As insects flap their wings, they generate complex wake structures critical to their aerodynamic force production. Specific flow structures such as the leading-edge vortex have been studied for decades; however, a complete understanding of the transient dynamics and energy exchange mechanisms in insect wakes remains elusive. To help bridge this gap, we employ data-driven reduced-order modelling techniques to identify a simple and interpretable model for a hovering hawkmoth’s wake. We begin by using an in-house immersed-boundary-method computational fluid dynamics solver to simulate hovering hawkmoth flight. We then perform dynamic mode decomposition to distil the resulting flow field into a set of time-varying modes. Finally, we employ sparse regression to identify a model capturing the driving modes’ temporal evolution, ranging from quiescent flow to periodic steady state. Notably, the model takes the form of a Stuart–Landau oscillator with higher-order nonlinear terms. The presence of a limit-cycle dynamics suggests a balance between energy input from wing motion and energy lost due to advective energy transfer and viscous dissipation. Using an impulse-based wake survey method, we show that this model provides an accurate estimation (mean absolute error within 3.5 % of body weight) of the hawkmoth’s long-term lift production. These findings highlight the significance of stability and energy transfer in flapping-flight aerodynamics, offering a framework for future studies of biological flight systems. Furthermore, by linking the wake dynamics to simple dynamic equations, this work provides inspiration for the design and control of bio-inspired micro-aerial vehicles.

The results of an experimental investigation of the entry of steel spheres into liquid water through either a free surface or a thin Mylar film at a range of velocities from 1740–2314 m/s are presented. Particular attention is given to characterizing the flow features. The entry velocity is observed to affect the sphere deceleration, the shock wave, and cavity formation. With increasing entry velocity, the post-impact deceleration increases due to the effect of compressible drag, and the cavity grows more quickly. The entry material is observed to only weakly influence the sphere deceleration and has a negligible effect on the shock wave and cavity formation. This is possibly due to the thin Mylar film reducing the kinetic energy of the sphere upon impact or causing a flattening of the front of the sphere, leading to increased drag especially at higher entry speeds. The splash occurs in four distinct phases: a fast-moving cloud of atomized spray, a pause, a slower bulk liquid ejection, and finally a faster liquid flow. With increasing impact velocity, the pause phase is shorter in duration, while the slow ejection phase is longer. The effect of adding a thin plastic film over the entry region lengthened the duration of the bulk ejection phase by 28

This experimental investigation focuses on understanding the influence of perturbations due to short-duration energy deposition on the shock train structure and flow dynamics in an axisymmetric over-expanded Mach 2.52 jet. The flow is perturbed by localized laser-induced breakdown at various locations within the jet, creating a shock wave and a high-temperature plasma zone in the shock train. A high-speed self-aligning focusing schlieren system is used to visualize the flow and characterize the shock train dynamics and the flow structure recovery process by measuring the distance to the first shock reflection point from the nozzle exit. The response of the jet flow is similar for cases with the perturbation at the nozzle exit and the pre-reflection point across a range of jet total pressures, but the response is qualitatively different when the perturbation occurs downstream of the first shock reflection in the jet, with the flow structures being forced upstream toward the nozzle. The frequency of the oscillations of the shock height is found to be the same for all cases, approximately 10 kHz, independent of the jet total pressure, laser energy, and deposition location. The oscillations reduce in magnitude over time, and the damping ratio for cases with the energy deposition at the pre-reflection point and nozzle exit is found to be nearly constant with respect to jet total pressure and deposition energy, varying within the range of 0.06–0.12, whereas it is dependent on the jet chamber pressure for the post-reflection case, varying from 0.07 to 0.14.

Optical flow velocimetry (OFV) is a method for determining dense and accurate velocity fields from a pair of particle images by solving the classical optical flow problem. However, this is an ill-posed inverse problem, which generally entails minimizing a weighted sum of two terms–fidelity and regularization–and the weights in the sum are parameters that require manual tuning based on the properties of both the flow and the particle images. This manual tuning has historically been a consistent challenge that has limited the general applicability of OFV for experimental data, as the calculated velocity field is sensitive to the value of the weights. This work proposes a hierarchical model for the weighting parameters in the framework of a maximum a posteriori-based Bayesian optimization approach. The method replaces the classical Lagrange multiplier weighting parameter with a new, less-sensitive parameter that can be automatically predetermined from experimental images. The resulting method is tested on three different synthetic particle image velocimetry (PIV) datasets and on experimental particle images. The method is found to be capable of self-adjusting the local weights of the optimization process in real-time while simultaneously determining the velocity field, leading to an optimally regularized estimate of the velocity field without requiring any dataset-specific manual tuning of the parameters. The presented approach is the first truly general, parameter-free optical flow method for particle image velocimetry (PIV) images. The developed method is freely available as a part of the PIVlab package.

