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Persistent mechanoluminescence (ML) with long lifetime is highly required to break the limits of the transient emitting behavior under mechanics stimuli. However, the existing materials with persistent ML are completely trap‐controlled, and a pre‐irradiation is required, which severely hinders the practical applications. In this work, a novel type of ML, self‐charging persistent ML, is created by compositing the Sr3Al2O5Cl2:Dy3+ (SAOCD) powders into flexible polydimethylsiloxane (PDMS) matrix. With no need for any pre‐irradiation, the as‐fabricated SAOCD/PDMS elastomer could exhibit intense and persistent ML under mechanics stimuli directly, which greatly facilitates its applications in mechanics lighting, displaying, imaging, and visualization. By investigating the matrix effects as well as the thermoluminescence, cathodoluminescence, and triboelectricity properties, the interfacial triboelectrification‐induced electron bombardment processes are demonstrated to be responsible for the self‐charged energy in  SAOCD under mechanics stimuli. Based on the unique self‐charging processes, the SAOCD/PDMS further exhibits mechanics storage and visualized reading activities, which brings novel ideas and approaches to deal with the mechanics‐related problems in the fields of mechanical engineering, bioengineering, and artificial intelligence. Herein, a novel type of mechanoluminescence (ML), self‐charging persistent ML, is reported by compositing the Sr3Al2O5Cl2:Dy3+ powders into flexible polydimethylsiloxane matrix. In addition to facilitating the applications in mechanics displaying and visualization, the unique self‐charging processes endow the materials with mechanics storage and visualized reading activities, showing broad application prospects in mechanical engineering, bioengineering, and artificial intelligence.

This RCT study aimed to investigate if VIS-Ed (Visualization through Imaging and Simulation - Education) had the potential to improve medical student education and specialist training in clinical diagnosis and treatment of trauma patients. The participants' general opinion was reported as high in both groups (lecture vs. virtual patient (VP)). Face validity of the VIS-Ed for cervical spine trauma was demonstrated and the VP group reported higher stimulation and engagement compared to the lecture group. No significant difference in the knowledge test between both groups could be observed, confirming our null hypothesis that VIS-Ed was on par with a lecture.

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. The results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

We present a combined numerical (particle vortex method) and experimental (soap film tunnel) study of a symmetric foil undergoing prescribed oscillations in a two-dimensional free stream. We explore pure pitching and pure heaving, and contrast these two generic types of kinematics. We compare measurements and simulations when the foil is forced with pitching oscillations, and we find a close correspondence between flow visualisations using thickness variations in the soap film and the numerically determined vortex structures. Numerically, we determine wake maps spanned by oscillation frequency and amplitude, and we find qualitatively similar maps for pitching and heaving. We determine the drag–thrust transition for both pitching and heaving numerically, and we discuss it in relation to changes in wake structure. For heaving with low oscillation frequency and high amplitude, we find that the drag–thrust transition occurs in a parameter region with wakes in which two vortex pairs are formed per oscillation period, in contrast to the common transition scenario in regions with inverted von Kármán wakes.

