Extreme-Point Symmetric Mode Decomposition to Define the Turbulence Characteristics of a Flume Flow

Turbulence is a key feature of solid-liquid two-phase flows, and the pulsating velocity is the basis for calculating turbulence characteristics. In general, the method of mathematical expectation is used to calculate pulsating velocity. However, this method does not reflect the fluctuating state of the instantaneous velocity. Therefore, the method of extreme-point symmetric mode decomposition (ESMD) is adopted to calculate pulsating velocity and turbulence characteristics. The ESMD involves two stages, namely, modal decomposition and time-frequency analysis. The optimal adaptive global mean (AGM), which is the result of modal decomposition, can accurately reflect the fluctuation state of the instantaneous velocity, and the theory of the pulsating velocity defined on this basis is reasonable. Moreover, the flow pattern and turbulence behaviour of a two-phase flow can be predicted using the calculated turbulence characteristics. The method is used to analyse the pulsating velocity of the flume, and its rationality in theoretically predicting the turbulence behaviour of flume flows is demonstrated.

Detection of magnetic tracers with Mx atomic magnetometer for application to blood velocimetry

In the new generation of blood velocimeter systems, considerable attention has been paid to atomic magnetometers due to their high resolution and high sensitivity for detection of magnetic tracers. Passing the magnetic tracers adjacent to the atomic magnetometer produces a spike-like signal, the shape of which depends on the position of the tracer, as well as its velocity and orientation. The present study aimed to evaluate the effect of abrupt variations in the instantaneous velocity of the magnetic tracer on the magnetometer response compare to constant velocity. Modeling the magnetic tracer as a dipole moment indicated that the velocity dependence of the magnetic field and local magnetic field gradient associated with moving magnetic tracer cause the spike-like signal to go out of symmetry in the case of variable velocity. Based on the experimental results, any instantaneous variation in tracer velocity leads to shrinkage in the signal width. The behavior has been studied for both magnetic microwire with variable instantaneous velocity and magnetic droplets in stenosis artery phantom. In addition, the position of the tracer could be detected by following the shrinkage behavior which may occur on the peak, valley, or both. These advantageous outcomes can be applied for high sensitivity diagnosis of arterial stenosis.

Large-Scale, Accurate, High Resolution, Measurements of River Surface Velocity and Turbulence Metrics Using Thermal Infrared Images

<p>We present a velocimetry method, which we refer to as Infrared Quantitative Velocimetry (IR-QIV), that uses images of thermal patterns, captured in the infrared, on the surface of rivers or other water bodies, to calculate the time-resolved instantaneous two-dimensional surface velocity field. The method works in all natural light conditions (day or night), and under most weather conditions, by tracking thermal patterns in the surface of the water, and is therefore suitable for a large range of flows and environments. The method, is a form of remote sensing and has significant advantages over traditional (visible-light) PIV (Particle Image Velocimetry) or LSPIV (Large Scale PIV) methods for non-contact measurement of water surface velocity field, as it requires no particle 'seeding' or contact with the water. </p><p>Measurements of instantaneous water flow velocity, from which turbulence metrics are calculated, are important for advancing the understanding of river hydrodynamics beyond fundamentals such as discharge and mean velocity. However, most velocity measurement methods used in the field are capable of measuring at a point, or along a transect, but not over a two-dimensional area. Additionally, tools such as ADCPs generally require temporal and spatial averaging, and therefore can not resolve instantaneous velocities.</p><p>Image-based velocimetry methods, including IR-QIV and LSPIV, measure at the surface of the water and over a large area. However, methods that utilize visible-light imagery, such as LSPIV, require external illumination at night, and are challenged by the relatively homogeneous appearance of the water surface, often requiring either naturally occurring, or added 'seeding' particles, that are advected by the flow. Due the intermittent availability of seeding or surface texture, spatial or temporal averaging is often required, limiting the technique to mean velocity measurements.</p><p>These limitations do not apply to IR-QIV since under natural conditions a rich texture of temperature differences exist at the surface of the water due to spatially heterogeneous air/water heat exchange. IR-QIV is capable of calculating the instantaneous velocity at high accuracy and resolution, in space and time (centimeter scale, several Hz), over large areas—up to thousands of square meters. The instantaneous velocity measurements can be used to calculate metrics of turbulence to inform applications such as the study of river and other surface water dynamics; small-scale hydrodynamics near flow features such as water diversions, junctions, obstacles, and river bends; fishery management; gas transfer measurement; non-contact estimation of bathymetry, discharge and bed stress, and more.</p><p>We present instantaneous velocity and turbulence metrics measured at sites in the Sacramento River, (California, USA,) made using IR-QIV. Additionally, we discuss issues related to uncertainty analysis in velocimetry techniques using oblique camera viewing angles, and pattern tracking in images containing gradients of intensity (not discrete particles), as well as effects of camera noise. These considerations are relevant to all types of large scale image-based velocimetry, regardless of wavelength of image collection (visible-light or IR), and can be used to inform and improve measurements from both fixed and mobile platforms such, as UASs.</p>