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- Author or Editor: Gwyn Griffiths x
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Abstract
Systematic error in the cross-track velocity measured under way from shipboard acoustic Doppler current profilers (ADCPs) can be attributed to error in measuring the ship's heading with a gyrocompass. Drift- and direction-dependent errors in marine gyrocompasses may amount to 2°–3°, yet they can be difficult to observe. A new system for obtaining attitude information using differential carrier phase measurements on signals from Global Positioning System (GPS) navigation satellites can provide a heading accuracy of 0.05°. This paper proposes a method of using these GPS heading measurements as a reference, with the gyrocompass as an interpolation device, to reduce the cross-track velocity error from a shipboard ADCP. The practical application of the method is illustrated by a long north-south section dominated by latitude-induced gyrocompass error, and a small-scale survey where heading-dependent errors in the gyrocompass dominated.
Abstract
Systematic error in the cross-track velocity measured under way from shipboard acoustic Doppler current profilers (ADCPs) can be attributed to error in measuring the ship's heading with a gyrocompass. Drift- and direction-dependent errors in marine gyrocompasses may amount to 2°–3°, yet they can be difficult to observe. A new system for obtaining attitude information using differential carrier phase measurements on signals from Global Positioning System (GPS) navigation satellites can provide a heading accuracy of 0.05°. This paper proposes a method of using these GPS heading measurements as a reference, with the gyrocompass as an interpolation device, to reduce the cross-track velocity error from a shipboard ADCP. The practical application of the method is illustrated by a long north-south section dominated by latitude-induced gyrocompass error, and a small-scale survey where heading-dependent errors in the gyrocompass dominated.
Abstract
The underwater glider is set to become an important platform for oceanographers to gather data within oceans. Gliders are usually equipped with a conductivity–temperature–depth (CTD) sensor, but a wide range of other sensors have been fitted to gliders.
In the present work, the authors aim at measuring the vertical water velocity. The vertical water velocity is obtained by subtracting the vertical glider velocity relative to the water from the vertical glider velocity relative to the water surface. The latter is obtained from the pressure sensor. For the former, a quasi-static model of planar glider flight is developed. The model requires three calibration parameters, the (parasite) drag coefficient, glider volume (at atmospheric pressure), and hull compressibility, which are found by minimizing a cost function based on the variance of the calculated vertical water velocity.
Vertical water velocities have been calculated from data gathered in the northwestern Mediterranean during the Gulf of Lions experiment, winter 2008. Although no direct comparison could be made with water velocities from an independent measurement technique, the authors show that, for two different heat loss regimes (≈0 and ≈400 W m−2), the calculated vertical velocity scales are comparable with those expected for internal waves and active open ocean convection, respectively. High noise levels resulting from the pressure sensor require the water velocity time series to be low-pass filtered with a cutoff period of 80 s. The absolute accuracy of the vertical water velocity is estimated at ±4 mm s−1.
Abstract
The underwater glider is set to become an important platform for oceanographers to gather data within oceans. Gliders are usually equipped with a conductivity–temperature–depth (CTD) sensor, but a wide range of other sensors have been fitted to gliders.
In the present work, the authors aim at measuring the vertical water velocity. The vertical water velocity is obtained by subtracting the vertical glider velocity relative to the water from the vertical glider velocity relative to the water surface. The latter is obtained from the pressure sensor. For the former, a quasi-static model of planar glider flight is developed. The model requires three calibration parameters, the (parasite) drag coefficient, glider volume (at atmospheric pressure), and hull compressibility, which are found by minimizing a cost function based on the variance of the calculated vertical water velocity.
Vertical water velocities have been calculated from data gathered in the northwestern Mediterranean during the Gulf of Lions experiment, winter 2008. Although no direct comparison could be made with water velocities from an independent measurement technique, the authors show that, for two different heat loss regimes (≈0 and ≈400 W m−2), the calculated vertical velocity scales are comparable with those expected for internal waves and active open ocean convection, respectively. High noise levels resulting from the pressure sensor require the water velocity time series to be low-pass filtered with a cutoff period of 80 s. The absolute accuracy of the vertical water velocity is estimated at ±4 mm s−1.
