Experiments in Fluids最新文献

A robust pairing algorithm with outlier removal is introduced in the context of two-pulse 3D particle tracking velocimetry at high seeding densities, with high concentrations of ghost particles. Integrating the vector field consensus approach from Ma et al. (IEEE Trans Image Process 23:1706–1721, 2014), the algorithm, its underlying hypotheses, and its relevant input parameters are investigated in the context of turbulent flow measurements. 2D synthetic tests are first carried out to quantify the algorithm’s performance and derive simple guidelines for optimal parameter tuning strategies based on experimental quantities. It is found that 2D vector fields with up to 90% outliers can be handled by our algorithm. 3D synthetic tests are then implemented to test the tracking strategy robustness to increasing image densities and ghost particle concentrations. We show that our algorithm can be used for particle pairing in particle clouds with up to 50% of ghost particles. Results submitted on the two-pulse dataset of the first LPT challenge, using the associated data portal with automatic evaluation, also showcase the overall excellent performances of the method. Finally, the method is used successfully on experimental data from our Giant Von Kármán setup (characterized by up to 65% of ghost particles), as evidenced by comparisons of its output with respect to results provided by the Shake-The-Box algorithm and with results provided by a pairing approach using a 3D cross-correlation predictor.

Wall shear stress ((tau _w)) quantification is fundamental in fluid dynamics but remains challenging in wind-tunnel experiments. Sensor-based methods offer high accuracy but lack spatial resolution for capturing complex three-dimensional effects. Conversely, oil-film visualization is a simple method to obtain high-resolution surface flow topology by processing a sequence of images using optical flow (OF) techniques. However, leveraging this approach for quantitative analysis suffers from noise and systematic biases. This study introduces SENSE (Sensor-Enhanced Neural Shear Stress Estimation), a data-driven approach that leverages a neural network to enhance OF-based (tau _w) estimation through the integration of sparse, high-fidelity sensor measurements via a multi-objective loss function. SENSE processes oil-film image sequences directly, inherently mitigating temporal noise without explicit averaging. The method is validated in a turbulent separated flow on a one-sided diffuser. Results demonstrate SENSE’s robustness to sequence length and spatial resolution compared to classical optical flow algorithms. Crucially, incorporating sparse sensor data significantly improves quantitative accuracy, achieving over 30% reduction in root-mean-squared error on validation sensors with only 8 strategically distributed sensors. The sensor data provide a global regularization effect, improving estimates far from sensor locations. SENSE offers a promising approach to elevate oil-film visualization to a reliable quantitative measurement technique by combining image sequences and sparse sensor data.

Flows in rapidly spinning bodies, such as the iconic libration-induced flow, are key ingredients of the dynamics of stars and planetary interiors. Laboratory experiments of such flows experience a strong centrifugal acceleration, which hinders the use of classical velocimetry methods relying on particle tracking. Modal acoustic velocimetry was introduced by Triana et al. (New J Phys 16(11):113005, 2014) as a new particle-free method, inspired from helioseismology, to alleviate this problem. In this method, acoustic modes are excited in the fluid and recorded in the spinning container. Rotation and fluid flow modify the characteristics of these modes, lifting the degeneracy of non-axisymmetric modes. To date, this method has only been applied to stationary or statistically stationary flows, by measuring frequency splittings in the spectral domain. Here, we analyze time-varying libration-induced flows. We propose and test two data acquisition strategies. The first strategy operates in the frequency domain and relies on the periodicity of the flow, while the second strategy involves a high-resolution algorithm applied in the time domain. The retrieved mode frequency splittings are compared to those computed for a classical linear libration-induced flow model as reported (Greenspan The theory of rotating fluids, Cambridge University Press, Cambridge, 1968). A very good agreement is obtained, but we observe an unexpected time delay, which we attribute to the buildup time of acoustic modes. We retrieve more than 50 splitting measurements at 10 successive libration phases. Inverting these data with the SOLA method, often used in helioseismology, we derive profiles (1D inversion) and maps (2D inversion) of the azimuthally averaged fluid rotation rate. The inversions recover the main characteristics of this time-dependent flow. The 2D inversion confirms the invariance of the flow along the rotation axis. Resolution kernels show that flow can be mapped on patches that spread over approximately (5 %) of a meridian quarter-plane. Our study paves the way to the investigation of more exotic regimes of precession- or libration-induced flows.

The tailless flying wing configuration represents a typical aerodynamic layout for next-generation aircraft. Rudderless flight control technology can significantly enhance the high-stealth performance and payload capability of flying wing aircraft, making it a disruptive technology that has gained widespread attention and is being gradually applied in advanced air vehicles. The implementation of this technology holds considerable strategic value and engineering significance. This review summarizes recent advances and flight verifications in three core active flow control technologies involved in rudderless flight control of flying wing aircraft: circulation control, flow separation control, and separation induction control. The current technology status and challenges are discussed, and an outlook on future development trends is provided.

