shear probe
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 460
Author(s):  
Yunli Nie ◽  
Dalei Song ◽  
Zhenyu Wang ◽  
Yan Huang ◽  
Hua Yang

The use of a multi-functional autonomous underwater vehicle (AUV) as a platform for making turbulence measurements in the ocean is developed. The layout optimization of the turbulence package and platform motion performance are limitation problems in turbulent AUV design. In this study, the computational fluid dynamics (CFD) method has been used to determine the optimized layout position and distance of the shear probe integrated into an AUV. When placed 0.8 D ahead of the AUV nose along the axis, the shear probe is not influenced by flow distortion and can contact the water body first. To analyze the motion of the turbulence AUV, the dynamic model of turbulence AUV for planar flight is obtained. Then, the mathematical equations of speed and angle of attack under steady-state motion have also been obtained. By calculating the hydrodynamic coefficients of the turbulence AUV and given system parameters, the simulation analysis has been conducted. The simulation results demonstrated that the speed of turbulent AUV is 0.5–1 m/s, and the maximum angle of attack is less than 6.5°, which meets the observation requirements of the shear probe. In addition, turbulence AUV conducted a series of sea-trials in the northern South China Sea to illustrate the validity of the design and measurement. Two continuous profiles (1000 m) with a horizontal distance of 10 km were completed, and numerous high-quality spatiotemporal turbulence data were obtained. These profiles demonstrate the superior flight performance of turbulence AUV. Analysis shows that the measured data are of high quality, with the shear spectra being in very good agreement with the Nasmyth spectrum. Dissipation rates are consistent with background shear. When shear velocity is weak, the measurement of dissipation rate is 10−10 W Kg−1. All indications are that the turbulence AUV is suitable for long-term, contiguous ocean microstructure measurements, which will provide data needed to understand the temporal and spatial variability of the turbulent processes in the oceans.


2021 ◽  
Author(s):  
Eivind Hugaas Kolås ◽  
Tore Mo-Bjørkelund ◽  
Ilker Fer

Abstract. A self-contained turbulence instrument from Rockland Scientific was installed on a Light Autonomous Underwater Vehicle (AUV) from OceanScan Marine Systems and Technology Lda. We report on the data quality and discuss limitations of dissipation estimated from two shear probes during a deployment in the Barents Sea in February 2021. The AUV mission lasted for 5 hours, operating at a typical horizontal speed of 1.2 m s−1. The AUV was programmed to find and cross the maximum along-path thermal gradient at 10, 20 and 30 m depths along 4 km transects. Although the AUV vibrations contaminate the shear probe records, the noise is mitigated by removing vibration-induced components from shear spectra using accelerometer signal measured in multiple directions. Dissipation rate estimates in the observed transects varied in the range 1 × 10−8 and 6 × 10−6 W kg−1, with the values from the two orthogonal probes typically in agreement to within a factor of 2. Dissipation estimates from the AUV show good agreement with nearby vertical microstructure profiles obtained from the ship during the transects, indicating that the turbulence measurements from the AUV are reliable for this relatively turbulent environment. However, the lowest reliable dissipation rates are limited to 5 × 10−8 W kg−1, making this setup unfit for use in quiescent environments.


2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mostafa Bakhoday-Paskyabi

AbstractIn this study, we explore the applicability of a wavelet-entropy based segmentation technique in reduction of motion-induced contaminations in time-domain from subsurface turbulence measurements made by a moving shear probe. After the quality screening of data, the Shannon entropy procedure is combined with a time-dependent adaptive wavelet thresholding method to split each 60-s long shear segment into a number of motion-reduced subblocks. The wavelet-entropy strategy leads to preventing the false detection effect caused by applying either wavelet de-noising or Shannon entropy alone for conditions where the turbulence (strongly) overlap with scales induced by waves or platform motions. The longest stationary subblock, with a size greater than 16-s, is then used to extract the Turbulent Kinetic Energy (TKE) dissipation rate, $$\varepsilon$$ε. Efficiency of the proposed method is verified by comparing with $$\varepsilon$$ε measurements made by a nearby free-falling microstructure profiler. While the quality of observations is constrained by a number of factors such as sensors’ angle of attack, and the wave kinematical and dynamical effects, results demonstrate significant improvements, by approximately a factor of 5–10, compared with $$\varepsilon$$ε measurements from each 60-s segment using the Goodman et al. [13] method. Furthermore, the magnitudes of the motion-corrected $$\varepsilon$$ε using the proposed method is largely consistent with the scaling suggested by Terray et al. [30].


