Physics of Fluids

Aerosols and droplets generated from expiratory events play a critical role in the transmission of infectious respiratory viruses. Increasingly robust evidence has suggested the crucial role of fine aerosols in airborne transmission of respiratory diseases, which is now widely regarded as an important transmission path of COVID-19. In this report, we used CFD modelling to investigate the efficiency of using portable air purifiers containing HEPA filters to reduce airborne aerosols in hospitals and serve as a potential retrofit mitigation strategy. We used a consulting room to set up our simulations because currently the clearance time between consultations is the controlling factor that limits the patient turnover rate. The results suggest the inlet/suction of the air purifier unit should be lifted above the floor to achieve better clearance efficiency, with up to 40% improvement possible. If multiple air purifiers are used, the combined efficiency can increase to 62%. This work provides practical guidance on a mitigation strategy that can be easily implemented in an expedient, cost-effective and rapid manner, and paves the way for developing more science-informed strategies to mitigate the airborne transmission of respiratory infections in hospitals.

Circulating tumour cells (CTCs) are promising biomarkers for early cancer detection, yet their extreme rarity in blood necessitates efficient separation from white blood cells (WBCs) in lysed liquid biopsies. Inertial microfluidics offers a high-throughput, label-free approach to this challenge by leveraging size-dependent lateral migration. However, experimental observations reveal that WBCs migrate more rapidly than predicted, reducing separation performance. Using 3D lattice-Boltzmann-immersed-boundary-finite-element simulations, we characterized the migration dynamics of CTCs and WBCs in a straight microchannel. Our results reveal that the presence of a CTC enhances WBC cross-streamline migration, providing a mechanistic explanation for WBC contamination in CTC-enriched outlets. The numerical model capturing heterochiral orbital dynamics was validated experimentally, confirming the role of intercellular hydrodynamic interactions. These findings underscore the critical role of intercellular interactions in inertial microfluidic systems and provide guidance for optimizing suspension concentration and channel geometry to improve purity in rare cell isolation.

To compare the transport of respiratory pathogens, computational fluid dynamics (CFD) simulations were performed to track particles released by coughing from a passenger on a Boeing 737 aircraft, and by a person in a comparable indoor commercial space. Simulation data were post-processed to calculate the amounts of particles inhaled by nearby persons in both environments. The effects of different airflow rates, placement of air inlets, positioning and distances between index (coughing) and susceptible (inhaling) persons were also analyzed. The removal of airborne particles from the indoor environment, due ventilation and deposition onto surfaces, was compared to that of an aircraft cabin. In an aircraft cabin 80% of the particles were removed 5 to 12 times faster than in the indoor commercial space; ultimately resulting in 7 times less particulate mass inhaled in the aircraft cabin.

In this study we assessed the effect of ethanol on the filtering properties of FFP2 masks. The permeability of parts of a FFP2 mask was measured before and after six cleanings with ethanol. As for any porous medium, the filtering properties of a mask are related to the size and tortuosity of the pores of the filter, and are quantified by its permeability. Any damage to the filter will change its permeability. We show here that after six cleaning cycles, the permeability remains very close to the permeability before cleaning. Amid the COVID-19 pandemic and the shortage of protective masks, this study suggests that ethanol could be used to sanitize a FFP2 mask without significantly altering its filtering properties. Additional measurements on FFP2 and N95 masks from different manufacturers need to be performed to validate this study.

