Designing stronger, smarter, more sustainable ships

A view from the deck of a ship looking out over a calm, dark blue ocean dotted with small pieces of floating ice under a clear sky

MASI is Western Canada’s centre of excellence in marine, aerospace, subsea and naval systems, anchored at the University of British Columbia. MASI is establishing Canada’s first Pacific–Arctic hub for world‑leading research, innovation and training that supports national sovereignty, security and sustainable blue‑economy growth.

The tools we use to design, build and maintain our ships have not kept pace with the demands facing modern fleets. Vessels must meet increasingly stringent criteria for emissions, operational efficiency, sustainability, safety, durability and more. Advanced materials and new structural approaches offer potential solutions, but being able to evaluate these options and understand their combined and interrelated impacts across the life cycle remains a challenge.

This gap is the focus of Dr. Jasmin Jelovica’s research, whose work examines how new materials, structural concepts, and high‑fidelity simulation tools can reshape the way ships are designed and sustained. By combining advanced computation, machine learning and experimental insights, Dr. Jelovica aims to create a future where ships are lighter, stronger, more sustainable and supported by digital tools that make long‑term performance far more predictable.

Dr. Jelovica is an Associate Professor in Mechanical Engineering, and from 2020 to 2025 was the NSERC/Seaspan Industrial Research Chair in Intelligent and Green Marine Vessels. He is also the Principal Investigator of the Laboratory for Structural Efficiency.

Tell us about the structural design and materials group within MASI

Within the MASI cluster, the structural design and materials research group is focusing on the structures, materials and analysis methods that support the design and operation of ships that are more sustainable, stronger and more resilient. The group is working to incorporate performance criteria that have historically not been considered due to the high computational costs of advanced analysis methods.

The team brings together people with expertise in a wide range of areas, including design, computation and physical experimentation.

Their work ranges from laboratory testing that reveals the behaviour of materials and structural components to field measurements that can be translated into computational models for design, operation and maintenance.

On the materials side, researchers are examining advanced materials and hybrid systems – such as composites combined with steel – to understand how they perform under dynamic loads, blast loads and other impacts.

We are examining material behaviour from the nano scale to larger systems, such as structural components, as well as the entire vessel.

Where does your research fit into all this?

My work has primarily focused on design, but I am interested in expanding into operational and maintenance aspects. Traditionally, ship design has relied on numerical methods that offer powerful insight but are limited because they are computationally heavy. These limitations don’t allow us to optimize design – it could literally take years for a proper assessment. It means that many tools are only used late in the design process for validation, at which point there is little room to make meaningful changes.

We want to bring that level of accuracy into the earlier stages of the design process by using machine learning, parallel computing and other methods.

This would enable us to understand how new structures and materials behave under complicated conditions and loadings, including how they corrode, degrade or fail over time.

My interests focus on modelling response and failure of ship structures. This involves a range of approaches that I am advancing with my research group, from reduced-order models for structural analysis, structural design though topology optimization and GenAI, and data-driven physics-informed models.

My work also has relevance on the production side. For example, we simulate how advanced welding methods impact residual stresses, geometrical imperfections and heat-affected zones around welds. This research supports shipyards in planning and optimizing their production processes. When combined with digital twins and advanced sensors, these tools could help extend the life of current assets and support ship owners in their decision making.

Much of my research – particularly that conducted with my colleague Dr. Rajeev Jaiman through a number of funding sources – also involves finding ways to reduce underwater noise associated with shipping. We are developing decision-support tools with community stakeholders to promote safer shipping through the Arctic.

How is this research transferred to industry?

Some of this knowledge can be transferred relatively quickly through workshops and training. A major pathway for knowledge transfer is through our students – master’s, PhD and postdoctoral researchers – who apply their expertise and methods directly into industry roles.

When it comes to introducing new materials and structures, the timelines for impact are much longer because new designs must go through lengthy certification processes.

These new tools should enable designers and shipyards to do more – and to do it faster. It will be possible to incorporate additional criteria into early-stage design and explore other ways to make ships more efficient or resilient, such as by reducing weight by optimizing the structure in ways that we are not doing now.

Why did you want to get involved with MASI?

We already have a strong team at UBC with overlapping and complementary expertise. MASI builds on that and will enable us to attract more interest from industry and potential partners – locally, nationally and internationally.

UBC is one of the few centres in the world with deep expertise in marine technology and it is one of the best places in the work to conduct research related to sustainability goals, including engagement with Indigenous communities. We are also drawing on UBC’s world-leading expertise in AI and its computational clusters where we can integrate mechanics, material degradation and environmental effects into next-generation models.