LiDAR to provide the ground truths and are not suitable for long-term operation and large-scale implementation due to the high costs. In the present study, we propose a framework that enables the effective and efficient detection and correction of static and dynamic yaw errors by using only turbine SCADA data, suitable for a low-cost regular inspection for large-scale wind farms in onshore, coastal, and offshore sites. This framework includes a short-period data collection of the turbine operating under multiple static yaw errors, a data mining correction for the static yaw error, and ultra-short-term dynamic yaw error forecasts with machine learning algorithms. Three regression algorithms, i.e., linear, support vector machine, and random forest, and a hybrid model based on the average prediction of the three, have been tested for dynamic yaw error prediction and compared using the field measurement data from a 2.5 MW turbine. For the data collected in the present study, the hybrid method shows the best performance and can reduce total yaw error by up to 85% (on average of 71%) compared to the cases without static and dynamic yaw error corrections. In addition, we have tested the transferability of the proposed method in the application of detecting other static and dynamic yaw errors.

eess.SP↗

arXiv2021

Evidence of preferential sweeping during snow settling in atmospheric turbulence

We present a field study of snow settling dynamics based on simultaneous measurements of the atmospheric flow field and snow particle trajectories. Specifically, a super-large-scale particle image velocimetry (SLPIV) system using natural snow particles as tracers is deployed to quantify the velocity field and identify vortex structures in a 22 m $\times$ 39 m field of view centered 18 m above the ground. Simultaneously, we track individual snow particles in a 3 m $\times$ 5 m sample area within the SLPIV using particle tracking velocimetry (PTV). The results reveal the direct linkage among vortex structures in atmospheric turbulence, the spatial distribution of snow particle concentration, and their settling dynamics. In particular, with snow turbulence interaction at near-critical Stokes number, the settling velocity enhancement of snow particles is multifold, and larger than what has been observed in previous field studies. SLPIV measurements show higher concentration of snow particles preferentially located on the downward side of the vortices identified in the atmospheric flow field. PTV, performed on high resolution images around the reconstructed vortices, confirms the latter trend and provides statistical evidence of the acceleration of snow particles, as they move toward the downward side of vortices. Overall, the simultaneous multi-scale particle imaging presented here enables us to directly quantify the salient features of preferential sweeping, supporting it as an underlying mechanism of snow settling enhancement in the atmospheric surface layer.

physics.flu-dyn↗

arXiv2021

Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine

The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational conditions, as observed in previous field-scale measurements. Most wind turbine wake investigations assume quasi-steady conditions, but real wind turbines operate in a highly stochastic atmosphere, and their operation (e.g., blade pitch, yaw angle) changes constantly in response. Furthermore, dynamic control strategies have been recently proposed to optimize wind farm power generation and longevity. Therefore, improved understanding of dynamic wake behaviors is essential. First, changes in blade pitch are investigated and the wake expansion response is found to display hysteresis as a result of flow inertia. The timescales of the wake response to different pitch rates are quantified. Next, changes in wind direction with different timescales are explored. Under short timescales, the wake deflection is in the opposite direction of that observed under quasi-steady conditions. Finally, yaw changes are implemented at different rates, and the maximum inverse wake deflection and timescale are quantified, showing a clear dependence on yaw rate. To gain further physical understanding of the mechanism behind the inverse wake deflection, the streamwise vorticity in different parts of the wake is quantified. The results of this study provide guidance for the design of advanced wake flow control algorithms. The lag in wake response observed for both blade pitch and yaw changes shows that proposed dynamic control strategies must implement turbine operational changes with a timescale on the order of the rotor timescale or slower.

physics.flu-dyn↗

arXiv2021

3D flow motions in the viscous sublayer

We employ novel digital Fresnel reflection holography to capture the 3D flows within the viscous sublayer of a smooth-wall turbulent channel flow at Reτ=400. The measurements reveal unsteady and diverse flow patterns in the sublayer including nearly uniform high and low speed flows and strong small-scale (on the order of viscous wall units) spanwise meandering motions. The probability density functions (PDFs) of wall shear stresses show a clear discrepancy in high stress range with those from direct numerical simulation (DNS), which is attributed to the unresolved streamwise and spanwise motions by DNS. Moreover, the PDF of Lagrangian particle accelerations yields a stretched exponential shape like that in homogenous isotropic turbulence, indicating strong intermittency in the sublayer. We find a significant fraction of high accelerations is associated with the small-scale meandering motions. Our study helps explain the effect of sublayer-scale roughness on reducing drag and flow separation reported in the literature.

physics.flu-dyn↗

arXiv2021

Evaluation of laser diffraction-based particle size measurements using digital inline holography