The intricate dynamics of volatile organic compounds (VOCs) in the human respiratory system remain poorly understood. In the present study, we integrate odor sensory tests (OSTs) coupled with computational fluid dynamics and a physiologically based pharmacokinetic (CFD-PBPK) model to elucidate various aspects of the olfaction process. Safe yogurt-derived substances were incorporated in OSTs to prevent harmful exposure. Acetaldehyde was identified as a key active component in determining odor intensity, prompting further analysis of acetone and other four constituents of yogurt. Logarithmic correlations were established between the perceived odor intensity from the OSTs and both time-averaged absorption flux and equilibrium concentration within the olfactory mucus layer. These parameters were numerically captured, enabling the logarithmic approximation of odor intensity for different breathing profiles and the development of reliable prediction models for odor sensation based on quantifiable physiological parameters. The CFD-PBPK model captured detailed spatial and temporal variations of these parameters, which offers potential for future integrated/in-silico applications. Minor peaks of odor concentration were observed in the posterior olfactory regions during exhalation, revealing a retro-nasal phenomenon. Location-specific analysis revealed the nostrils and olfactory regions as the most accurate indicators of perceived odor intensity, proving the limitations of rough sensory assessments in the indoor/breathing zone scales. Acetone exhibited distinct absorption and desorption trends during the transitional phase between inhalation and exhalation, owing to the physical properties (diffusion and partition coefficients) that strongly characterize the olfaction dynamics. Author SummaryWe developed an integrated method using odor sensory tests (OSTs) coupled with computational fluid dynamics and physiologically based pharmacokinetic models (CFD-PBPK) to assess the temporal and spatial transport of yogurt odorants to the human olfactory region. Multiple phenomena were observed, including ortho-nasal and retro-nasal olfaction, temporal changes in the perceived odor intensity associated with breathing/sniffing profiles, absorption and desorption curves of acetone in the mucus epithelium, and regional-based olfaction distribution. The perceived odor intensity from OSTs can be predicted logarithmically in correlation with both the time-averaged absorption flux and the equilibrium concentration in the olfactory mucus layer, offering a reliable in-silico prediction model for odor sensation based on numerically quantifiable parameters. This model offers potential implications for multiple computational, biomedical, and industrial applications, such as the electric noses, smart odor sensors, food assessments, and fragrance development, particularly for long-term exposure in the industries that emit odorous compounds. It can open the door for more accurate predictions of the complex micro-fluid dynamics in the microbial ciliated tissues in the olfactory receptors.

There exists an ongoing need to improve the validity and accuracy of computational fluid dynamics (CFD) simulations of turbulent airflows in the extra-thoracic and upper airways. Yet, a knowledge gap remains in providing experimentally-resolved 3D flow benchmarks with sufficient data density and completeness for useful comparison with widely-employed numerical schemes. Motivated by such shortcomings, the present work details to the best of our knowledge the first attempt to deliver in vitro-in silico correlations of 3D respiratory airflows in a generalized mouth-throat model and thereby assess the performance of Large Eddy Simulations (LES) and Reynolds-Averaged Numerical Simulations (RANS). Numerical predictions are compared against 3D volumetric flow measurements using Tomographic Particle Image Velocimetry (TPIV) at three steady inhalation flowrates varying from shallow to deep inhalation conditions. We find that a RANS k-{omega} SST model adequately predicts velocity flow patterns for Reynolds numbers spanning 1500 to 7000, supporting results in close proximity to a more computationally-expensive LES model. Yet, RANS significantly underestimates turbulent kinetic energy (TKE), thus underlining the advantages of LES as a higher-order turbulence modeling scheme. In an effort to bridge future endevours across respiratory research disciplines, we provide end users with the present in vitro -in silico correlation data for improved predictive CFD models towards inhalation therapy and therapeutic or toxic dosimetry endpoints. Author SummaryThe dispersion and ensuing deposition of inhaled airborne particulate matter in the lungs are strongly influenced by the dynamics of turbulent respiratory airflows in the mouth-throat region during inhalation. To cirumvent costly in vitro experimental measurement resources, fluid dynamics (CFD) simulations are widely sought to predict deposition outcomes but often lack detailed experimental data to first validate the three-dimensional (3D) flow structures anticipated to arise in the upper respiratory tract. In an effort to reconcile such data scarcity, we deliver experimental-numerical correlations of 3D respiratory airflows in an idealized 3D printed mouth-throat model against two widely-established numerical schemes with varying computational costs, namely coarse RANS and finer LES technique. Our time-resolved 3D flow data underline the complexity of these physiological inhalation flows, and discuss advantages and drawbacks of the different numerical techniques. With an outlook on future respiratory applications geared towards broad preclinical inhaled aerosol deposition studies, our open source data are made available for future benchmark comparisons for a broad range of end users in the respiratory research community.

