The main idea is to develop theoretical approaches and methodology utilizing the potential of using AI in combination with classical hydrodynamics to develop more accurate numerical models for ship performance, including both energy and motion characteristics.
Traditional hydrodynamic models are based on first-principle, reflecting physical laws. AI-based models which uses operational data are used to establish a statistical relationship between inputs and outputs. Both approaches have their pros and cons, and we aim to fuse the best of both approaches and develop hybrid models as input to voyage planning and onboard decision support systems.
Deep Sea and Ferry Operations
As a starting point, we will gather user requirements from the industrial partners focusing on two selected use cases; deepsea and ferry operations. The research activities will cover data analytics, AI and ship hydrodynamics and voyage optimization and decision support.
Relevant research topics involve comparing the different approaches for estimating ship performance, considering accuracies and valid usage of the models and evaluation of sensitivity and applicability of the models. Finally, the potential impact of using hybrid models in voyage optimization will be investigated and demonstrated.
A PhD candidate will be educated from the NTNU with focus on fusing AI with ship hydrodynamics.
Topics within FUSE:
• Models for ship performance
• Hybrid models that combine AI and hydrodynamics
• Voyage optimization
• Energy efficiency in the maritime transport