This study presents an experimental investigation on the internal flow of a ventilated supercavity using fog flow visualization and particle image velocimetry (PIV) measurements. The ventilated supercavity is generated on a backward-facing cavitator and studied in the high-speed water tunnel at St. Anthony Falls Laboratory. Fog particles are introduced into the supercavity through the ventilation line, and then illuminated by a laser sheet for flow visualizations and PIV measurements. The experiments are performed on the supercavities with two closure types, i.e. the re-entrant jet (RJ) and the twin vortex (TV), under the same water tunnel flow condition but different ventilation rates. The flow visualization revealed three distinct regions within the supercavity, including the ventilation influence region near the cavitator, the extended internal boundary layer along the liquid–gas interface and the reverse flow region occupying a large centre portion of the supercavity. The streamwise and vertical extent of the ventilation influence region, the streamwise growth of the internal boundary layer and the reverse flow within the supercavity are then quantified through PIV flow measurements. Compared to the RJ case, the results indicate that the TV supercavity yields a longer vertical extent of the ventilation influence region, a thinner internal boundary layer and a stronger reverse flow. The internal flow results suggest that at the upstream of the location of the maximum cavity diameter, the gas enters the forward flow (including the internal boundary layer and the forward moving portion of the ventilation influence region) from the reverse flow, while at the downstream of that location, the gas is stripped from the internal boundary layer and enters the reverse flow due to the increasing adverse pressure gradient in the streamwise direction. The above results are combined with visualization results of the supercavity geometry and closure patterns to further explain the influence of gas leakage mechanisms on cavity growth and closure transition. Specifically, visualization of the cavity geometry change during the RJ to TV supercavity transition indicates external flow separation associated with a critical incline angle of the bottom liquid–gas interface at the closure contributes to the onset of RJ closure. The closure visualization shows the coexistence of the toroidal vortex and twin-vortex tubes for the RJ supercavity leads to two gas leakage mechanisms: one associated with the shedding of toroidal vortices ( $Q_{RJ}$ ) and the other due to the gas entrained by the internal boundary layer and leaking from the twin-vortex tubes ( $Q_{TV}$ ). For the RJ supercavity, with increasing ventilation input, due to the reduction of  $Q_{RJ}$ , the supercavity needs to elongate to increase the gas entrained by the internal boundary layer (i.e.  $Q_{TV}$ ) to balance the ventilation increase. The elongation of the supercavity leads to reduced flow separation, and eventually a transition to the TV supercavity with ventilation above a critical value. For the TV supercavity, $Q_{RJ}$ is absent. An increase of ventilation input can be balanced by the increase of $Q_{TV}$ associated with the widening of the twin-vortex tubes, and therefore, no appreciable elongation of cavity length is observed.

A pioneering study conducted by Egbers and Rath [Acta Mech. 111 pp. 125–140 (1995)] experimentally captured spiral waves to elucidate the transition in the wide-gap spherical Couette flow. However, the physical field quantities of the spiral waves corresponding to light patterns of various intensities, as obtained in the experiment, remain unclear, and we have yet to move beyond the understanding that the reflected light from shear-sensitive flake tracers responds to a flow that appears at the transition. In this study, the experiment to visualize spiral waves using aluminum flakes, as performed by Egbers and Rath, was numerically reproduced by solving the translational and rotational motions of the particles in a spiral wave. First, the spiral wave in a spherical Couette flow with an aspect ratio η=1/2 was numerically calculated using the Newton–Raphson method. Subsequently, the image that was numerically reproduced from the spiral wave was compared with an experimentally visualized image. The torque acting on the inner sphere and the phase angular velocity of the spiral waves with various wavenumbers were provided. Attempts have been made to determine the instantaneous physical quantity that corresponds to the light and dark patterns observed in the flow visualization. From the attempts, we concluded the orientation motion of the flakes developed in the advective history of the flow is essential to yield these patterns. Exploring the correlation between flow visualization results and shear structures may provide a new avenue for quantitatively estimating spatial structures and time scales in complex and quickly time-varying flow fields, such as turbulence.

An artificial intelligence (AI) control system is developed to maximize the mixing rate of a turbulent jet. This system comprises of six independently operated unsteady minijet actuators, two hot-wire sensors placed in the jet and genetic programming for the unsupervised learning of a near-optimal control law. The ansatz of this law includes multi-frequency open-loop forcing, sensor feedback and nonlinear combinations thereof. Mixing performance is quantified by the decay rate of the centreline mean velocity of the jet. Intriguingly, the learning process of AI control discovers the classical forcings, i.e. axisymmetric, helical and flapping achievable from conventional control techniques, one by one in the order of increased performance, and finally converges to a hitherto unexplored forcing. Careful examination of the control landscape unveils typical control laws, generated in the learning process, and their evolutions. The best AI forcing produces a complex turbulent flow structure that is characterized by periodically generated mushroom structures, helical motion and an oscillating jet column, all enhancing the mixing rate and vastly outperforming others. Being never reported before, this flow structure is examined in various aspects, including the velocity spectra, mean and fluctuating velocity fields and their downstream evolution, and flow visualization images in three orthogonal planes, all compared with other classical flow structures. Along with the knowledge of the minijet-produced flow and its effect on the initial condition of the main jet, these aspects cast valuable insight into the physics behind the highly effective mixing of this newly found flow structure. The results point to the great potential of AI in conquering the vast opportunity space of control laws for many actuators and sensors and in optimizing turbulence.