The Next Generation Impactor (NGI) is one of the regulatory gold standards for characterizing aerodynamic particle size distributions (APSDs) of orally inhaled drug products (OIDPs); however, its reliance on complex, resource-intensive in vitro testing under tightly controlled environmental conditions limits experimental flexibility and introduces variability. In alignment with the growing regulatory emphasis on New Approach Methodologies (NAMs) for drug development, this study presents a rigorously validated computational fluid particle dynamics (CFPD) based virtual NGI (vNGI) as an in silico method complementary to conventional testing. The vNGI replicates a significant portion of the NGI geometry and airflow physics, enabling high-resolution spatiotemporal analysis of aerosol transport and deposition mechanisms that are otherwise inaccessible experimentally. A comprehensive verification and validation framework was implemented, including mesh and particle independence studies, turbulence model assessment, and comparison of stagewise deposition efficiencies with available in vitro data at 30 L/min. The model's capabilities were further extended to low and high flow rates, and two bio-relevant mouth-throat models and polydisperse particle laden aerosol were added. The model demonstrates strong predictive capability for a few stages and provides mechanistic insight into discrepancies in other stages, depending on the type of analysis. Importantly, this work establishes the vNGI as a fit-for-purpose according to NAM by (i) defining a clear context of use for APSD prediction and inhaler performance evaluation, (ii) capturing physically and biologically relevant air-particle interactions, and (iii) demonstrating technical robustness and reproducibility through systematic validation. The platform can potentially further enable simulation of environmental and physiological conditions, such as humidity effects, that are difficult to control experimentally, thereby improving human relevance and reducing reliance on costly and time-consuming in vitro testing. This study positions the vNGI as a scalable, regulatory aligned NAM capable of supporting early stage drug device combination product development, device optimization, and an alternative bioequivalence assessment, contributing to ongoing efforts to enhance predictive performance, reduce experimental burden, and transition toward human centric, inhalation product evaluation.

ObjectivesConsidering three viral transmission routes: fomite contact, aerial transmission by droplets, and aerial transmission by aerosols, the aerial routes have been the focus of debate about the relative role of droplets and aerosols in SARS-CoV-2 infection. We seek to quantify infection risk in an enclosed space via short-range airborne transmission from droplets and long-range risk from aerosols toward focusing public health measures. MethodsData from three published studies were analyzed to predict relative exposure at distances of 1 m and farther, mediated by droplet size divided into two bins: larger than 8 {micro}m and smaller than 75 {micro}m (medium droplets) and smaller than 8 {micro}m (small droplets or aerosols). The results at 1 m from an infectious individual were treated as a boundary condition to model infection risk at greater distance. At all distances, infection risk was treated as the sum of exposure to small and medium droplets. It was assumed that number of virions is proportional to droplet volume. ResultsThe largest infection risk (as exposure to droplet volume) came from medium droplets, close to the infectious individual out to approximately 1 m. Farther away, the largest risk was due to aerosols. For one model, medium droplet exposure disappeared at 1.8 m. ConclusionsPolicy concerning social distancing for meaningful infection reduction relies on droplet exposure as a function of distance, yet within this construct droplet size determines respiratory deposition. This two-fold distance effect can be used to evaluate additional measures such as plexiglass barriers and masking.

