Zinc-air flow batteries provide a scalable and cost-efficient energy storage solution. However, the achieved power density depends on the local flow conditions of the zinc particle suspension in the electrochemical cell. Numerical modeling is challenging due to the complex multiphase fluid and the interaction of flow and electrochemistry. Hence, performing experiments is crucial to investigate the influence of the flow conditions on the electrical performance, which requires flow instrumentation for the opaque suspension. To resolve the flow field across the 2.6-mm-wide flow channel of the investigated zinc-air flow battery (ZAB), a spatial resolution below 100 μm has to be typically achieved. Using ultrasound techniques, the achieved spatial resolution is limited by the trade-off between ultrasound frequency and imaging depth. This trade-off is even more critical for suspensions due to the scattering of the ultrasound, which increases strongly with frequency. We propose super-resolution particle tracking velocimetry (SRPTV) to overcome this limitation by achieving the required spatial resolution at a low ultrasound frequency. SRPTV is based on the super-resolution technique ultrasound localization microscopy, which is adapted to strongly scattering suspensions by using a dual-frequency-phased array and applying a coherence weighting beamformer to suppress speckles, which result from the scattering at the zinc particles of the suspension. The spatial resolution and the velocity uncertainty are characterized through calibration measurement and numerical simulation. A spatial resolution of 66 μm at an excitation wavelength of 330 μm was achieved, which is sufficient for performing flow investigation in an operational ZAB. The measured flow profile reveals shear-thinning properties and wall slip and therefore differs significantly-from a parabolic flow profile of a Newtonian fluid. The presented technique offers potential for performing flow investigations of suspensions in small geometries with a spatial resolution beyond the diffraction limit.

Ultrasound image velocimetry (UIV) allows noninvasive flow imaging of opaque media with high spatial and temporal resolution, which is especially relevant for experimental research in magnetohydrodynamics (MHD). To extract quantitative flow data from lab-scale experiments in low melting alloys and interpret the results, the measurement uncertainty of a specific experiment has to be determined. In this article, we describe a novel, generic method for estimating the uncertainty of a specific experimental setup according to the guide to the expression of uncertainty in measurement (GUM). It is based on an uncertainty model of the UIV processing, which is calibrated using a flow reference in liquid metal. The proposed model uses a priori knowledge of an expected velocity gradient, a static calibration of the focus distortion, and the peak ratio of the correlation from the measurement data itself to yield the optimal parameterization of UIV and the velocity uncertainty for a specific experiment. This enables the use of the UIV method for quantitative flow imaging with an explicit uncertainty estimate in experiments where no reference flow is available. Building on this, we derive generic guidelines for an optimal parameterization of UIV to a specific experiment. The approach is demonstrated on a magnetically driven flow of gallium-indium-tin (GaInSn) alloy in a cuboid container. In conclusion, this work is a blueprint to obtain quantified planar vector flow maps with UIV in diverse MHD experiments; practical guidelines are derived and an uncertainty estimation procedure is elaborated.

Flow batteries using suspension electrodes, e.g., zinc-air flow batteries (ZABs), have recently gained renewed interest as potential candidates for grid energy storage or mobile applications. The performance of ZABs depends on the local flow conditions of the suspension in the electrochemical cell, which acts as an electrode. Hence, it is crucial to measure and understand the complex flow characteristics of such solid-liquid suspensions. The investigated suspension electrode is an opaque slurry that consists of microscopic zinc particles and an aqueous potassium hydroxide electrolyte. Commonly, ultrasound Doppler velocimetry is used for flow imaging in opaque fluids. However, due to the high particle concentration in the suspension electrode, strong scattering and wavefront distortions of the ultrasound are introduced. In this paper, we show that this results in an increased measurement uncertainty for Doppler-based velocity estimation. Instead, ultrasound image velocimetry is applied to measure the 2-D and two-component flow field in the zinc-electrolyte suspension. This is possible by adapting the measurement system to the suspension with a calibration setup. The total measurement uncertainties of 4.1% and 2.5% for the axial and lateral flow components are derived from the calibration measurements. For the first time, the flow field of such a suspension could be measured in a scaled fluidic model of a ZAB. The comparison of the estimated flow rates from the velocity profiles showed good agreement to a gravimetric reference. A significant difference in the flow characteristics of a macroscopically homogeneous electrolyte and the same electrolyte loaded with 8 vol.-% zinc particles, i.e., the suspension electrode, was found. Along with the demonstration of the measurement technique for opaque, concentrated suspensions, the measurement data will be used to calibrate and validate numerical models for comparable multiphase fluids.

Water management is a key issue in the design and operation of proton exchange membrane fuel cells (PEMFCs). For an efficient and stable operation, the accumulation of liquid water inside the flow channels has to be prevented. Existing measurement methods for localizing water are limited in terms of the integration and application of measurements in operating PEMFC stacks. In this study, we present a measurement method for the localization of liquid water based on ultrasonic guided waves. Using a sparse sensing array of four piezoelectric wafer active sensors (PWAS), the measurement requires only minor changes in the PEMFC cell design. The measurement method is demonstrated with ex situ measurements for water drop localization on a single bipolar plate. The wave propagation of the guided waves and their interaction with water drops on different positions of the bipolar plate are investigated. The complex geometry of the bipolar plate leads to complex guided wave responses. Thus, physical modeling of the wave propagation and tomographic methods are not suitable for the localization of the water drops. Using machine learning methods, it is demonstrated that the position of a water drop can be obtained from the guided wave responses despite the complex geometry of the bipolar plate. Our results show standard deviations of 4.2 mm and 3.3 mm in the x and y coordinates, respectively. The measurement method shows high potential for in situ measurements in PEMFC stacks as well as for other applications that require deposit localization on geometrically complex waveguides.

Wearable ultrasound sensing systems are rapidly emerging for precise, continuous, and intuitive biomedical monitoring and human-in-the-loop interaction in healthcare, industry, and rehabilitation. These systems must operate under stringent constraints on size, weight, and power while delivering actionable physiological and functional information. Advances in micromachined transducers, conformable electronics, low-power signal processing, and edge artificial intelligence (AI) have enabled the first generation of wearable prototypes, yet integration of hardware and software at the system-level remains a major barrier to mass deployment. This review maps the technology readiness and architectures of wearable ultrasound systems, and examines critical design trade-offs, including edge versus cloud-based processing and pulse-echo versus coded signal approaches. We identify recurring design principles and argue that modular, scalable, and reusable platforms are key to lowering development barriers and accelerating translation from prototypes to commercial deployment across healthcare, industrial, and consumer domains.