Defocus particle tracking (DPT) has gained increasing importance for its use to determine particle trajectories in all three dimensions with a single-camera system, as typical for a standard microscope, the workhorse of today’s ongoing biomedical revolution. DPT methods derive the depth coordinates of particle images from the different defocusing patterns that they show when observed in a volume much larger than the respective depth of field. Therefore it has become common for state-of-the-art methods to apply image recognition techniques. Two of the most commonly and widely used DPT approaches are the application of (astigmatism) particle image model functions (MF methods) and the normalized cross-correlations between measured particle images and reference templates (CC methods). Though still young in the field, the use of neural networks (NN methods) is expected to play a significant role in future and more complex defocus tracking applications. To assess the different strengths of such defocus tracking approaches, we present in this work a general and objective assessment of their performances when applied to synthetic and experimental images of different degrees of astigmatism, noise levels, and particle image overlapping. We show that MF methods work very well in low-concentration cases, while CC methods are more robust and provide better performance in cases of larger particle concentration and thus stronger particle image overlap. The tested NN methods generally showed the lowest performance, however, in comparison to the MF and CC methods, they are yet in an early stage and have still great potential to develop within the field of DPT.

By integrating surface acoustic waves (SAW) into microfluidic devices, microparticle systems can be fractionated precisely in flexible and easily scalable Lab-on-a-Chip platforms. The widely adopted driving mechanism behind this principle is the acoustic radiation force, which depends on the size and acoustic properties of the suspended particles. Superimposed fluid motion caused by the acoustic streaming effect can further manipulate particle trajectories and might have a negative influence on the fractionation result. A characterization of the crucial parameters that affect the pattern and scaling of the acoustically induced flow is thus essential for the design of acoustofluidic separation systems. For the first time, the fluid flow induced by pseudo-standing acoustic wave fields with a wavelength much smaller than the width of the confined microchannel is experimentally revealed in detail, using quantitative three-dimensional measurements of all three velocity components (3D3C). In Part I of this study, we focus on the fluid flow close to the center of the surface acoustic wave field, while in Part II the outer regions with strong acoustic gradients are investigated. By systematic variations of the SAW-wavelength λ_SAW and channel height H, a transition from vortex pairs extending over the entire channel width W to periodic flows resembling the pseudo-standing wave field is revealed. An adaptation of the electrical power, however, only affects the velocity scaling. Based on the experimental data, a validated numerical model was developed in which critical material parameters and boundary conditions were systematically adjusted. Considering a Navier slip length at the substrate-fluid interface, the simulations provide a strong agreement with the measured velocity data over a large frequency range and enable an energetic consideration of the first and second-order fields. Based on the results of this study, critical parameters were identified for the particle size as well as for channel height and width. Progress for the research on SAW-based separation systems is obtained not only by these findings but also by providing all experimental velocity data to allow for further developments on other sites.

Turbulent superstructures in horizontally extended three-dimensional Rayleigh–Bénard convection flows are investigated in controlled laboratory experiments in water at Prandtl number ${Pr}=7$ . A Rayleigh–Bénard cell with square cross-section, aspect ratio $\varGamma =l/h=25$ , side length $l$ and height $h$ is used. Three different Rayleigh numbers in the range $10^{5} < {Ra} < 10^{6}$ are considered. The cell is accessible optically, such that thermochromic liquid crystals can be seeded as tracer particles to monitor simultaneously temperature and velocity fields in a large section of the horizontal mid-plane for long time periods of up to 6 h, corresponding to approximately $10^{4}$ convective free-fall time units. The joint application of stereoscopic particle image velocimetry and thermometry opens the possibility to assess the local convective heat flux fields in the bulk of the convection cell and thus to analyse the characteristic large-scale transport patterns in the flow. A direct comparison with existing direct numerical simulation data in the same parameter range of $Pr$ , ${Ra}$ and $\varGamma$ reveals the same superstructure patterns and global turbulent heat transfer scaling ${Nu}({Ra})$ . Slight quantitative differences can be traced back to violations of the isothermal boundary condition at the extended water-cooled glass plate at the top. The characteristic scales of the patterns fall into the same size range, but are systematically larger. It is confirmed experimentally that the superstructure patterns are an important backbone of the heat transfer. The present experiments enable, furthermore, the study of the gradual evolution of the large-scale patterns in time, which is challenging in simulations of large-aspect-ratio turbulent convection.

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Spin-hydrodynamic coupling is a recently discovered method to directly generate electricity from an electrically conducting fluid flow in the absence of Lorentz forces. This method relies on a collective coupling of electron spins—the internal quantum-mechanical angular momentum of the electrons—with the local vorticity of a fluid flow. In this work, we experimentally investigate the spin-hydrodynamic coupling in circular- and noncircular-capillary pipe flows and extend a previously obtained range of Reynolds numbers to smaller and larger values, $20<\mathrm{Re}<21500$, using the conducting liquid-metal alloy ($\mathrm{Ga}$,$\mathrm{In}$)$\mathrm{Sn}$ as the working liquid. In particular, we provide experimental evidence for the linear dependence of the generated electric voltage with respect to the bulk-flow velocity in the laminar regime of the circular pipe flow as predicted by Matsuo et al. [Phys. Rev. B. 96, 020401 (2017)]. Moreover, we show analytically that this behavior is universal in the laminar regime regardless of the cross-sectional shape of the pipe. Finally, the proposed scaling law by Takahashi et al. [Nat. Phys. 12, 52 (2016)] for the generated voltage in turbulent circular pipe flows is experimentally evaluated at Reynolds numbers higher than in previous studies. Our results verify the reliability of the proposed scaling law for Reynolds numbers up to $\mathrm{Re}=21500$ for which the flow is in a fully developed turbulent state.

High-resolution in situ measurements provide deep insights into acoustophoresis induced by surface acoustic waves, revealing the correlation between acoustic fields and 3D particle positions, energy conversion processes and localized heating effects.