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles to these regions is crucial to maximize treatment efficacy while minimizing side effects. However, conventional inhalation therapy approaches (i.e., full-mouth particle release and inhalation (FMD)) typically result in insufficient drug deposition in the small airways and an uneven distribution across the five lung lobes. To address such deficiencies, the goals of this study are triple folds: (1) to develop a fast and accurate framework to secure target drug delivery (TDD) nozzle diameter and location based on the conventional computational fluid particle dynamics (CFPD)-FMD simulations, (2) to develop a CFPD-informed machine learning (ML) inverse-design framework that predicts optimal inhaler nozzle parameters based on patient-specific breathing patterns and drug properties, and (3) to demonstrate the feasibility of embedding this framework into a user-centered smart inhaler prototype to improve uniform TTD to the small airways across all five lung lobes. Specifically, a subject-specific mouth-to-generation-10 human respiratory system was employed, and 108 high-fidelity CFPD-FMD simulations were performed under varied physiological and design parameters, including tidal volume, particle diameter, release location, and release timing. Particle release maps generated from those CFPD-FMD simulations via backtracking identified optimal nozzle diameters and locations that promote uniform multi-lobe drug delivery while limiting off-target deposition. Accordingly, a dataset was compiled with inputs (i.e., flow rate, particle size, release z-coordinate, release time) and targets (i.e., nozzle center x- and y-coordinates, nozzle diameter). These inputs and targets form the CFPD-TDD dataset, on which 16 ML models were trained to learn inverse mapping from patient- and drug-specific inputs to optimal nozzle design parameters. Performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) overall and per target feature. Parametric analysis using CFPD-FMD simulations was conducted to determine how patient-specific and drug-specific factors affect pulmonary air-particle transport dynamics and to explain why achieving CFPD-TDD in small airways with CFPD-FMD strategies remains challenging. Furthermore, the ML evaluation in this feasibility study demonstrated robust learning of the inverse mapping from patient-specific inputs to optimal nozzle parameters. Four top-performing models showed consistently low MSE/MAE across cases, and an ensemble (i.e., mixed model (MixModel)) combining their strengths was formulated. Independent CFPD-TDD simulations beyond the training and testing datasets were used as the ground truth to validate ML-predicted nozzle configurations. Compared with conventional CFPD-FMD strategies, ML-guided nozzle designs significantly improved inter-lobar deposition uniformity and reduced off-target deposition in the upper airways, demonstrating the feasibility of ML-enabled TDD to the small airways. Overall, this study establishes a CFPD-informed ML inverse-design framework as a viable algorithmic foundation for user-centered smart inhalers, enabling adaptive, patient-specific TDD to the small airways with improved deposition uniformity across all five lung lobes. By integrating first-principle-based CFPD with ML, this work provides a methodological pathway toward next-generation smart inhalers for more effective treatment of small airway diseases.

An instrument-free particle-templated droplet formation can be achieved upon simple mixing of amphiphilic particles with aqueous and oil phases in a well plate by using a common lab pipette. Here, a two-dimensional, two-phase flow model was established using a finite element method to mimic the droplet formation within a concentric amphiphilic particle, which consisted of an outer hydrophobic layer and an inner hydrophilic layer. Immiscible water and oil phases selectively interacted with the hydrophilic and hydrophobic layers of the particle, respectively, to form an isolated aqueous compartment within a cavity. Three extreme models were also simulated, including completely hydrophilic, completely hydrophobic, and oppositely amphiphilic particle, which indicated that a right order of the particle layers was necessary to capture the droplet inside the cavity. Moreover, we performed a systematic study of particle-templated droplet formation by varying the individual layer thicknesses of particle, particle height, interfacial tension between water and oil, contact angle of interface with different surfaces, velocity of incoming oil media, and distance between neighboring particles. The volume fraction of water droplet trapped within the target cavity region was calculated to characterize the droplet formation. Our work will help to optimize the particle fabrication process, predict the experiment droplet formation, and explain the physical mechanism underlying compartmentalization phenomena.

