Recovering pressure fields from image velocimetry measurements has two general strategies: (i) directly integrating the pressure gradients from the momentum equation and (ii) solving or enforcing the pressure Poisson equation (divergence of the pressure gradients). In this work, we analyze the error propagation of the former strategy and provide some practical insights. For example, we establish the error scaling laws for the pressure gradient integration (PGI) and the pressure Poisson equation. We explain why applying the Helmholtz–Hodge decomposition (HHD) could significantly reduce the error propagation for the PGI. We also propose to use a novel HHD-based pressure field reconstruction strategy that offers the following advantages or features: (i) effective processing of noisy scattered or structured image velocimetry data on a complex domain; (ii) using radial basis functions (RBFs) with divergence/curl-free kernels to provide divergence-free correction to the velocity fields for incompressible flows and curl-free correction for pressure gradients; and (iii) enforcing divergence/curl-free constraints without using Lagrangian multipliers. Complete elimination of divergence-free bias in measured pressure gradient and curl-free bias in the measured velocity field results in superior accuracy. Synthetic velocimetry data based on exact solutions and high-fidelity simulations are used to validate the analysis as well as demonstrate the flexibility and effectiveness of the RBF-HHD solver.

[目的/意义]本研究旨在从研究主题、方法等多个角度分析智慧养老的研究现状和热点趋势,为智慧养老未来发展提供方向.[方法/过程]首先,基于词频统计扩展智慧养老相关关键词,于CNKI数据库中获取与智慧养老密切相关的574篇文献;其次采用文献计量、社会网络分析、LDA主题聚类与内容分析方法,对发文趋势、核心发文主体、研究主题、研究方法进行探析,并运用Python的Echarts工具实现智慧养老领域的多维度可视化分析.[结果/结论]智慧养老研究热度总体呈现攀升趋势,且形成了以高校内部合作为主的核心发文团体;研究主题主要包括智慧养老服务模式、技术需求与接受度、信息技术应用效果和智能养老产品设计,其中技术类、政策类关键词不断拓展丰富,紧随时代热点;研究方法的使用呈现多元化态势,但仍以定性分析为主,实证研究较少,科学性上有所欠缺.

Emerging time-resolved volumetric PIV techniques have made simultaneous measurements of velocity and pressure fields possible. Yet, in many experimental setups, satisfying the spatial and temporal resolution requirements is a challenge. To improve the quality of sparse and noisy data, this paper introduces a constrained cost minimization (CCM) technique, which interpolates unstructured particle tracks to obtain the velocity, velocity gradients, material acceleration, hence the pressure, on a Eulerian grid. This technique incorporates physical constraints, such as a divergence-free velocity field and curl-free pressure gradients. The performance is evaluated using synthetic particle tracks for an unsteady double gyre and direct numerical simulations data for a turbulent channel flow, with varying particle concentrations and added errors. The errors in pressure, calculated using omni-directional integration, and correlations with the original data are compared to those obtained using the singular value decomposition (SVD) interpolation technique. The CCM errors are mostly lower, and the correlation is higher and less sensitive to particle sparsity and added errors compared to those of SVD. The synthetic particle traces are also projected onto four planar images to evaluate the performance of the new procedure together with shake-the-box (STB) particle tracking. A comparison of pressure spectra and correlation with the original data show very good agreement for the CCM method. Hence, CCM appears to be an effective method for improving the interpolation of sparse data. Sample experimental data obtained in the shear layer behind a backward-facing step demonstrate the application of STB and CCM to resolve the pressure field in coherent vortex structures.

The detection of three-dimensional coherent vortical structures that get advected as well as deformed with time is a challenge. However, it is critical for the statistical analysis of these vortices, for example, the quasi-streamwise vortices (QSVs) in the near field of a turbulent shear layer, where cavitation inception typically occurs. These structures exhibit underlying correlations among different properties that can be derived from the velocity gradients. Exploiting these correlations, a pseudo-Lagrangian vortex detection method is proposed that uses k-means clustering based on vorticity magnitude and direction, values of λ2, strain rate structure, axial stretching, and location. The method facilitates the finding that QSVs have pressure minima that are lower than those in the surrounding flow, including the primary spanwise vortices. These minima typically appear after a period of axial stretching and before contraction events.

