Effects of sampling configurations of acoustic Doppler velocimeter (ADV) on turbulence measurements around large roughness elements

Golpira, Amir; Baki, Abul Basar M.; Azimi, Amir H.

doi:10.1007/s11600-022-00769-9

Cited by 8 publications

(5 citation statements)

References 46 publications

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“…To eliminate the poor-quality data, signal-to-noise ratio (SNR) and velocity signal correlations (COR) are commonly used. A filtering scheme with an average COR ≤ 70% and average SNR ≤ 15 dB was used to eliminate low quality data from the velocity time series to yield reliable data 28 , 29 .…”

Section: Methodsmentioning

confidence: 99%

The effects of large roughness elements on the in-stream transport and retention of polystyrene microplastics

Ijaz

Baki

Abdul-Aziz

et al. 2023

Sci Rep

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The mechanisms controlling transport and retention of microplastics (MPs) in riverine systems are not understood well. We investigated the impact of large roughness elements (LREs) on in-stream transport and retention of the ubiquitous polystyrene-microplastics (PS-MPs). Scaled experiments were conducted with and without LREs under various shear Reynolds numbers (Re*) in an ecohydraulics flume. Our results, for the first time, demonstrated a clear dependence of the MPs’ velocity on Re* in LREs-dominated channel. Two distinct regimes and thresholds were identified: lower Re* (≤ 15,000) regime corresponding to higher velocities of MPs ($${U}_{MPs}^{*}$$ U MPs ∗ > 0.45), and higher Re* (> 15,000) to lower $${U}_{MPs}^{*} ($$ U MPs ∗ ( < 0.45). The presence and higher density of LREs increased Re*, decreased $${U}_{MPs}^{*}$$ U MPs ∗ , and enhanced the PS-MPs capture. The LREs-generated turbulence kinetic energy (TKE) was found to be a good predictor of PS-MPs transport and retention rates, indicating the effectiveness of LREs in retaining PS-MPs in streams and rivers.

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Section: Methodsmentioning

confidence: 99%

The effects of large roughness elements on the in-stream transport and retention of polystyrene microplastics

Ijaz

Baki

Abdul-Aziz

et al. 2023

Sci Rep

Self Cite

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“…Larger number of the obtained samples allow for higher statistical moments to be calculated, while first and second statistical moments of velocity components can be calculated with reduced sampling. Chanson et al [42] recommend sampling at 50,000 and 5000 for higher and lower statistical moments, respectively, while some studies report that turbulence can be estimated with even lower number of samples, e.g., 3000 [43], and 2250 [44], or that cumulative moving averages the Reynolds shear stress stabilize under 60 s [28]. Cumulative average of TKE calculated for two distinct flow conditions (Q1d1 and Q2d2) is given in the Appendix F. Cumulative average of TKE is presented for the flume mid-section, for each probe configuration and For the sensitivity analysis, the same dataset that was previously used for calibration and verification of ADVP operation parameters was utilized.…”

Section: Methodsmentioning

confidence: 99%

“…The errors in the acoustic velocity measurements come from two main sources, instrument architecture and flow oscillations, in the form of Doppler noise and random spikes [26,27]. Golpira et al [28] found that adjusting cell size and sampling frequency significantly influenced the mean flow velocity for time-averaged data and Reynolds stress near the bed for instantaneous data, consistent with the finding of Zedel and Hay [19] and Leng and Chanson [20]. They concluded that higher sampling frequency and smaller cell size improve ADV quality parameters and increase the noise variance.…”

Section: Introductionmentioning

confidence: 99%

Calibration and Verification of Operation Parameters for an Array of Vectrino Profilers Configured for Turbulent Flow Field Measurement around Bridge Piers—Part II