Deposits of biofluid droplets on surfaces (such as respiratory droplets formed during an expiratory event fallen on surfaces) are composed of the water-based salt-protein solution that may also contain an infection (bacterial/viral).The final patterns of the deposit formed are dictated by the composition of the fluid and flow dynamics within the droplet. This work reports the spatio-temporal, topological regulation of deposits of respiratory fluid droplets and control of motility of bacteria by tweaking flow inside droplets using non-contact vapor-mediated interactions. When evaporated on a glass surface, respiratory droplets form haphazard multiscale dendritic, cruciform-shaped precipitates--using vapor mediation as a tool to control these deposits at the level of nano-micro-millimeter scales. Wemorphologically control dendrite orientation, size and subsequently suppress cruciform-shaped crystals. The nucleation sites are controlled via preferential transfer of solutes in the droplets; thus, achieving control over crystal occurrence and growth dynamics. The active living matter like bacteria is also preferentially segregated with controlled motility without attenuation of its viability and pathogenesis. For the first time, we have experimentally presented a proof-of-concept to control the motion of live active matter like bacteria in a near non-intrusive manner. The methodology can have ramifications in biomedical applications like disease detection, controlling bacterial motility, and bacterial segregation.

Drug delivery for viral respiratory infections, such as SARS-CoV-2, can be enhanced significantly by targeting the nasopharynx, which is the dominant initial infection site in the upper airway, for example by nasal sprays. However, under the standard recommended spray usage protocol ("Current Use", or CU), the nozzle enters the nose almost vertically, resulting in sub-optimal deposition of drug droplets at the nasopharynx. Using computational fluid dynamics simulations in two anatomic nasal geometries, along with experimental validation of the generic findings in a different third subject, we have identified a new "Improved Use" (or, IU) spray protocol. It entails pointing the spray bottle at a shallower angle (almost horizontally), aiming slightly toward the cheeks. We have simulated the performance of this protocol for conically injected spray droplet sizes of 1 - 24 m, at two breathing rates: 15 and 30 L/min. The lower flowrate corresponds to resting breathing and follows a viscous-laminar model; the higher rate, standing in for moderate breathing conditions, is turbulent and is tracked via Large Eddy Simulation. The results show that (a) droplets sized between [~] 6 - 14 m are most efficient at direct landing over the nasopharyngeal viral infection hot-spot; and (b) targeted drug delivery via IU outperforms CU by approximately 2 orders-of-magnitude, under the two tested inhalation conditions. Also quite importantly, the improved delivery strategy, facilitated by the IU protocol, is found to be robust to small perturbations in spray direction, underlining the practical utility of this simple change in nasal spray administration protocol.

As we know, bacterial motion navigates complex environments in natural settings, providing a basis for scientific study to understand their dynamics. Hydrodynamics drive wall alignment and accumulation, but research remains unclear about the extent to which confinement alone, without hydrodynamics, modulates bacterial dynamics. Therefore, we develop a 2D model to study the effects of isolated steric and hydrodynamic forces on bacterial motion in 10- and 50-m microchannels. We used bacteria to model self-propelled rigid rods with run-and-tumble motion, and then compared dry systems (purely steric wall effects) with wet systems (hydrodynamic effects). We found that bacterial speeds and their orientation are independent of channel dimensions in dry systems. In wet systems, we observed strong wall-hugging and alignment due to wall-induced hydrodynamic interactions, enhanced residence times, and a slight increase in the observable effective speed in 10 m microchannels compared to 50 m channels (where bacteria-maintained bulk-like dynamics). Our study thus emphasises when confinement affects bacterial motion solely due to pure dry geometry, and when hydrodynamics play an essential role. This study provides a template for microfluidics-based experimental prediction of bacterial dynamics and could be applied to an analysis of antibiotic resistance. Significance StatementMicrobes rely on self-propelled motion to navigate complex environmental systems and survive and persist. In such an environment, physical interactions with surrounding boundaries play a key role in how bacteria move, orient, or settle in complex spaces. Although many studies show that hydrodynamic forces near walls influence how bacteria swim in liquid, there is still confusion about whether confinement, with or without hydrodynamic effects, can change the complete bacterial pattern. Our 2D model shows that confinement (purely steric wall effects, dry limit) alone does not alter bacterial motion patterns. It is the hydrodynamics (wet limit) that drives bacteria to orient toward walls, remain near boundaries, and direct motion in narrow channels. This work clarifies when confinement and hydrodynamics actually affect bacterias motion and provides a practical way to understand bacterial behaviour in biological systems, including in microfluidic applications and studies of antibiotic resistance strategies.