This study examines the interactions of a compliant wall with a turbulent boundary layer as the deformation scale increases from submicron to several wall units (delta(nu)). The friction velocity Reynolds number ranges between 1435 and 5179, and E/rho U-0(2), where E is the Young modulus, varies from 59 to 2.4, rho is fluid density and U-0 is free-stream velocity. Time-resolved Mach-Zehnder interferometry is used for measuring the spatial distribution of the surface deformation, and two-dimensional (2-D) particle image velocimetry for measuring the velocity in the inner part of the boundary layer. Reynolds stresses and two-point correlations are measured in the log layer. The deformation amplitude increases from 0.02 delta(nu) at E/rho U-0(2) to 3.6 delta(nu) at E/rho U-0(2) = 2.4. Wavenumber-frequency and 2-D spatial spectra show that the deformations consist of two modes: The first is an advected mode that travels downstream at 66 % of U-0, has a lattice-like structure and a preferential spanwise alignment. The amplitude and frequency of this mode agree with the Chase (J. Acoust. Soc. Am., vol. 89, no. 6, 1991, pp. 2589-2596) and Benschop et al. (J. Fluid Mech., vol. 859, 2019, pp. 613-658) model predictions. The second mode is a streamwise-aligned wave that travels at the material shear speed (C-t = 7.85 m s(-1)) in the spanwise direction and has a wavelength of three times the compliant layer thickness. With decreasing E/rho U-0(2) , the velocity profiles in the boundary layer increasingly deviate from those of a rigid smooth wall. Yet, these deviations begin when the deformation is 0.02 delta(nu). The most prominent features are a sharp decrease in velocity at y < 10 delta(nu) and an increase in the near-wall turbulence, both consistent, for matching E/rho U-0(2), with the direct numerical simulation results of Rosti and Brandt (J. Fluid Mech., vol. 830, 2017, pp. 708-735).

This paper introduces the Constrained Cost Minimization (CCM) technique to interpolate unstructured sparse particle tracks and get velocity, velocity gradients, material acceleration, and hence pressure on Eulerian grids. The technique incorporates known information like the divergencefree condition of velocity and curl-free condition of material acceleration to improve the reliability of reconstruction of the flow, compensating for sparse data. The method is tested for sub-sampled simulations of a 2D double-gyre system, artificial tracks from DNS of turbulent channel flow, as well as tomographic experiments for a shear layer. The results show that even with sparse data, lower than comparable experimental limits, we achieve interpolation errors of <1%.

The introduction of 3D time-resolved velocity measurement techniques enables calculation of the instantaneous pressure distribution by spatially integrating the material acceleration. This paper introduces an efficient method for 3D integration of the acceleration, which does not require prescribed Dirichlet boundary condition on one of the surfaces, minimizes the propagation of errors in acceleration, and can be easily utilized in flows with complex boundaries. This parallel-line, omni-directional integration procedure (Omni3D) calculates the pressure at every point by integration from all directions, while avoiding regions with large acceleration errors. To reduce the computational costs, the calculations are performed by a GPU-based algorithm, which determines the 3D pressure field from tomographic PIV data in 1 min. The accuracy of Omni3D is compared to that of several techniques, including procedures based on solving the Pressure Poisson Equation (PPE) with different Dirichlet boundary conditions. The error analysis is based on Direct Numerical Simulation (DNS) data for isotropic turbulence, synthetic 3D PIV images for turbulent channel flow generated from DNS data, and experimental data. It examines the effects of spatial resolution, propagation, and avoidance of embedded local errors, boundary conditions, method for calculating the velocity, as well as viscous and sub-grid stresses on the calculated pressures. For acceleration fields with low errors and properly specified boundary conditions, Omni3D and PPE give similar results. However, Omni3D is more effective in suppressing the effects of acceleration errors. Sample experimental results including instantaneous plot of pressure, pressure statistics, and pressure–velocity correlations based on tomographic PIV data are also provided.