The measurements of size distribution of small particles (e.g. dusts, droplets, bubbles, etc.) are critical for a broad range of applications in environmental science, public health, industrial manufacturing, etc. Laser diffraction (LD), a widely used method for such applications depends on model-based inversion with underlying assumptions on particle properties. Furthermore, the presence of sampling biases such as velocity differentials are often overlooked by the simple ex-situ calibrations, which introduces as an additional source of error. In contrast, digital inline holography (DIH), a single camera coherent imaging technique, can both measure particle size distributions without the need for a model-based inversion and can directly provide information on the shape characteristics of the particles. In this study, we evaluate the performance of an LD system in characterizing polydisperse droplets produced in a flat fan spray using in-situ DIH based imaging as a reference. The systematic differences in the two techniques are examined. A droplet-trajectory-based correction for the LD-inferred size distributions is proposed to compensate for the observed differences. We validate the correction using NIST standard polydisperse particles undergoing differential settling, and then apply the correction to polydisperse spray droplet measurements. The correction improves agreement between LD and DIH size distributions for droplets over two orders of magnitude, but with LD still underestimating the fraction of droplets at sizes above ~1 mm. This underestimation is possibly linked to the complex oscillatory and rotational motion of droplets which cannot be faithfully captured by measurement or modelled by the correction algorithm without additional information.

physics.flu-dyn↗

arXiv2021

Insights into ventilation hysteresis shift due to flow unsteadiness in ventilated supercavitation

Understanding ventilation strategy of a supercavity is important for designing high-speed underwater vehicles wherein an artificial gas pocket is created behind a flow separation device for drag reduction. Our study investigates the effect of flow unsteadiness on the ventilation requirements to form (CQf) and collapse (CQc) a supercavity. Imposing flow unsteadiness on the incoming flow has shown an increment in higher CQf at low free stream velocity and lower CQf at high free stream velocity. High-speed imaging reveals distinctly different behaviors in the recirculation region for low and high freestream velocity under unsteady flows. At low free stream velocities, the recirculation region formed downstream of a cavitator shifted vertically with flow unsteadiness, resulting in lower bubble collision and coalescence probability, which is critical for the supercavity formation process. The recirculation region negligibly changed with flow unsteadiness at high free stream velocity and less ventilation is required to form a supercavity compared to that of the steady incoming flow. Such a difference is attributed to the increased transverse Reynolds stress that aids bubble collision in a confined space of the recirculation region. CQc is found to heavily rely on the vertical component of the flow unsteadiness and the free stream velocity. Interfacial instability located upper rear of the supercavity develops noticeably with flow unsteadiness and additional bubbles formed by the distorted interface shed from the supercavity, resulting in an increased CQc. Further analysis on the quantification of such additional bubble leakage rate indicates that the development and amplitude of the interfacial instability accounts for the variation of CQc under a wide range of flow unsteadiness. Our study provides some insights on the design of a ventilation strategy for supercavitating vehicles in practice.

physics.flu-dyn↗

arXiv2021

Spatially-Resolved Characterization of Oil-in-Water Emulsion Sprays

This study concerns the effects of pressure, spatial location, and application of oil emulsions on the resulting droplet size, eccentricity, as well as velocity distributions, all of which are crucial information in determining the dispersion dynamics of the droplets during the spray applications. Experiments were conducted with the abovementioned droplets information measured using digital inline holography (DIH). Results show that the volumetric droplet size distributions (VDSD) span widely from sub-200 um to over 2 mm in size. The application of an oil-in-water emulsion results largely in the suppression of smaller droplets, while the VDSD is relatively insensitive to increasing the oil volume fraction beyond a critical level. DIH additionally enables the determination of size-dependent droplet eccentricity and velocity measurements. Interestingly, the application of oil-in-water emulsion generally decreases the eccentricity, more significantly at the center than at the edge of the spray fan. We attribute this decrease to the increase in lamella sheet thickness and thus decrease in characteristic shrinkage rates, consistent with the observation using high-speed shadowgraphs. In all instances, oil-in-water emulsion droplets have higher velocities than equivalent sized water droplets. We attribute this to the earlier action of the spray breakup process in the oil-in-water emulsion, reduced surface energy generation during a breakup (larger droplets), and reduce energy dissipation during breakup with oil-in-water emulsions, leading to increased translational energy after the breakup process. Therefore, it appears that oil-in-water emulsion application simultaneously suppresses small droplet formation and increases droplet velocity, and hence spray penetration in agricultural application.