Swarming, a multicellular mode of flagella-based motility observed in many bacteria species, enables coordinated and rapid surface translocation, expansion and colonization. In the swarming state, bacterial films display several characteristics of active matter including intense and persistent long-ranged flocks and strong fluctuating velocity fields with significant vorticity. Swarm fronts are typically dynamically evolving interfaces. Many of these fronts separate motile active domains from passive frictional regions comprised of dead or non-motile bacteria. Here, we study the dynamics and structural features of a model active-passive interface in swarming Serratia marcescens. We expose localized regions of the swarm to high intensity wide-spectrum light thereby creating large domains of tightly packed immotile bacteria. When the light source is turned off, swarming bacteria outside this passivated region advance into this highly frictional domain and continuously reshape the interphase boundary. Combining results from Particle Image Velocimetry (PIV) and intensity based image analysis, we find that the evolving interface has quantifiable and defined roughness. Correlations between spatially separated surface fluctuations and damping of the same are influenced by the interaction of the interphase region with adjacently located and emergent collective flows. Dynamical growth exponents characterizing the spatiotemporal features of the surface are extracted and are found to differ from classically expected values for passive growth or erosion. To isolate the effects of hydrodynamic interactions generated by collective flows and that arising from steric interactions, we propose and analyze agent-based simulations with full hydrodynamics of rod-shaped, self-propelled particles. Our computations capture qualitative features of the swarm and predict correlation lengths consistent with experiments. We conclude that hydrodynamic and steric interactions enable different modes of surface dynamics, morphology and thus front invasion.

How human respiratory physiology and inhaled airflow therein proceed to impact transmission of SARS-CoV-2, leading to the initial infection, is an open question. An answer can help determine the susceptibility of an individual on exposure to a COVID-2019 carrier and can also quantify the still-unknown infectious dose for the disease. Combining computational fluid mechanics-based tracking of respiratory transport in anatomic domains with sputum assessment data from hospitalized COVID-19 patients and earlier measurements of ejecta size distribution during regular speech - this study shows that the regional deposition of virus-laden inhaled droplets at the initial nasopharyngeal infection sites, located in the upper airway, peaks over the droplet size range of 2.5 - 19 {micro}; and reveals that the number of virions that can potentially establish the infection is, at most, of[O] (102).

Partly overlapping feathers form a large part of birds wing surfaces, but in many species the outermost feathers split, making each feather function as an independent wing. These feathers are complex structures that evolved to fulfil both aerodynamic and structural functions. Yet relatively little is known about how the profile shape and microstructures of feathers impact aerodynamic performance. Here we determined, using fluid dynamic modelling, the aerodynamic capabilities of a section of the primary flight feather forming the leading edge of the split wing tip of a Jackdaw (Corvus monedula). Our findings demonstrate that the feather section exhibits a relatively high performance, with lift comparable to manmade aerofoils, however, there is a drag penalty associated with the feather shaft. The models vortex shedding behaviour shows low amplitude temporal fluctuations in lift, compared to manmade aerofoils. Notably, the aerodynamic pitch torque around the shaft varies with angle of attack. This, when combined with the built-in pitch-up twist of the feather implies a passive pitch control mechanism for the feather. Taken together, our findings suggest evolutionary adaptations of the flow around the feather, which could be of interest when designing micro-air vehicles and wind turbines. O_TBL View this table: org.highwire.dtl.DTLVardef@658851org.highwire.dtl.DTLVardef@1d8a3fforg.highwire.dtl.DTLVardef@1f6e34dorg.highwire.dtl.DTLVardef@1c4e085org.highwire.dtl.DTLVardef@7065b3_HPS_FORMAT_FIGEXP M_TBL C_TBL

