Observations of Turbulence at a Near-Surface Temperature Front in the Arctic Ocean

High-resolution ocean temperature, salinity, current, and turbulence data were collected at an Arctic thermohaline front in the Nansen Basin. The front was close to the sea ice edge and separated the cold and fresh surface melt water from the warm and saline mixed layer. Measurements were made on 18 September 2018, in the upper 100 m, from a research vessel and an autonomous underwater vehicle. Destabilizing surface buoyancy fluxes from a combination of heat loss to the atmosphere and cross-front Ekman transport by down-front winds reduced the potential vorticity in the upper ocean. Turbulence structure in the mixed layer was generally consistent with turbulence production through convection by heat loss to atmosphere and mechanical forcing by moderate winds. Conditions at the front were favorable for forced symmetric instability, a mechanism drawing energy from the frontal geostrophic current. A clear signature of increased dissipation from symmetric instability could not be identified; however, this instability could potentially account for the increased dissipation rates at the front location down to 40 m depth that could not be explained by the atmospheric forcing. This turbulence was associated with turbulent heat fluxes of up to 10 W m−2, eroding the warm and cold intrusions observed between 30 and 60 m depth. A Seaglider sampled across a similar frontal structure in the same region 10 days after our survey. The submesoscale-to-turbulence-scale transitions and resulting mixing can be widespread and important in the Atlantic sector of the Arctic Ocean.publishedVersio

Compact Subsea Separation Unit: - Nonlinear Model Predictive Control and Nonlinear Observers

In search of increased wealth and public prosperity, the oil industry have met new challenges in their quest for black gold. These challenges have driven Statoil's expertise to develop a compact subsea separation unit. The compact structure of this unit makes advanced process control a requirement. The study of this thesis will focus on conguring a NMPC with state process information supplied by a nonlinear observer. The study will be based on previous work on the Compact Separation unit. Statoil's internal MPC tool will be used for process control. The state and parameter estimation performance of the implemented observers has been assessed with regards to both measurement noise and model/plant mismatches. This has been performed through simulations on the implemented model, but tests have also been conducted on an off-line data set from a test rig of the compact separation unit. The observers provided sufficiently accurate state estimates, with the exception being when the estimates were based on too severe measurement noise. The parameter estimation scheme proved to be suboptimal, but provided vital information during tests on the off-line data set. The NMPC conguration developed during this project has been tested on several process disturbances, and have provided good results regarding regulation of the process within the desired control objectives. The conguration have proved to fulfill the performance criteria specied, both with the use of ideal process information and estimates supplied by the observers

A Multibeam-Based SLAM Algorithm for Iceberg Mapping Using AUVs

Using autonomous underwater vehicles (AUVs) for mapping underwater topography of sea-ice and icebergs, or detecting keels of ice ridges, is foreseen as enabling technology in future Arctic marine operations. Wind, current, and Coriolis forces affect an iceberg’s trajectory, making automated mapping difficult. This paper presents a method aiming at enabling autonomous iceberg mapping using AUVs equipped with a multibeam echosounder by estimating the position and orientation of the iceberg. The method is based on a bathymetric simultaneous localization and mapping (SLAM) algorithm, namely the bathymetric distributed particle filter SLAM (BPSLAM) algorithm. The proposed method estimates the AUV’s pose in an iceberg-fixed coordinate system. The relative states can be used for both guiding the vehicle to achieve complete coverage, as well as estimation of a consistent iceberg topography. The algorithm also provides an estimate of the iceberg’s drift velocity – an important parameter for the AUV trajectory planning as well as any related ice management (IM) operations. Two new weighting algorithms for the BPSLAM method are proposed, enabling batch processing of multibeam echosounder (MBE) measurements to ensure real-time operation without discarding information. The proposed method is demonstrated using a real iceberg topography taken from the PERD iceberg sightings database, with simulated AUV and MBE range measurements. The algorithm is also evaluated on a real world bathymetric dataset, collected using the HUGIN HUS AUV