physics.flu-dyn↗

arXiv2020

Droplet evaporation residue indicating SARS-COV-2 survivability on surfaces

SARS-CoV-2 survives and remains viable on surfaces for several days under different environments as reported in recent studies. However, it is unclear how the viruses survive for such a long time and why their survivability varies across different surfaces. To address these questions, we conduct systematic experiments investigating the evaporation of droplets produced by a nebulizer and human-exhaled gas on surfaces. We found that these droplets do not disappear with evaporation, but instead shrink to a size of a few micrometers (referred to as residues), persist for more than 24 hours, and are highly durable against changes of environmental conditions. The characteristics of these residues change significantly across surface types. Specifically, surfaces with high thermal conductivity like copper do not leave any resolvable residues, while stainless steel, plastic, and glass surfaces form residues from a varying fraction of all deposited droplets at 40% relative humidity. Lowering humidity level suppresses the formation of residues while increasing humidity level enhances it. Our results suggest that these microscale residues can potentially insulate the virus against environmental changes, allowing them to survive inhospitable environments and remain infectious for prolonged durations after deposition. Our findings can also be extended to other viruses transmitted through respiratory droplets (e.g., SARS-CoV, flu viruses, etc.), and can thus lead to practical guidelines for disinfecting surfaces and other prevention measures (e.g., humidity control) for limiting viral transmission.

physics.med-ph↗

arXiv2020

Risk assessment of airborne transmission of COVID-19 by asymptomatic individuals under different practical settings

The lack of quantitative risk assessment of airborne transmission of COVID-19 under practical settings leads to large uncertainties and inconsistencies in our preventive measures. Combining in situ measurements and numerical simulations, we quantify the exhaled particles from normal respiratory behaviors and their transport under elevator, small classroom and supermarket settings to evaluate the risk of inhaling potentially virus-containing particles. Our results show that the design of ventilation is critical for reducing the risk of particle encounters. Inappropriate design can significantly limit the efficiency of particle removal, create local hot spots with orders of magnitude higher risks, and enhance particle deposition causing surface contamination. Additionally, our measurements reveal the presence of substantial fraction of crystalline particles from normal breathing and its strong correlation with breathing depth.

physics.med-ph↗

arXiv2020

Settling and Clustering of Snow Particles in Atmospheric Turbulence

The effect of turbulence on snow precipitation is not incorporated into present weather forecasting models. Here we show evidence that turbulence is in fact a key influence on both fall speed and spatial distribution of settling snow. We consider three snowfall events under vastly different levels of atmospheric turbulence. We characterize the size and morphology of the snow particles, and we simultaneously image their velocity, acceleration, and relative concentration over vertical planes about 30 m2 in area. We find that turbulence-driven settling enhancement explains otherwise contradictory trends between the particle size and velocity. The estimates of the Stokes number and the correlation between vertical velocity and local concentration indicate that the enhanced settling is rooted in the preferential sweeping mechanism. When the snow vertical velocity is large compared to the characteristic turbulence velocity, the crossing trajectories effect results in strong accelerations. When the conditions of preferential sweeping are met, the concentration field is highly non-uniform and clustering appears over a wide range of scales. These clusters, identified for the first time in a naturally occurring flow, display the signature features seen in canonical settings: power-law size distribution, fractal-like shape, vertical elongation, and large fall speed that increases with the cluster size. These findings demonstrate that the fundamental phenomenology of particle-laden turbulence can be leveraged towards a better predictive understanding of snow precipitation and ground snow accumulation. They also demonstrate how environmental flows can be used to investigate dispersed multiphase flows at Reynolds numbers not accessible in laboratory experiments or numerical simulations.

physics.flu-dyn↗

arXiv2020

Operational-dependent wind turbine wake impact on surface momentum flux revealed by snow-powered flow imaging

As wind energy continues to expand, increased interaction between wind farms and their surroundings can be expected. Using natural snowfall to visualize the air flow in the wake of a utility-scale wind turbine at unprecedented spatio-temporal resolution, we observe intermittent periods of strong interaction between the wake and the ground surface and quantify the momentum flux during these periods. Significantly, we identify two turbine operational-dependent pathways that lead to these periods of increased wake-ground interaction. Data from a nearby meteorological tower provides further insights into the strength and persistence of the enhanced flux for each pathway under different atmospheric conditions. These pathways allow us to resolve discrepancies between previous conflicting studies on the impact of wind turbines on surface fluxes. Furthermore, we use our results to generate a map of the potential impact of wind farms on surface momentum flux throughout the Continental United States, providing a valuable resource for wind farm siting decisions. These findings have implications for agriculture in particular, as crop growth is significantly affected by surface fluxes.

physics.ao-ph↗