Experiments in Fluids最新文献

Self-aligned focusing schlieren (SAFS) provides depth-selective visualization of compressible flows while avoiding the alignment complexity of conventional focusing schlieren systems. However, its practical implementation still requires reliable methods for selecting and positioning the optical components. This work introduces HORUS, a predictive tool developed to support the design and optimization of single- and double-lens SAFS systems. The study had three objectives: to develop practical expertise in applying SAFS to compressible flow facilities, to establish and validate HORUS, and to assess the performance of SAFS in representative flow environments. Two experimental campaigns were conducted at the von Kármán Institute for Fluid Dynamics. Tests on a supersonic ogive showed close agreement between the predicted and measured optical performance. A second campaign on a linear transonic turbine cascade confirmed the ability of HORUS to predict the field of view, depth of field, vignetting issues, and cutoff behavior. The experiments also validated a revised cutoff formulation for configurations in which the Rochon prism and Ronchi ruling are located on opposite sides of the field lens. The agreement between predictions and measurements demonstrates that HORUS provides a reliable framework for the design of SAFS systems and supports their application to complex compressible flow experiments.

Particle image velocimetry (PIV) is a powerful diagnostic tool for analyzing the properties and structures of laminar and turbulent flows. Modern PIV systems provide high spatial and temporal resolution measurements, often generating hundreds of gigabytes of raw data. Before these data can be used for flow analysis, they must be post-processed to identify and remove erroneous velocity vectors. Post-processing techniques typically require substantial manual intervention by highly trained analysts, making the process tedious, time-consuming, and inherently subjective. To address these challenges, we developed a physics-aware, data-driven framework for automated PIV post-processing. Our machine learning-based tool, PIVPostProcess, employs an ensemble of gradient-boosted decision trees trained on real experimental PIV datasets and is validated using direct numerical simulation data with synthetic noise. The fully trained model achieves excellent performance, with an F1 score—a performance metric that combines precision and recall—of 0.991 on the testing dataset. To evaluate generalization beyond the training distribution, we apply the model to six additional experimental datasets featuring previously unseen flow fields. The model achieves a mean F1 score of 0.990 across tested experimental datasets, demonstrating robust predictive capability. Importantly, this automated technique processes each dataset significantly faster compared to manual post-processing. By substantially reducing processing time while maintaining high accuracy, this tool accelerates and standardizes PIV vector validation approaches.

The objective of this study is to investigate the spatiotemporal multiscale characteristics of cavitating flows around a NACA66 hydrofoil using a complementary experimental approach combining high-speed imaging and digital in-line holography (DIH). Systematic measurements across inflow velocities from 6.0 to 9.5 m/s reveal a sequential transition through inception, sheet, and cloud cavitation regimes. Macroscale observations demonstrate that the spatial cavity extent and the temporal shedding frequency are intrinsically coupled, predominantly associated with the propagation dynamics and characteristic time scale of the re-entrant flow. At the mesoscale, the spatiotemporal evolution of discrete microbubbles is deeply modulated by macroscale dynamics. Temporally, the microbubble number density transitions from intermittent fluctuations to quasi-periodic oscillations as cavitation develops. Spatially, the bubble distribution evolves from rapid streamwise attenuation during inception into a pattern of localized discontinuities in the sheet regime, reflecting intense concurrent coalescence and fragmentation. In the fully developed cloud regime, the violent disintegration of detached cavities drives a continuous streamwise accumulation of microbubbles. Furthermore, the analysis of the Sauter mean diameter (d₃₂) reveals a critical spatial transition around U = 7.5 m/s. This transition signifies a shift from a fragmentation-dominated flow to a synergistic statistical mechanism involving coalescence potentially enhanced by vortex-driven bubble clustering and the selective extinction of fine microbubbles. Crucially, the probability density functions (PDF) of microbubble sizes exhibit a distinct “double power-law” spectrum, yielding a -10/3 scaling for super-Hinze bubbles and a -4/3 scaling for sub-Hinze fine microbubbles. The Hinze scale (d_H) is further identified as a dynamic indicator of local turbulent intensity, shifting significantly from 80–110 μm in the shedding zone down to 55–80 μm in the highly turbulent wake. These findings provide detailed quantitative characterization of dispersed microbubbles under varying turbulence conditions and verify the feasibility of digital in-line holography in resolving multiscale hydrodynamic features.

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Reconstruction of large-scale atmospheric boundary layer (ABL) dynamics from sparse measurements is essential for interpreting wind-tunnel experiments and for data-driven modelling of complex flows. Using a time-resolved experimental campaign providing three-component velocity fields on a transverse-vertical (YZ) plane from stereoscopic particle image velocimetry (SPIV), together with sparse hot-wire anemometry (HWA) probe signals, we assess an autoencoder (AE)-defined large-scale representation and benchmark it against a spectral proper orthogonal decomposition (SPOD)-based representation. Building on linear stochastic estimation (LSE) and convolutional neural networks (CNNs), including a temporal CNN (TCN) variant, four approaches (SPOD-LSE, SPOD-CNN, AE-CNN, and AE-TCN) are evaluated to identify the most accurate prediction of large scales from YZ-plane centreline measurements and probes. AE-TCN achieves the best overall composite performance and provides the strongest centroid tracking. Physical-consistency checks are then used to assess whether the reconstructed fields retain coherent-structure organisation and nonlinear interactions with the small scales.