Control of fluids is a hallmark of microfluidic systems and fundamental for the successful application of microfluidic devices. Trigger valves use geometric features to autonomously control the release of fluids in microfluidic devices. Our previous work has adapted geometries used in closed trigger valve systems to enable use in open systems, allowing for open microfluidic devices with up to three trigger valves. Here, we focus on the parallel co-flows produced by sequential release of trigger valves and present a model that predicts their layer widths as a function of the geometric characteristics of the different side channels of each trigger valve. We show layered co-flows with widths as low as 50 microns. Additionally, we expand the use of trigger valves in open microfluidic devices by incorporating 1) varied step heights, 2) devices with up to seven trigger valves, and 3) use of varied fluids and plastics. To validate the implementation and use of these trigger valves in open systems, we have developed a theoretical framework to compare predicted outcomes (i.e., fluid travel distance, velocity, and layering width) with our experimental values. This theoretical work offers applications in various fields, including hydrogel patterning for 3D cell culture, organ-on-a-chip models, at-home sample preparation, and autonomous microfluidic systems for biosensing.

Electrowetting is an electrokinetic effect whereby an applied electric field induces changes in the measured contact angle at a fluid-surface contact line. On hydrophobic, dielectric electrode surfaces, this effect generates droplet motion termed "electrowetting on dielectric" or EWOD. Applications of this phenomenon range from lab-on-a-chip to liquid lenses capable of altering their topology and focus within milliseconds. Electrowetting or EWOD theoretical models quantifying this effect fall into two paradigms: the Young-Lippman and the electromechanical theories. In this work, both paradigms were simulated to predict the velocity of a water droplet moving over an array of electrodes. Results were compared to experimental observations of measured velocities for two dielectric films: ETFE and household cling film. Theoretical model parameters, namely the length scale of the Maxwell force on the droplet, were also determined to align simulation and experiment. The results reveal the trend of droplet velocity in relation to applied voltage, and recapitulate the relationship between the two models.

We present a simple method to generate discrete aerodynamic gust under controlled laboratory condition in the form of a vortex ring which, unlike conventional methods of perturbation, is well studied and highly controllable. We characterized the flow properties of the vortex ring using flow visualization and novel light bead method. Reynolds number of the vortex ring, based on its average propagation velocity and nozzle exit diameter, was 16000. We demonstrate this method by studying the impact of head-on gust on freely flying soldier flies, Reynolds number of which, based on its wingtip velocity and mean wing chord, was 1100. We also present simple theoretical models to characterize the vortex ring based on generating conditions. The device can also be used to generate continuous gust in any direction and can be applied, in general, to study the gust response of natural fliers and swimmers, man-made micro aerial vehicles and aquatic plant lives.

The control and stability of flying and swimming animals is typically determined by measuring their responses to discrete gust perturbations. For the rigorous measurement and analysis of such responses, it is necessary to generate gusts that are precise, controllable and repeatable. Here, we present a method to generate discrete gusts under laboratory conditions using a vortex ring. Unlike other methods of gust generation, the vortex ring can be well characterized and is highly controllable. We first outline the theoretical basis for the design of a gust generator, and then describe an apparatus that we developed to generate discrete gusts. As a case study, we tested the efficacy of this method on freely-flying soldier flies Hermetia illucens. The method described here can be used to study diverse phenomena ranging from natural flight and swimming in insects, birds, bats and fishes, to the artificial flight of drones and micro-aerial vehicles.