Title: AI-accelerated hydrodynamic-structural analysis of offshore wind turbine floaters

Main supervisor: Mojtaba Mokhtari

Co-supervisor: Bernt Johan Leira

Research Focus

My PhD research addresses the dynamic response and structural behaviour of flexible floating offshore wind turbine (FOWT) platforms subjected to combined environmental loading. As floating wind systems move into deeper waters and harsher environments, floater flexibility and nonlinear hydrodynamic effects become increasingly important.

The project develops a high-fidelity two-way CFD-FEM co-simulation framework to capture coupled hydrodynamic loads and structural deformation of floating wind turbine floaters. The hydrodynamic solver (CFD) is coupled with structural finite element modelling (FEM) to account for nonlinear fluid-structure interaction effects.

A key innovation of the project is the integration of machine learning techniques into the simulation workflow to accelerate computational performance while maintaining acceptable accuracy. The AI-accelerated framework aims to reduce the computational cost of high-fidelity simulations and enable more efficient design and analysis of floating wind systems.

The work includes:

• Development of coupled CFD-FEM models for flexible floaters
• Quantification of flexibility effects on motions, loads, and structural response
• Development of AI-based surrogate or hybrid models
• Comparative assessment against potential-theory-based methods
• Validation against experimental data