This paper presents an extensive study of the air-blast atomization cascade from liquid core to ligaments and droplets. Microscopic backlit images of the sprays were captured along the jet centerline at various axial locations using two orthogonal high-speed cameras. A comprehensive imaging and image processing methodology was performed to enable direct measurements of the wavelength and amplitude of ligament instabilities forming on individual ligaments ejected from the primary liquid jet. This has enabled a complete reconstruction of the link between the probability distribution of the wavelength of ligament instabilities and the subsequent formation of droplets. Statistical analysis revealed the dependence of ligament instabilities on inlet conditions while enabling quantification of the full cascade of processes from the formation of liquid jet as a function of inlet conditions to the subsequent formation of ligaments and then, finally, to droplets.

Liquid foams have demonstrated a strong capability to reduce the impact of pressure disturbances, including acoustic, sustained shock, and blast waves. However, the wide range of experimental methodologies reported in the literature often prevents direct comparison of results, largely due to incomplete characterisation of foam properties, notably bubble size. In this study, experiments were conducted using a dedicated set-up enabling a precise control of critical parameters, including bubble size (160–450 (upmu )m), liquid fraction (2–8%), Mach number (1.15–1.45), and the incident waveform (either a sustained shock or a blast wave). The influence of each wave and foam parameter on the attenuation of the pressure signal was examined independently. The resulting dataset provides a robust experimental foundation for the design and performance assessment of protective systems against blast loading based on liquid foams, such as foam barriers.

Early-stage condensation droplets set initial conditions for subsequent growth, coalescence, and surface re-exposure, yet their time-resolved three-dimensional morphology at the microscale remains difficult to measure using conventional two-dimensional imaging techniques. Here, digital holographic microscopy (DHM) was used to reconstruct time-resolved height fields of an individual condensing microdroplet on a silane-coated glass surface, with lateral and temporal resolutions of 0.83 ({upmu })m and 66 ms, respectively. The reconstructed phase fields were converted into droplet height maps, from which maximum height, footprint-equivalent diameter, integrated volume, and projected-boundary descriptors were quantified. For the representative droplet analyzed in this study, early growth departed from uniform spherical-cap-like enlargement. Vertical growth was more pronounced than lateral footprint expansion during an early growth interval, while projected-boundary analysis showed that the initially less regular footprint became more circular as growth proceeded. At three selected growth stages, threshold-aligned spherical-cap cross-estimates underestimated the DHM-measured maximum height by 10.47–17.29% and the projected-footprint area by 9.00–27.74%, while overestimating the integrated volume by 9.84–28.53%. These observations indicate that the early lateral development of the analyzed droplet did not occur through uniform self-similar expansion, but through non-uniform morphological evolution involving coupled height growth, footprint development, volume accumulation, and boundary regularization. Together, the coupled temporal measurements and direct geometric comparison demonstrate that DHM can quantify non-uniform three-dimensional morphological evolution and assess the limitations of simplified droplet geometries during early-stage condensation.

Electrohydrodynamic (EHD) fans represent a viable solid-state alternative to conventional thermal management, addressing the demand for miniaturized, silent, and vibration-free cooling in modern electronics. While Particle Image Velocimetry (PIV) is widely utilized for aerodynamic characterization, standard PIV assumptions break down under EHD flows. Classical tracer fluid coupling assumptions are invalidated due to the complex interactions between tracer particles, the induced electric field, and ambient conditions. This study experimentally quantifies the coupled effects of relative humidity (36%, 50%, and 80%) and PIV seeding materials (DEHS droplets and incense smoke) on the operational characteristics of a needle-to-ring EHD fan (22 mm diameter, 4–14 kV operating range). Experiments were conducted in a controlled environmental chamber to evaluate discharge current stability, current–voltage relationships, and the resulting flow fields. The findings confirm that while the linear corona discharge regime (Townsend relationship) is preserved across all humidity levels, increasing humidity suppresses the discharge current. Notably, higher humidity conditions yield higher outlet velocities, a trend attributed to condensation-enhanced local field effects near breakdown. More importantly, the choice of seeding material emerges as a primary source of measurement bias. Dielectric DEHS droplets suppress ionization and distort the flow topology, inducing a 36% reduction in peak axial velocity and a 17% underestimation of the volumetric flow rate at 14 kV. In contrast, electrically compatible incense smoke preserves the high-velocity ionic jet core. These findings establish that seeding selection and environmental conditions are active variables governing EHD flow behavior, with direct implications for diagnostic methodology and the deployment of ionic wind devices.