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Application of large scale PIV in river surface turbulence measurements and water depth estimation.

Authors :
Jin, Tong
Liao, Qian
Source :
Flow Measurement & Instrumentation. Jun2019, Vol. 67, p142-152. 11p.
Publication Year :
2019

Abstract

Large-Scale Particle Image Velocimetry (LSPIV) has emerged as a reliable technology to measure river surface flow velocity distribution and can be applied to estimate river discharge. Fewer studies have explored the capability of surface turbulence measurements using LSPIV. In this paper, LSPIV is applied to evaluate statistics of surface turbulence of a natural river. Turbulence measurements including velocity fluctuation, velocity spectra and the dissipation rate of turbulent kinetic energy (TKE) are validated by comparing with those measured by an Acoustic Doppler Velocimeter (ADV). Traditionally, estimation of stream discharge through LSPIV needs a secondary measurement to determine river bathymetry and water depth. A new method is presented here to demonstrate that for a fully developed and channel-controlled flow, the cross section geometry can be estimated from the combined measurements of surface mean velocity and the dissipation rate, following the Manning-Strickler formula. Therefore, river discharge can be estimated with LSPIV along with a calibrated Manning's roughness, without additional bathymetry survey. The proposed new method is applied to measure discharge in Milwaukee River (Milwaukee, Wisconsin, U.S.A.), which agreed well with data obtained from a nearby streamgage station. • LSPIV is applied to measure surface turbulence structures in a natural river. • Surface turbulence velocity spectra demonstrated a −5/3 power law. • River depth can be estimated from surface turbulence measured by LSPIV. • River discharge can be measured by LSPIV without channel geometry survey. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09555986
Volume :
67
Database :
Academic Search Index
Journal :
Flow Measurement & Instrumentation
Publication Type :
Academic Journal
Accession number :
136692020
Full Text :
https://doi.org/10.1016/j.flowmeasinst.2019.03.001