Defining the roughness sublayer and its turbulent statistics

The roughness sublayer in a turbulent openchannel flow over a very rough wall is investigated experimentally both within the canopy and above using particle image velocimetry by gaining complete optical access with new methodologies without disturbing the flow. This enabled reliable estimates of the double-averaged mean and turbulence profiles to be obtained by minimizing and quantifying the usual errors introduced by limited temporal and spatial sampling. It is shown, for example, that poor spatial sampling can lead to erroneous vertical profiles in the roughness sublayer. Then, in order to better define and determine the roughness sublayer height, a methodology based on the measured spatial dispersion is proposed which takes into account temporal sampling errors. The results reveal values well below the usual more ad hoc estimates for all statistics. Finally, the doubleaveraged mean and turbulence statistics in the roughness sublayer are discussed

Particle Image Velocimetry (PIV) for Positron Emission Particle Tracking (PEPT) and Turbulence Modeling Validation

A Particle Image Velocimetry (PIV) experiment is designed and data collected with intention to validate Positron Emission Particle Tracking (PEPT) methods. The PIV data are collected in a narrow rectangular channel for flow Reynolds number near 20,000. The narrow channel and attendant pump, header tanks and flow instrumentation are portable and designed to allow identical tests in a Concord Microsystems MicroPET P4 pre-clinical PET scanner at the pre-clinical Imaging Suite at the UT Hospital. The PIV data are instantaneous velocity field data, allowing statistics on the flow turbulence to be collected in the Eulerian frame. The PEPT method measures activated particle trajectories in time, corresponding to a Lagrangian measurement. The relationship between the PIV data collected herein, and the anticipated PEPT data is explored to provide a path for validating the performance of the PEPT method for flow measurement. The utility of the PEPT method extends to opaque fluids and flow in complex and opaque flow boundaries. These flow conditions are impossible or technically difficult for optical PIV methods to address. The PEPT method also provides full 4 dimensional particle trajectory data, with temporal and spatial resolution competitive with the most advanced optical PIV methods

Turbulent characteristics in the intensity fluctuations of a solar quiescent prominence observed by the \textit{Hinode} Solar Optical Telescope

We focus on Hinode Solar Optical Telescope (SOT) calcium II H-line observations of a solar quiescent prominence (QP) that exhibits highly variable dynamics suggestive of turbulence. These images capture a sufficient range of scales spatially ($\sim$0.1-100 arc seconds) and temporally ($\sim$16.8 s - 4.5 hrs) to allow the application of statistical methods used to quantify finite range fluid turbulence. We present the first such application of these techniques to the spatial intensity field of a long lived solar prominence. Fully evolved inertial range turbulence in an infinite medium exhibits multifractal \emph{scale invariance} in the statistics of its fluctuations, seen as power law power spectra and as scaling of the higher order moments (structure functions) of fluctuations which have non-Gaussian statistics; fluctuations $\delta I(r,L)=I(r+L)-I(r)$ on length scale $L$ along a given direction in observed spatial field $I$ have moments that scale as $<\delta I(r,L)^p>\sim L^{\zeta(p)}$. For turbulence in a system that is of finite size, or that is not fully developed, one anticipates a generalized scale invariance or extended self-similarity (ESS) $\sim G(L)^{\zeta(p)}$. For these QP intensity measurements we find scaling in the power spectra and ESS. We find that the fluctuation statistics are non-Gaussian and we use ESS to obtain ratios of the scaling exponents $\zeta(p)$: these are consistent with a multifractal field and show distinct values for directions longitudinal and transverse to the bulk (driving) flow. Thus, the intensity fluctuations of the QP exhibit statistical properties consistent with an underling turbulent flow

Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics

We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we extract statistical concepts (fast-motion region and the corresponding dominant direction, spatio-temporal color diversity, dominant color, etc.) from simple patterns in both spatial and temporal domains. Unlike prior puzzles that are even hard for humans to solve, the proposed approach is consistent with human inherent visual habits and therefore easy to answer. We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach. The experiments show that our approach can significantly improve the performance of C3D when applied to video classification tasks. Code is available at https://github.com/laura-wang/video_repres_mas.Comment: CVPR 201