Gilja

Fliszar

Harasti

et al. 2023

Fluids

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High-frequency velocimeters used for flow measurements during laboratory experiments allow the user to select the range for several operation parameters to set up the instrument for optimal velocity measurement. The discrepancies between velocity measurements collected with different instrument configurations can be significant, depending on the flume bed configuration and boundary conditions. The aim of this paper is to quantify the differences in flow velocity profiles measured with Acoustic Doppler Velocimeter Profilers (ADVPs) configured using a combination of profiling parameters: Ping Algorithm (PA), Transmit Pulse Size (TPS), and Cell Size (CS). Whereas in Part I of this research, the goal was to identify the optimal probe configuration for downstream measurement of the complex hydraulic structure (pier protected with riprap) based on a match of the flow rate with measurements from other instruments, in this paper, effect of distinct probe configuration on velocity profile and turbulent kinetic energy (TKE) is demonstrated. Differences between ADVPs’ configurations were analyzed through sensitivity analysis with the intention to calculate and compare any discrepancies in the velocity measurements for all the three measured velocity components: streamwise u, spanwise v and vertical w collected on two characteristic flume cross-sections. The results show that each parameter change has a significant effect on the measured values of each velocity component when compared to the Target Configuration (TC). The largest root-mean-square-error (RMSE) is observed when TPS is changed, followed by CS and PA. Absolute RMSE calculated for TPS change from 4 mm to 1 mm is, on average, 6.30 cm/s, 0.90 cm/s, and 0.82 cm/s for velocity components u, v and w, respectively. Absolute RMSE calculated for CS change from 1 mm to 4 mm is, on average, 4.49 cm/s, 0.88 cm/s, and 0.71 cm/s for velocity components u, v and w, respectively. Absolute RMSE calculated for PA change from Adaptive to Max interval is, on average, 4.04 cm/s, 0.63 cm/s, and 0.68 cm/s for velocity components u, v and w, respectively. For a change in all parameters, RMSE is greater for the cross-section downstream of the pier than for the approach cross-section: on average, 90%, 57% and 54% for a change in the PA, TPS, and CS, respectively.

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“…This can also cause a sharp adjustment of the riverbed and the formation of a large number of morphologically diverse boulder microterrains (Hassan and Reid, 2010;Strom et al, 2004). The appearance of boulders can lead to a series of changes in local waters, such as average ow eld conversion, turbulence intensi cation and bed shear stress change along the way (Roy et al, 2004;Miller et al, 2010; Lacey and Rennie, 2012; Golpira et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic adjustment of mean flow and turbulence around a sinking boulder during local scouring

Ye,

Zhang,

Wang

et al. 2024

Preprint

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The fact that on a live bed, boulders tend to sink during scouring is usually ignored, weakening the true understanding of hydrodynamics in boulder beds. In this paper, flume experiments were conducted to investigate the hydrodynamics around a boulder over a movable bed with a particle tracking velocimetry (PTV) system. By measuring the velocity field, the major flow characteristics, such as velocity distribution, turbulent kinetic energy (TKE) and bed shear stress, were analyzed. The results show that the sinking boulder apparently mediates the local flow structure and turbulence pattern. The near wake region is located in the range of 2D (D is the particle size of the boulder) downstream of the boulder. There is a near-bed countercurrent in the near-wake region, the TKE increase sharply, and the velocity distribution deviates from the logarithmic distribution. Compared with the flat bed, the turbulent kinetic energy extreme point of the boulder riverbed in the near wake area deviate from the bed surface to the water depth at the top of the boulder, and the direction reversal and extreme point appear at the top of the boulder. The bed shear stress increases sharply in the near-wake region of 1.5 ~ 2D.

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Effects of sampling configurations of acoustic Doppler velocimeter (ADV) on turbulence measurements around large roughness elements

Cited by 8 publications

References 46 publications

The effects of large roughness elements on the in-stream transport and retention of polystyrene microplastics

The effects of large roughness elements on the in-stream transport and retention of polystyrene microplastics

Calibration and Verification of Operation Parameters for an Array of Vectrino Profilers Configured for Turbulent Flow Field Measurement around Bridge Piers—Part II

Hydrodynamic adjustment of mean flow and turbulence around a sinking boulder during local scouring

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