The yield stress and shear thinning properties of mucus are identified as critical for ciliary coordination and mucus transport in human airways. We use here numerical simulations to explore the hydrodynamic coupling of cilia and mucus with these two properties using the Herschel–Bulkley model, in a lattice Boltzmann solver for the fluid flow. Three mucus flow regimes, i.e. a poorly organized regime, a swirly regime, and a fully unidirectional regime, are observed and analysed by parametric studies. We systematically investigate the effects of ciliary density, interaction length, Bingham number and flow index on the mucus flow regime formation. The underlying mechanism of the regime formation is analysed in detail by examining the variation of two physical quantities (polarization and integral length) and the evolution of the flow velocity, viscosity and shear-rate fields. Mucus viscosity is found to be the dominant parameter influencing the regime formation when enhancing the yield stress and shear thinning properties. The present model is able to reproduce the solid body rotation observed in experiments (Loiseau et al., Nat. Phys., vol. 16, 2020, pp. 1158–1164). A more precise prediction can be achieved by incorporating non-Newtonian properties into the modelling of mucus as proposed by Gsell et al. (Sci. Rep., vol. 10, 2020, 8405).

A passive flow control device, Clark-Y airfoil-shaped vortex generator (VG) on NREL Phase VI turbine blade, which has s809 airfoil section, is investigated. Both qualitative oil flow visualization from wind tunnel experiments and quantitative measures of aerodynamic coefficients using steady-state CFD with OpenFOAM are reported. Airfoil-shaped VGs are proposed and compared with traditional rectangular and triangular VGs. The use of airfoil-shaped VGs to delay separation, improving aerodynamic efficiency, inducing local pressure peaks and augmenting vorticity in the flow field are reported in detail. Results show that blades equipped with airfoil-shaped VGs provide a 5% lift coefficient increase and a 27.68% drag coefficient reduction compared to clean blades at a stall angle of α=11∘. Airfoil-shaped VGs also generate more vorticity downstream compared to conventional VGs, contributing to maximum increase in peak vorticity inducing an additional momentum to the flow to delay separation without significant drag penalty. Thus, airfoil-shaped VGs offer a promising alternative to traditional VG designs.

The Taylor–Couette flow of non-colloidal, neutrally buoyant spherical particle suspensions in the $\\phi =0\\,\\%-28\\,\\%$ concentration range and (17–250) Reynolds number ($\\mathcal {R})$ range is studied using synchronized flow visualization and torque measurements. Both methods are applied in ramp-up/down (acceleration/deceleration of the inner cylinder) experiments to detect the various flow structure states and bifurcation natures, their critical conditions and their lifetime in $\\mathcal {R}$ range. Torque measurement allows us to discuss the evolution of the (pseudo) Nusselt number, $\\mathcal {N}$, and friction coefficient with $\\mathcal {R}$ or alternatively the Taylor number, Ta. Flow visualization brings additional information on the unsteady dynamics of flow states. For concentrations higher than $\\phi =6\\,\\%$, two unsteady (spiral vortex flow, wavy vortex flow) and one steady (Taylor vortex flow) flow state are observed in both ramp-up/down experiments. Hysteretic behaviour is reported for some primary, secondary and tertiary bifurcations, which are thus found to be subcritical. A critical concentration is observed at $\\phi =15\\,\\%$ for the range of $\\mathcal {R}$ at which spiral vortex flow is encountered. Characteristic frequencies of unsteady flow state (spiral vortex flow, wavy vortex flow) for different suspension concentrations are evaluated. Finally, three hydrodynamic concentration subregimes are identified for the first time, with their distinct sets of concentration-dependent critical conditions, torque scaling exponents and friction coefficients.