2016 ◽  
Vol 33 (4) ◽  
pp. 817-837 ◽  
Author(s):  
Justine M. McMillan ◽  
Alex E. Hay ◽  
Rolf G. Lueck ◽  
Fabian Wolk

AbstractThe ability to estimate the rate of dissipation (ε) of turbulent kinetic energy at middepth in a high-speed tidal channel using broadband acoustic Doppler current profilers (ADCPs) is assessed by making comparisons to direct measurements of ε obtained using shear probes mounted on a streamlined underwater buoy. The investigation was carried out in Grand Passage, Nova Scotia, Canada, where the depth-averaged flow speed reached 2 m s−1 and the Reynolds number was 8 × 107. The speed bin–averaged dissipation rates estimated from the ADCP data agree with the shear probe data to within a factor of 2. Both the ADCP and the shear probe measurements indicate a linear dependence of ε on the cube of the flow speed during flood and much lower dissipation rates during ebb. The ebb–flood asymmetry and the small-scale intermittency in ε are also apparent in the lognormal distributions of the shear probe data. Possible sources of bias and error in the ε estimates are investigated, and the most likely causes of the discrepancy between the ADCP and shear probe estimates are the cross-channel separation of the instruments and the high degree of spatial variability that exists in the channel.


2014 ◽  
Vol 31 (5) ◽  
pp. 1128-1150 ◽  
Author(s):  
Ilker Fer ◽  
Algot K. Peterson ◽  
Jenny E. Ullgren

AbstractMeasurements of ocean microstructure are made in the turbulent Faroe Bank Channel overflow using a turbulence instrument attached to an underwater glider. Dissipation rate of turbulent kinetic energy ε is measured using airfoil shear probes. A comparison is made between 152 profiles from dive and climb cycles of the glider during a 1-week mission in June 2012 and 90 profiles collected from the ship using a vertical microstructure profiler (VMP). Approximately one-half of the profiles are collocated. For 96% of the dataset, measurements are of high quality with no systematic differences between dives and climbs. The noise level is less than 5 × 10−11 W kg−1, comparable to the best microstructure profilers. The shear probe data are contaminated and unreliable at the turning depth of the glider and for U/ut < 20, where U is the flow past the sensor, ut = (ε/N)1/2 is an estimate of the turbulent velocity scale, and N is the buoyancy frequency. Averaged profiles of ε from the VMP and the glider agree to better than a factor of 2 in the turbulent bottom layer of the overflow plume, and beneath the stratified and sheared plume–ambient interface. The glider average values are approximately a factor of 3 and 9 times larger than the VMP values in the layers defined by the isotherms 3°–6° and 6°–9°C, respectively, corresponding to the upper part of the interface and above. The discrepancy is attributed to a different sampling scheme and the intermittency of turbulence. The glider offers a noise-free platform suitable for ocean microstructure measurements.


2013 ◽  
Vol 69 ◽  
pp. 73-87 ◽  
Author(s):  
Sotiris Kioroglou ◽  
Elina Tragou ◽  
Vassilis Zervakis

2012 ◽  
Vol 18 (2) ◽  
pp. 135-140 ◽  
Author(s):  
Shuxin Wang ◽  
Xuezhong Xiao ◽  
Yanhui Wang ◽  
Zilong Wang ◽  
Baokuo Chen

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