We present a newly constructed jet array with a novel driving scheme for turbulence generation in a vertical water tunnel and measurements of the turbulent flow this jet array establishes. The design of the array allows us to control the mean background flow and the turbulence intensity independently of each other. The array consists of a rectangular arrangement of 112 individually computer-controlled water jets that are aligned streamwise to the measurement section of our 8-m tall vertically recirculating water tunnel. Using solenoid valves, individual jets are activated following predefined protocols that can be tailored to obtain different turbulence statistics within the measurement section. The protocols are based on four-dimensional OpenSimplex noise, a type of gradient noise that features spatial and temporal coherence. Details of the mechanical and electrical designs are presented, together with a detailed description of the protocol generation. We show that the resulting turbulence is near homogeneous and isotropic, with a turbulence intensity of Order 1, an energy dissipation rate of order (10^{-1},mathrm {m^2/s^3}) and (textrm{Re}_{lambda }approx 1400). Additionally, we present experiments that show the effects that various system and protocol parameters have on the created flow conditions and address the streamwise development, as well as the homogeneity and isotropy of the flow.

We investigate fluid instabilities in a Hele–Shaw cell driven by the combined effects of double diffusion (DD) and Rayleigh–Taylor (RT) mechanisms, focusing on the formation of vertical fingers and overturning plumes. These patterns emerge from the interplay between sediment settling and the diffusion of a scalar (dextrose). A novel experimental design is introduced in which particle sizes are selected so that their settling velocities are slower than, comparable to, or faster than the solute diffusion rate. This systematic variation allows the relative influence of settling and diffusion to be isolated and quantified. Two primary questions are addressed. First, under what conditions do fingering instabilities arise, as opposed to pure particle settling? We find that fingering is suppressed when the particle settling velocity exceeds the DD finger tip velocity. A predictive criterion is developed based on a dimensionless gravity parameter and the ratio of characteristic settling to diffusion times. Second, when instabilities are present, which mechanism—DD or RT—dominates? As expected, DD dominates when diffusion is rapid. A transition from DD to RT is observed when the settling-to-diffusion time ratio falls below approximately 0.2, using the gap width as the characteristic length scale. This work introduces a straightforward framework for exploring competing instability mechanisms in sediment-laden, diffusive flows in an experimentally accessible, previously uncharacterized parameter space.

Predicting droplet evaporation is particularly complex when the liquid phase consists of multiple components. To date, only a limited number of physical optical phenomena have been used to non-intrusively measure the composition of droplets. Laser-induced fluorescence is a promising approach, as the emission and absorption of certain fluorescent dyes are known to depend on solvent polarity, viscosity, and, more generally, the chemical environment. However, a challenge is that fluorescence signal intensity is generally sensitive to both temperature and composition. This study investigates fluorescence lifetime measurements as a robust alternative. We demonstrate that, with a well-chosen fluorescent dye, it is possible to measure the composition of bicomponent droplets using a single dye and a single detection band, with minimal constraint on detection band selection, and without ambiguity due to temperature variations. To validate the technique, it is applied to acoustically levitated droplets across several mixtures that exhibit markedly different behaviors.

The hydrodynamic influence of beveled edges on air–water flow properties in a stepped chute were studied. Air–water flow measurements were made with a double tip phase-detection conductivity probe and an ultrasonic sensor for unit discharges up to 0.565 m²/s in a beveled stepped chute for two interchangeable step heights of 0.1 m and 0.2 m. Flow regimes, the onset of aeration, and the streamwise development of air concentrations, interfacial velocities, and free-surface fluctuations were quantified. Bubble count rates, chord lengths, and their distributions were also derived from measurements with a discussion of the flow physics. A direct comparison of air–water flow properties with vertical steps revealed that bevels elongated and reduced the stability of recirculating cavities, directly influencing flow regimes and reducing the distance to the air-entrainment inception point by 20–30%. At the chute exit, beveled steps produced higher mean air concentrations, greater flow depths and reduced interfacial velocities. These results highlight the value of detailed air–water flow measurements to quantify flow properties and processes that may be used in engineering applications.

Tomographic reconstruction, a critical process for tomographic particle image velocimetry (Tomo-PIV), remains inefficient due to the required massive memories and high computational cost. In this work, a fast tomographic reconstruction technique is proposed to improve the efficiency significantly. The weighting coefficient, which represents the contribution of the voxel to the corresponding pixel intensity, is remodeled to be independent of the voxel’s positions by artificially improving the particle image resolution. Consequently, the simultaneous multiplicative algebraic reconstruction technique (SMART) is re-implemented with convolution operations. The proposed method is named Convolutional-SMART (Conv-SMART). Moreover, the numerous convolution operations are accelerated using a graphics processing unit (GPU) to further reduce the reconstruction time. A synthetic three-dimensional 3D experiment with a vortex ring is carried out to numerically evaluate the precision and efficiency of the proposed method. The results show that the speed-up ratio of Conv-SMART to the original SMART reaches about five times faster in the central processing unit (CPU) environment and 15 times faster in the GPU environment without losing accuracy when the particle density is 0.05 particles per pixel (ppp) and the resolution is 20 voxels/mm. The speed-up ratio as a function of the particle density and resolution is also provided. Conv-SMART is also applied to the left ventricular Tomo-PIV experiment. The velocity field derived from Conv-SMART is consistent with that from SMART, whereas Conv-SMART achieves 50 times faster within the GPU.