OBJECTIVES:The risk of viral infection to healthcare workers during aerosol-generating procedures (AGPs) is an area of significant concern within the medical community. In this work, we analyze the flow patterns created by jet ventilation, a type of AGP used in laryngeal surgery, both qualitatively and quantitatively to assess the effects associated with commonly used jet ventilation settings. METHODS:We apply background oriented schlieren (BOS) imaging to visualize and analyze the aerosol-laden plume. Simultaneous direct measurements of aerosol content were performed using an optical particle sizer (OPS). Twelve configurations for jet ventilation were studied, varying the jet frequency, placement within the airway, and level of suction, and the aerosol contained in the plume was quantitatively evaluated. RESULTS:Low-frequency supraglottic ventilation exhibits the lowest total signal, approximately 50% lower than the high-frequency cases and less than 10% of the low-frequency infraglottic case. The highest signal magnitude was seen in low-frequency infraglottic ventilation, 20% higher than the next highest signal with no suction. The use of suction was observed to reduce the signal by 40%-100%, with a stronger effect in the SG configuration. CONCLUSION:Both BOS and OPS measurements reveal that a supraglottic arrangement with a pulse frequency of 0.2 Hz produces the lowest amount of plume extent and aerosol content, and that the use of suction considerably reduces the amount of aerosol content present in the plume. Qualitative agreement was observed between the BOS data and the OPS in terms of the relative signal between configurations, but not quantitatively. LEVEL OF EVIDENCE:N/A.

Particle image velocimetry (PIV) is an established velocimetry technique in experimental fluid mechanics that involves determining a fluid flow velocity field from the motion of tracer particles illuminated by a laser sheet. The necessity of laser illumination poses challenges in certain applications and is a potential entry barrier due to its high cost and safety considerations. A laser-free alternative to PIV is particle shadow velocimetry (PSV), which uses images of the shadows cast by the particles on the camera sensor under back-illumination, instead of the Mie scattering signal produced by laser illumination. This study aims to compare various aspects of PSV such as depth of field, seeding density, type of illumination required, particle size, image filtering, cost-effectiveness and limitations with those of PIV. PSV and PIV measurements are taken in the wake of a flow past a cylinder and in a boundary layer developing over a flat plate. It is found that PSV is capable of achieving equivalent accuracy to PIV and is a viable alternative to PIV in certain applications where light sheet illumination creates experimental challenges.

Particle image velocimetry is a standard non-intrusive experimental fluid mechanics technique that is used for determining fluid flow velocities quantitatively. The motion requires inferring the fluid motion from the motion of the seeded particles in a flow field. One of the methods to determine the motion of particles is optical flow, however, the available optical flow velocimetry algorithms contain user-tuned parameters leading to limited applicability on experimental data. An optical flow algorithm for fluid flow velocimetry with automatic parameter adjustment is developed and implemented as a processing module in an open-source GUI-based PIV package PIVlab.

The ability of PIV processing algorithms to accurately determine velocity vectors across the range of motion present in PIV images is characterized by the algorithm’s dynamic velocity range (DVR). Conventionally, the DVR of PIV is defined using the ratio between the maximum and minimum resolvable particle displacements, with the minimum based on the uncertainty in the location of a single particle in the optical system. In this work, it is demonstrated that this definition is inadequate in practice, as it ignores many factors which affect the accuracy of an algorithm when determining small displacements, and the error in vectors with small magnitudes in actual flows is often many times larger than the theoretical minimum. A more useful criterion for determining the DVR of a PIV setup is proposed that depends on conditional errors, using synthetic data to produce a known ground truth. The introduced error-based DVR accounts for the effect of multiple flow velocity scales present in a PIV experiment as well as multi-particle effects. It is found that the practical, error-based DVR of cross-correlation-based PIV is highly experiment-dependent and much lower than the widely accepted value of 𝒪( 10^2) , typically 𝒪( 10^0) - ( 10^1) . The findings from the synthetic data results are corroborated using experimental PIV data to approximate the DVR via a deviation-based approach when the ground truth is unknown.