Updating Autonomous Underwater Vehicle Risk Based on the Effectiveness of Failure Prevention and CorrectionAbstract
Autonomous underwater vehicles (AUVs) have proven to be feasible platforms for marine observations. Risk and reliability studies on the performance of these vehicles by different groups show a significant difference in reliability, with the observation that the outcomes depend on whether the vehicles are operated by developers or nondevelopers. This paper shows that this difference in reliability is due to the failure prevention and correction procedures—risk mitigation—put in place by developers. However, no formalization has been developed for updating the risk profile based on the expected effectiveness of the failure prevention and correction process. A generic Bayesian approach for updating the risk profile is presented, based on the probability of failure prevention and correction and the number of subsequent deployments on which the failure does not occur. The approach, which applies whether the risk profile is captured in a parametric or nonparametric survival model, is applied to a real case study of the International Submarine Engineering Ltd. (ISE) Explorer AUV.
Abstract
Autonomous underwater vehicles (AUVs) have proven to be feasible platforms for marine observations. Risk and reliability studies on the performance of these vehicles by different groups show a significant difference in reliability, with the observation that the outcomes depend on whether the vehicles are operated by developers or nondevelopers. This paper shows that this difference in reliability is due to the failure prevention and correction procedures—risk mitigation—put in place by developers. However, no formalization has been developed for updating the risk profile based on the expected effectiveness of the failure prevention and correction process. A generic Bayesian approach for updating the risk profile is presented, based on the probability of failure prevention and correction and the number of subsequent deployments on which the failure does not occur. The approach, which applies whether the risk profile is captured in a parametric or nonparametric survival model, is applied to a real case study of the International Submarine Engineering Ltd. (ISE) Explorer AUV.
Abstract
It has been 20 years since the concept of the Autonomous Ocean Sampling Network (AOSN) was first introduced. This vision has been brought closer to reality with the introduction of underwater gliders. While in terms of functionality the underwater glider has shown to be capable of meeting the AOSN vision, in terms of reliability there is no communitywide hard evidence on whether persistent presence is currently being achieved. This paper studies the reliability of underwater gliders in order to assess the feasibility of using these platforms for future AOSN. The data used are taken from nonunderwater glider developers, which consisted of 205 glider deployments by 12 European laboratories between 2008 and 2012. Risk profiles were calculated for two makes of deep underwater gliders; there is no statistically significant difference between them. Regardless of the make, the probability of a deep underwater glider surviving a 90-day mission without a premature mission end is approximately 0.5. The probability of a shallow underwater glider surviving a 30-day mission without a premature mission end is 0.59. This implies that to date factors other than the energy available are preventing underwater gliders from achieving their maximum capability. This reliability information was used to quantify the likelihood of two reported underwater glider surveys meeting the observation needs for a period of 6 months and to quantify the level of redundancy needed in order to increase the likelihood of meeting the observation needs.
Abstract
It has been 20 years since the concept of the Autonomous Ocean Sampling Network (AOSN) was first introduced. This vision has been brought closer to reality with the introduction of underwater gliders. While in terms of functionality the underwater glider has shown to be capable of meeting the AOSN vision, in terms of reliability there is no communitywide hard evidence on whether persistent presence is currently being achieved. This paper studies the reliability of underwater gliders in order to assess the feasibility of using these platforms for future AOSN. The data used are taken from nonunderwater glider developers, which consisted of 205 glider deployments by 12 European laboratories between 2008 and 2012. Risk profiles were calculated for two makes of deep underwater gliders; there is no statistically significant difference between them. Regardless of the make, the probability of a deep underwater glider surviving a 90-day mission without a premature mission end is approximately 0.5. The probability of a shallow underwater glider surviving a 30-day mission without a premature mission end is 0.59. This implies that to date factors other than the energy available are preventing underwater gliders from achieving their maximum capability. This reliability information was used to quantify the likelihood of two reported underwater glider surveys meeting the observation needs for a period of 6 months and to quantify the level of redundancy needed in order to increase the likelihood of meeting the observation needs.
Abstract
The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk-informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper, a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, that is, reaching agreement on the distributions of risks for faults or incidents, is followed by an agreed upon initial estimate of the likelihood of success of the proposed risk mitigation methods. Postexpedition, a second workshop assesses the new data and compares observed to predicted risk, thus updating the prior estimate using Bayes’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that the maximum error between the predicted and the actual risk was 9% and that the experts’ assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction.
Abstract
The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk-informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper, a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, that is, reaching agreement on the distributions of risks for faults or incidents, is followed by an agreed upon initial estimate of the likelihood of success of the proposed risk mitigation methods. Postexpedition, a second workshop assesses the new data and compares observed to predicted risk, thus updating the prior estimate using Bayes’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that the maximum error between the predicted and the actual risk was 9% and that the experts’ assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction.