This laboratory experimental study investigates the temporal evolution of the size distribution of subsurface oil droplets generated as breaking waves entrain oil slicks. The measurements are performed for varying wave energy, as well as large variations in oil viscosity and oil-water interfacial tension, the latter achieved by premixing the oil with dispersant. In situ measurements using digital inline holography at two magnifications are applied for measuring the droplet sizes and Particle Image Velocimetry (PIV) for determining the temporal evolution of turbulence after wave breaking. All early (2-10 s) size distributions have two distinct size ranges with different slopes. For low dispersant to oil ratios (DOR), the transition between them could be predicted based on a turbulent Weber (We) number in the 2-4 range, suggesting that turbulence plays an important role. For smaller droplets, all the number size distributions have power of about -2.1, and for larger droplets, the power decreases well below -3. The measured steepening of the size distribution over time is predicted by a simple model involving buoyant rise and turbulence dispersion. Conversely, for DOR 1:100 and 1:25 oils, the diameter of slope transition decreases from approximate to 1 mm to 46 and 14 mu m, respectively, much faster than the We-based prediction, and the size distribution steepens with increasing DOR. Furthermore, the concentration of micron-sized droplets of DOR 1:25 oil increases for the first 10 min after entrainment. These phenomena are presumably caused by the observed formation and breakup oil microthreads associated with tip streaming.

Interaction of a compliant wall with a turbulent channel flow is investigated experimentally by simultaneously measuring the time-resolved, three-dimensional (3D) flow field and the two-dimensional (2D) surface deformation. The optical set-up integrates tomographic particle image velocimetry to measure the flow with Mach–Zehnder interferometry to map the deformation. The Reynolds number is $Re_{\unicode[STIX]{x1D70F}}=2300$ , and the Young’s modulus of the wall is 0.93 MPa, resulting in a ratio of shear speed to the centreline velocity ( $U_{0}$ ) of 6.8. The wavenumber–frequency spectra of deformation show the surface motions consist of a non-advected low-frequency component and advected modes, some travelling downstream at approximately $U_{0}$ and others at ${\sim}0.72U_{0}$ . The r.m.s. values of the advected and non-advected modes are $0.04~\unicode[STIX]{x03BC}\text{m}$ $(0.004\unicode[STIX]{x1D6FF}_{\unicode[STIX]{x1D708}})$ and $0.2~\unicode[STIX]{x03BC}\text{m}$ ( $0.02\unicode[STIX]{x1D6FF}_{\unicode[STIX]{x1D708}}$ ), respectively, much smaller than the wall unit ( $\unicode[STIX]{x1D6FF}_{\unicode[STIX]{x1D708}}$ ), hence they do not affect the flow. Trends in the wall dynamics are elucidated by correlating the deformation with flow variables, including the 3D pressure distribution calculated by spatially integrating the material acceleration. Predictions by the Chase [J. Acoust. Soc. Am., vol. 89 (6), pp. 2589–2596] linear model are also calculated and compared to the measured trends. The spatial deformation–pressure correlations peak at $y/h\approx 0.12$ ( $h$ is half channel height), the elevation of Reynolds shear stress maximum in the log-layer. Streamwise lagging of the deformation behind the pressure is caused in part by phase lag of the pressure with decreasing distance from the wall, and in part by material damping. Positive deformations (bumps) caused by negative pressure fluctuations are preferentially associated with ejections involving spanwise vortices located downstream and quasi-streamwise vortices with spanwise offset. Results of conditional correlations are consistent with the presence of hairpin-like structures. The negative deformations (dimples) are preferentially associated with positive pressure fluctuations at the transition between an upstream sweep to a downstream ejection.

Interaction of a compliant wall with a turbulent channel flow is investigated by simultaneously measuring the time-resolved, three-dimensional flow field using tomographic PIV and the two-dimensional surface deformation using Mach-Zehnder interferometry. The friction Reynolds number is Reτ = 2300, and the Young’s Modulus of the wall is 0.93 MPa, resulting in a ratio of shear speed to centerline velocity (U0) of 6.8. The wavenumber-frequency spectra of deformation contain a non-advected low-frequency component and advected modes, some traveling at U0 and others at 0.72U0. The wall dynamics is elucidated by correlating the deformation with flow variables, including the 3D pressure distribution. The pressure-deformation correlations peak at y/h~0.12 (h is half channel height), the elevation of Reynolds stress maximum in the log-layer. Streamwise lagging of the deformation behind the pressure is caused in part by phase-lag of the pressure with decreasing elevation, and in part by material damping predicted by the Chase (1991) model. Positive deformations (bumps) are preferentially associated with ejections involving spanwise vortices downstream and quasi-streamwise vortices with spanwise offset, consistent with presence of hairpin-like structures. The negative deformations (dents) are preferentially associated with a positive pressure fluctuation at the sweep-ejection transition. [Sponsored by ONR.]