scholarly journals Technical note: Turbulence measurements from a Light Autonomous Underwater Vehicle

2021 ◽  
Author(s):  
Eivind Hugaas Kolås ◽  
Tore Mo-Bjørkelund ◽  
Ilker Fer
Keyword(s):  
Barents Sea ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Technical Note ◽  
Turbulence Measurements ◽  
Marine Systems ◽  
The Barents Sea ◽  
Accelerometer Signal ◽  
Shear Probe ◽  
Good Agreement

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.

scholarly journals A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration

2017 ◽  
Vol 231 (4) ◽  
pp. 446-464 ◽  
Author(s):  
Christoph Alexander Thieme ◽  
Ingrid Bouwer Utne
Keyword(s):  
Risk Model ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Bayesian Belief Network ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Risk Models ◽  
Marine Systems ◽  
Belief Network ◽  
Human Autonomy

Autonomous marine systems, such as autonomous ships and autonomous underwater vehicles, gain increased interest in industry and academia. Expected benefits of autonomous marine system in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. Autonomous underwater vehicles are applied in scientific, commercial, and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. Autonomous underwater vehicles are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with autonomous underwater vehicles to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian belief network to assess the human–autonomy collaboration performance, as part of a risk model for autonomous underwater vehicle operation. Human–autonomy collaboration represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the human–autonomy collaboration can be improved in two ways: (1) through better training and inclusion of experienced operators and (2) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the human–autonomy collaboration Bayesian belief network can improve autonomous underwater vehicle design and autonomous underwater vehicle operations by clarifying relationships between technical, human, and organizational factors and their influence on mission risk. The article focuses on autonomous underwater vehicle, but the results should be applicable to other types of autonomous marine systems.

Method application of optimal multi-parameter analysis to assess the distribution of water masses as an example of CTD data of the Barents Sea in summer 2017

2019 ◽  
pp. 38-51
Author(s):  
Valeriy G. Yakubenko ◽  
Anna L. Chultsova
Keyword(s):  
Barents Sea ◽  
Field Measurements ◽  
Structural Similarity ◽  
Water Masses ◽  
Parameter Analysis ◽  
Water Dynamics ◽  
Phosphorus Concentration ◽  
Nitrogen And Phosphorus ◽  
The Barents Sea ◽  
Expert Assessments

Identification of water masses in areas with complex water dynamics is a complex task, which is usually solved by the method of expert assessments. In this paper, it is proposed to use a formal procedure based on the application of the method of optimal multiparametric analysis (OMP analysis). The data of field measurements obtained in the 68th cruise of the R/V “Academician Mstislav Keldysh” in the summer of 2017 in the Barents Sea on the distribution of temperature, salinity, oxygen, silicates, nitrogen, and phosphorus concentration are used as a data for research. A comparison of the results with data on the distribution of water masses in literature based on expert assessments (Oziel et al., 2017), allows us to conclude about their close structural similarity. Some differences are related to spatial and temporal shifts of measurements. This indicates the feasibility of using the OMP analysis technique in oceanological studies to obtain quantitative data on the spatial distribution of different water masses.

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