Stagnation-point injection experiments were performed using a 7 deg half-angle cone with a 19-mm-radius spherical nose and a single, 1.93-mm-radius sonic jet in the center of the model directed into a Mach 6 quiet and a Mach 5.8 noisy freestream. The primary data consist of high-speed schlieren imaging at 76 kHz. Spectral analyses of the schlieren data were performed resulting in the observation of three key modes fundamental to the motions of the flow structures: 1) the vortex-coupled mode, 2) vortex-shedding mode, and 3) the longitudinal mode. The proper orthogonal decomposition was used to track the mode energy fractions as a function of thrust coefficient, while the dynamic mode decomposition was used to gain insight into the nature of the motions present in each mode. The spectral proper orthogonal decomposition is used to probe specific frequencies present in the jet reservoir spectra. As the thrust coefficient increases, the dominant mode changes from the vortex-coupled mode (f=27.2 kHz) to the longitudinal mode (f less than or similar to 10 kHz). The vortex-shedding mode is shown to be strongly correlated at f=31.2 kHz, but similar, weaker modes exist between 15 and 20 kHz.

Abstract Optical flow methods have been developed over the past two decades for application to particle image velocimetry (PIV) images with the goal of acquiring higher resolution measurements of the velocity field than conventional cross-correlation (CC)-based techniques. Numerous optical flow velocimetry (OFV) algorithms have been devised to solve the ill-posed optical flow problem, with various physics-inspired strategies to tailor them to fluid flows. While OFV can be applied to continuous scalar fields, it has demonstrated the most success on images of tracer particles, i.e. traditional planar PIV images. Compared to state-of-the-art CC algorithms, OFV methods have demonstrated an order of magnitude increase in spatial resolution and up to a factor of two improvement in overall accuracy when evaluated on synthetic data, at the cost of increased computational time. The requirements for particle seeding density, inter-frame displacement, and image quality are also more stringent for OFV methods compared to CC. OFV has been applied sparingly in experiments to date, but appears to offer the same advantages demonstrated on synthetic data. At this stage, OFV seems best suited to planar velocity measurements, although extensions to stereoscopic measurements have been demonstrated.

Since its introduction in the year 2000, background-oriented schlieren (BOS) has become a cornerstone technique for visualizing variable-density flows. In this review, we provide a rigorous examination of the optical principles underpinning BOS and related refractive-index-based techniques, complemented by an appendix linking schlieren imaging to Maxwell's equations. The core sections delve into the practical aspects of BOS, with detailed discussions on image processing algorithms and critical considerations for experimental setups. We then explore recent advancements and innovations, including extensions of BOS with tomography, data assimilation, and event-based imaging. Finally, we present notable applications of BOS in challenging and unconventional environments, showcasing the method's versatility, and offer inspiration for future research directions.

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

While optical flow velocimetry (OFV) methods have demonstrated superior accuracy and resolution compared to conventional cross-correlation algorithms for obtaining accurate fluid flow velocity measurements from tracer particle images, substantial room for improvement remains. One of the challenges is the difficulty of incorporating fluid flow physics in the data term present in the variational formulation of the optical flow problem. To address this issue, we solve the optical flow problem in a domain constructed from a fluid flow-inspired quasi-optimal basis that imposes an implicit constraint on the solution. It was shown in previous work that the bases obtained are capable of efficiently representing a wide variety of fluid flow fields with minimal accuracy loss, as the basis elements are ordered so that the main features of the flow are captured by the first few elements of the basis, allowing the dimensionality of the problem to be reduced to help resolve the inherent ill-posedness of the optical flow problem. To further impose physicality on the obtained velocity fields, a viscosity-inspired explicit regularization strategy is implemented in the method in the quasi-optimal basis domain. The method is applied to synthetic as well as experimental PIV data to test its capabilities and is compared to correlation-based PIV.