Grant: Alliance Grants
Memorial University of Newfoundland
$168,690.00
Characterization and delineation of oil-in-water at the Scott Inlet seeps through robotic autonomous underwater vehicle technology
EF 2023-24
Résumé
Increased risks of oil spills associated with the offshore oil and gas as well as shipping operations in the northern Atlantic and Arctic oceans of Canada call for advanced technologies for subsurface data collection. Autonomous underwater vehicle (AUV) technology is proposed in this project to help understand oil plume behavior and improve the efficiency of Canada's oil spill response. High-resolution subsurface data provided by AUVs has proven to be more suitable for searching and delineating patchy oil plumes compared with other traditional survey methods. This project aims to address gaps in current AUV technology by considering the realistic behaviour of oil in water and the time-critical nature of disaster response. The project will develop an innovative underwater data collection strategy for AUVs, based on an adaptive sampling approach. Using this approach, an AUV autonomously modifies its mission in real-time based on features of the plumes detected by on-board sensors, resulting in a path concentrated nearby or within the plume and in information-rich areas identified by the sensors. To date, AUV and oil sensing technologies specific to the characteristics of underwater oil spills are still under development. Only a few experiments were done in real oil spill conditions; most in laboratory and mesoscale experiments or with oil proxies, which significantly limits the ability to understand and capture the complexity found in the field. Hence, another key feature of our project is to test our oil detection methods in real oil-in-water ocean environments through field trials near Baffin Island where oil is naturally present in the water. Our project, in partnership with Canadian AUV operators and manufacturers, will result in a substantial step forward in delineation of subsurface oil plumes using AUVs, providing a significant improvement in Canada's oil spill response and underwater artificial intelligence capacity, as well as enhanced knowledge of marine oil contaminants and monitoring.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Newfoundland and Labrador, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
561516-2020
Name nom
Bose, Neil N
Department département
Engineering and Applied Science, Faculty of
Organization id
89
Institution établissement
Memorial University of Newfoundland
Province en
Newfoundland and Labrador
Province fr
Terre-Neuve-et-Labrador
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2020
Award amount
168690
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
402
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Oceans, seas and estuaries
Area of application fr
Océans, mers et estuaires
Research subject code
1501
Research subject group en
Environmental engineering
Research subject group fr
Water quality, pollution
Research subject en
Génie de l'environnement
Research subject fr
Qualité de l'eau, pollution
Application title
Characterization and delineation of oil-in-water at the Scott Inlet seeps through robotic autonomous underwater vehicle technology
Keyword
Artificial intelligence; Autonomous underwater vehicle; AUV; Marine pollution; Marine robotics; Oceanography; Oil detection; Oil spill; Underwater robotics
Application summary
Increased risks of oil spills associated with the offshore oil and gas as well as shipping operations in the northern Atlantic and Arctic oceans of Canada call for advanced technologies for subsurface data collection. Autonomous underwater vehicle (AUV) technology is proposed in this project to help understand oil plume behavior and improve the efficiency of Canada's oil spill response. High-resolution subsurface data provided by AUVs has proven to be more suitable for searching and delineating patchy oil plumes compared with other traditional survey methods. This project aims to address gaps in current AUV technology by considering the realistic behaviour of oil in water and the time-critical nature of disaster response. The project will develop an innovative underwater data collection strategy for AUVs, based on an adaptive sampling approach. Using this approach, an AUV autonomously modifies its mission in real-time based on features of the plumes detected by on-board sensors, resulting in a path concentrated nearby or within the plume and in information-rich areas identified by the sensors. To date, AUV and oil sensing technologies specific to the characteristics of underwater oil spills are still under development. Only a few experiments were done in real oil spill conditions; most in laboratory and mesoscale experiments or with oil proxies, which significantly limits the ability to understand and capture the complexity found in the field. Hence, another key feature of our project is to test our oil detection methods in real oil-in-water ocean environments through field trials near Baffin Island where oil is naturally present in the water. Our project, in partnership with Canadian AUV operators and manufacturers, will result in a substantial step forward in delineation of subsurface oil plumes using AUVs, providing a significant improvement in Canada's oil spill response and underwater artificial intelligence capacity, as well as enhanced knowledge of marine oil contaminants and monitoring.
