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Marine Technology and Ocean Engineering

Through this research theme, we aim to reduce emissions and noise from marine vessels, increase coastal climate adaptation, and ensure responsible Arctic shipping and security.

We are pioneering next-generation marine technologies through innovative ship hull designs, hybrid propulsion systems, and bio-inspired sensors for Arctic and coastal monitoring. These innovations are supported by advanced digital twin and machine learning models that enhance vessel efficiency and reduce ecological impacts. We are also developing state-of-the-art marine power systems that integrate renewable energy sources and use sensor-based optimization for improved performance.

Our advancements in marine robotics will redefine adaptive environmental monitoring of shipping impacts through modular, AI-enabled autonomous vehicles. Miniature sensors will further transform ocean and coastal monitoring by leveraging real-time analytics to optimize sustainable marine resource management practices and inform data-driven regulatory decisions.

Our designs also address the unique challenges of Arctic shipping, balancing economic opportunities with environmental protection and Indigenous interests. By integrating sensor technologies with coastal system models and community-based monitoring, we aim to support the development of transformative solutions for climate adaptation, disaster risk reduction, security, sustainability, and climate mitigation in coastal regions. We are also enhancing coastal process models to better manage the impacts of coastal hazards.

Key research topics: Ocean dynamics, digital twins, AI-enabled models of marine/coastal systems, sensing and monitoring, hybrid propulsion, intelligent vessel design & control, and coastal systems modeling

Researchers


Related Publications



Title Publication Date Authors
MUTE-DSS: A Digital-Twin-Based Decision Support System for Minimizing Underwater Radiated Noise in Ship Voyage Planning
Cavitating wake dynamics and hydroacoustics performance of marine propeller with a nozzle
A multi-objective optimization framework for reducing the impact of ship noise on marine mammals
Predicting transmission loss in underwater acoustics using convolutional recurrent autoencoder network

First Nations land acknowledegement

The PRISMS research excellence cluster acknowledges that our work takes place on the traditional territories of the Musqueam, Squamish and Tsleil-Waututh, and in the traditional, ancestral, unceded territory of the Syilx Okanagan Nation and their peoples.


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