ADV Data Analysis for Turbulent Flows: Low Correlation Problem

Martin, Véronique; Fisher, Timothy S.; Millar, Robert G.; Quick, Michael C.

doi:10.1061/40655(2002)101

Cited by 48 publications

(48 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At high levels of turbulence particles in the water will not maintain their relative positions with respect to each other, which increases the Doppler noise level of the return signal. As reported by Martin et al 2002, increasing the velocity range will increase the correlation values. However, such an approach will decrease the measurement accuracy and should be applied with caution.…”

Section: Percentage Of Outliersmentioning

confidence: 68%

“…Cutoff values of 50%, 60% and 70% correspond to ~2%, ~4% and ~10% filtered data. The velocity measurements become less well correlated near the bed due to increased turbulence levels near boundaries (Martin et al 2002). At high levels of turbulence particles in the water will not maintain their relative positions with respect to each other, which increases the Doppler noise level of the return signal.…”

Section: Percentage Of Outliersmentioning

confidence: 99%

“…The strategy adopted to detect outliers is to apply a low correlation filter so as not to remove 1 legitimate data. Following the suggestion of Martin et al (2002), we recommend applying a 2 correlation filter cutoff value of 40%; and in extreme cases not applying a correlation filter if 3 correlations are consistently below 40%. Subsequently, replacement using an overall average or 4 previous valid point is recommended given the problems identified with the polynomial 5 interpolation method.…”

Section: Pre-and Post-processing Stepsmentioning

confidence: 99%

See 2 more Smart Citations

Ornl Adv Post-Processing Guide and Matlab Algorithms for MHK Site Flow and Turbulence Analysis

Gunawan

Neary²,

McNutt³

2011

View full text Add to dashboard Cite

Section: Percentage Of Outliersmentioning

confidence: 68%

Section: Percentage Of Outliersmentioning

confidence: 99%

Section: Pre-and Post-processing Stepsmentioning

confidence: 99%

See 1 more Smart Citation

Ornl Adv Post-Processing Guide and Matlab Algorithms for MHK Site Flow and Turbulence Analysis

Gunawan

Neary²,

McNutt³

2011

View full text Add to dashboard Cite

“…In general, the PST filters detect a higher number of outliers in the near bed region for the X velocity components, for both cases (Figures 3.8,A1 and A2), which could be due to increased turbulence levels near boundary (Martin et al 2002) and/or beam spreading. The mPST filters also detect a higher number of outliers in the near bed region, but it is much less than the PST method.…”

Section: Percentage Of Outliersmentioning

confidence: 99%

“…Low correlated data often have similar velocity and turbulence statistics to the high correlated data; and, based on this definition, can be considered as valid (see also Martin et al 2002;Rusello 2009;Gunawan et al 2011).…”

Section: Percentage Of Outliersmentioning

confidence: 99%

Ornl Adcp Post-Processing Guide and Matlab Algorithms for MHK Site Flow and Turbulence Analysis

Gunawan

Neary²

2011

View full text Add to dashboard Cite

A Froude‐scaled model of a bedrock‐alluvial channel reach: 1. Hydraulics

Hodge

Hoey

2016

JGR Earth Surface

View full text Add to dashboard Cite

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract The controls on hydraulics in bedrock-alluvial rivers are relatively poorly understood, despite the importance of the flow in determining rates and patterns of sediment transport and consequent erosion. To measure hydraulics within a bedrock-alluvial channel, we developed a 1:10 Froude-scaled laboratory model of an 18 × 9 m bedrock-alluvial river reach using terrestrial laser scanning and 3-D printing. In the reported experiments, water depth and velocity were recorded at 18 locations within the channel at each of five different discharges. Additional data from runs with sediment cover in the flume were used to evaluate the hydraulic impact of sediment cover; the deposition and erosion of sediment patches in these runs are analyzed in the companion paper. In our data (1) spatial variation in both flow velocity and Froude number increases with discharge; (2) bulk flow resistance and Froude number become independent of discharge at higher discharges; (3) local flow velocity and Reynolds stress are correlated to the range of local bed topography at some, but not most, discharges; (4) at lower discharges, local topography induces vertical flow structures and slower velocities, but these effects decrease at higher discharges; and (5) there is a relationship between the linear combination of bed and sediment roughness and local flow velocity. These results demonstrate the control that bedrock topography exerts over both local and reach-scale flow conditions, but spatially distributed hydraulic data from bedrock-alluvial channels with different topographies are needed to generalize these findings.

show abstract

ADV Data Analysis for Turbulent Flows: Low Correlation Problem

Cited by 48 publications

References 10 publications

Ornl Adv Post-Processing Guide and Matlab Algorithms for MHK Site Flow and Turbulence Analysis

Ornl Adv Post-Processing Guide and Matlab Algorithms for MHK Site Flow and Turbulence Analysis

Ornl Adcp Post-Processing Guide and Matlab Algorithms for MHK Site Flow and Turbulence Analysis

A Froude‐scaled model of a bedrock‐alluvial channel reach: 1. Hydraulics

Contact Info

Product

Resources

About