Using Autonomous Underwater Vehicles as Sensor Platforms for Ice-Monitoring

Due to the receding sea-ice extent in the Arctic, and the potentially large undiscovered petroleum resources present north of the Arctic circle, offshore activities in ice-infested waters are increasing. Due to the presence of drifting sea-ice and icebergs, ice management (IM) becomes an important part of the offshore operation, and an important part of an IM system is the ability to reliably monitor the ice conditions. An autonomous underwater vehicle (AUV) has a unique capability of high underwater spatial and temporal coverage, making it suitable for monitoring applications. Since the first Arctic AUV deployment in 1972, AUV technology has matured and has been used in complex under-ice operations. This paper motivates the use of AUVs as an ice-monitoring sensor platform. It discusses relevant sensor capabilities and challenges related to communication and navigation. This paper also presents experiences from a field campaign that took place in Ny-Aalesund at Svalbard in January 2014, where a REMUS 100 AUV was used for sea-floor mapping and collection of oceanographic parameters. Based on this, we discuss the experiences related to using AUVs for ice-monitoring. We conclude that AUVs are highly applicable for ice-monitoring, but further research is needed.(c) 2014 Norwegian Society of Automatic Control. Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. See: http://creativecommons.org/licenses/by/3.0/

A Multibeam-Based SLAM Algorithm for Iceberg Mapping Using AUVs

Using autonomous underwater vehicles (AUVs) for mapping underwater topography of sea-ice and icebergs, or detecting keels of ice ridges, is foreseen as enabling technology in future Arctic marine operations. Wind, current, and Coriolis forces affect an iceberg’s trajectory, making automated mapping difficult. This paper presents a method aiming at enabling autonomous iceberg mapping using AUVs equipped with a multibeam echosounder by estimating the position and orientation of the iceberg. The method is based on a bathymetric simultaneous localization and mapping (SLAM) algorithm, namely the bathymetric distributed particle filter SLAM (BPSLAM) algorithm. The proposed method estimates the AUV’s pose in an iceberg-fixed coordinate system. The relative states can be used for both guiding the vehicle to achieve complete coverage, as well as estimation of a consistent iceberg topography. The algorithm also provides an estimate of the iceberg’s drift velocity – an important parameter for the AUV trajectory planning as well as any related ice management (IM) operations. Two new weighting algorithms for the BPSLAM method are proposed, enabling batch processing of multibeam echosounder (MBE) measurements to ensure real-time operation without discarding information. The proposed method is demonstrated using a real iceberg topography taken from the PERD iceberg sightings database, with simulated AUV and MBE range measurements. The algorithm is also evaluated on a real world bathymetric dataset, collected using the HUGIN HUS AUV.publishedVersionCopyright 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission

Adaptive Sampling of Surface Fronts in the Arctic Using an Autonomous Underwater Vehicle

Fronts between Arctic- and Atlantic-origin waters are characterized by strong lateral gradients in temperature and salinity. Ocean processes associated with fronts are complex with considerable space and time variability. Therefore, resolving the processes in frontal zones by observation is challenging but important for understanding the associated physical–biological interactions and their impact on the marine ecosystem. The use of autonomous robotic vehicles and in situ data-driven sampling can help improve and augment the traditional sampling practices, such as ships and profiling instruments. Here, we present the development and results of using an autonomous agent for detection and sampling of an Arctic front, integrated on board an autonomous underwater vehicle. The agent is based on a subsumption architecture implemented as behaviors in a finite-state machine. Once a front is detected, the front tracking behavior uses observations to continuously adapt the path of the vehicle to perform transects across the front interface. Following successful sea trials in the Trondheimsfjord, the front-tracking agent was deployed to perform a full-scale mission near 82∘N north of Svalbard, close to the sea ice edge. The agent was able to detect and track an Arctic frontal feature, performing a total of six crossings while collecting vertical profiles in the upper 90 m of the water column. Measurements yield a detailed volumetric description of the frontal feature with high resolution along the frontal zone, augmenting ship-based sampling that was run in parallel