Surface wave measurements from an autonomous underwater vehicle

Haven, Scott; Terray, Eugene A.

doi:10.1109/cwtm.2015.7098106

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Although the REMUS 100 can be programmed to maintain a prescribed depth and speed, surface waves have the potential to influence its dynamics. In practice, the wave field will perturb the vehicle's speed and vertical displacement to a degree, especially in shallow water, in routes near the sea surface and in the presence of large waves (e.g., Goodman et al 2010;Sgarioto 2011;Amador et al 2015;Haven and Terray 2015). Here we show how the influence of the wave velocity field on the vehicle trajectory can lead to a bias in spatially averaged velocity measurements, and we develop a theoretical framework to quantify this bias.…”

Section: Introductionmentioning

confidence: 91%

“…Here, we provide wave-induced bias estimates based on only directional wave spectra measured by bottom-mounted, fixed ADCPs in close proximity to the AUV and over a time window that exceeds the AUV averaging time. However, recent work by Haven and Terray (2015) has shown that it is possible to measure sea surface spectra and mean wave direction from an underway AUV equipped with an onboard ADCP and inertial sensors. This new capability could provide a more accurate way FIG.…”

Section: B Additional Commentsmentioning

confidence: 99%

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ADCP Bias and Stokes Drift in AUV-Based Velocity Measurements

Amador

Jaramillo

Pawlak

2017

Journal of Atmospheric and Oceanic Technology

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A theoretical model is developed to describe how autonomous underwater vehicle (AUV)-based current measurements are influenced by a surface wave field. The model quantifies a quasi-Lagrangian, wave-induced velocity bias as a function of the local wave conditions, and the vehicle’s depth and velocity using a first-order expansion of the linear wave solution. The theoretical bias is verified via field experiments carried out off the coast of Oahu, Hawaii. Spatially averaged along- and cross-track AUV velocity measurements are calculated over one effective wavelength and compared with time-averaged, fixed ADCP measurements in a range of wave and current conditions. The wave-induced bias is calculated using wave directional spectra derived from fixed ADCP data. Ensemble-averaged velocity differences confirm the presence of the wave-induced bias O(1–5) cm s−1 and reveal an additional bias in the direction of the vehicle motion O(1) cm s−1. The analysis considers velocity measurements made using a Remote Environmental Monitoring Units (REMUS) 100 AUV, but the content applies to any small AUV (vehicle size wavelength) immersed in a wave field.

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Section: Introductionmentioning

confidence: 91%

Section: B Additional Commentsmentioning

confidence: 99%

ADCP Bias and Stokes Drift in AUV-Based Velocity Measurements

Amador

Jaramillo

Pawlak

2017

Journal of Atmospheric and Oceanic Technology

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“…Jeans et al 2003) or, in the case of shallow water, Acoustic Doppler Current Profilers (ADCPs) or other wave gauges mounted on the sea floor (Herbers and Lentz 2010). Alternative methods, such as mounting of ADCPs on autonomous underwater vehicles (Haven and Terray 2015), the Surpact Waverider (Reverdin et al 2013), and shipmounted acoustic sensors (Christensen et al 2013) have also been demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Directional wave measurements using an autonomous vessel

2016

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An autonomous vessel, the Offshore Sensing Sailbuoy, was used for wave measurements near the Ekofisk oil platform complex in the North Sea (56.5º N, 3.2º E, operated by ConocoPhillips) from 6 to 20 November 2015. Being 100 % wind propelled, the Sailbuoy has two-way communication via the Iridium network and has the capability for missions of 6 months or more. It has previously been deployed in the Arctic, Norwegian Sea and the Gulf of Mexico, but the present study was the first test for wave measurements. During the campaign the Sailbuoy held position about 20 km northeast of Ekofisk (on the lee side) during rough conditions. Mean wind speed measured at Ekofisk during the campaign was 9.8 m/s, with a maximum of 20.4 m/s, with wind mostly from south and southwest. A Datawell MOSE G1000 GPS-based 2 Hz wave sensor was mounted on the Sailbuoy. Mean significant wave height (H s 1 min) measured was 3 m, whereas maximum H s was 6 m. Mean wave period was 7.7 s, while maximum wave height, H max , was 12.6 m. These measurements have been compared with non-directional Waverider observations at the Ekofisk complex. The agreement between the two data sets was very good, with a mean percent absolute error of 7 % and a linear correlation coefficient of 0.97. The wave frequency spectra measured by the two instruments compared very well, except for low H s (∼1 m), where the motion of the vessel seemed to influence the measurements. Nevertheless, the Sailbuoy performed well during this campaign, and results suggest that it is a suitable platform for wave measurements in a broad range of sea conditions.

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Error Analysis and Correction of ADCP Attitude Dynamics under Platform Swing Conditions

Sun,

Yao

2024

JMSE

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The Acoustic Doppler Current Profiler (ADCP) on a platform generates rotational linear velocity due to dynamic factors in attitude changes, leading to measurement errors in vessel and water flow velocities. This study derives and analyzes these errors, focusing on factors such as emission angle, transducer position, water depth, and measured depth, while also accounting for the variation in linear velocity and radial direction during each transmit–receive pulse cycle in the simulations. A method is proposed that introduces the concept of an equivalent radial radius to correct vessel and flow velocities, specifically designed for the common scenario where the ADCP is installed on the central longitudinal section of a vessel undergoing free roll motion. This method is suited for shallow water conditions without waves, with measurements taken vertically downward. It uses least squares fitting with an exponentially decaying sinusoidal model to process low-sampling-rate inclinometer data from the ADCP. This approach requires only the processing of measured data based on existing ADCP hardware, without the need for additional equipment. Field tests in a pool demonstrate that the proposed method significantly reduces vessel velocity errors, outperforming the traditional attitude static correction method.

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Surface wave measurements from an autonomous underwater vehicle

Cited by 3 publications

References 6 publications

ADCP Bias and Stokes Drift in AUV-Based Velocity Measurements

ADCP Bias and Stokes Drift in AUV-Based Velocity Measurements

Directional wave measurements using an autonomous vessel

Error Analysis and Correction of ADCP Attitude Dynamics under Platform Swing Conditions

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