Project description
Revolutionising deep water offshore wind mooring design
Technological advancements and efforts to achieve green transition goals have made offshore wind solutions essential. However, mooring systems remain a major cost driver, currently relying on conservative rules and trial-and-error methods that fail to account for non-linear, climate-sensitive loads. Supported by the Marie Skłodowska-Curie Actions, FLOW-Moor addresses these barriers by designing an AI-driven optimisation methodology. By combining deep-learning surrogates with frequency-domain screening, the project enables rapid, high-fidelity assessments of extreme environmental scenarios. The project will produce an open-source toolbox validated against industrial cases, providing a decision framework for safe carbon-efficient mooring layouts. Ultimately, FLOW-Moor accelerates deployment and reduces life-cycle costs, positioning the EU as a leader in offshore engineering and climate-resilient energy.
Objective
The European Strategic Energy Technology Plan prioritises offshore wind to reach climate neutrality by 2050. With most shallow sites developed, deployment is shifting to deeper waters where floating turbines are essential and many factors jointly shape cost, reliability, and environmental impact. Mooring systems are a major driver, yet practice still relies on conservative rules and trial-and-error, while nonlinear, climate-sensitive loads and evolving meteorological and oceanographic conditions add uncertainty. This keeps costs high, complicates certification, and slows deployment.
FLOW-Moor will deliver a climate-resilient, AI-driven optimisation methodology to facilitate a paradigm shift in mooring design. The primary outcome is a validated design and decision framework that produces safe, cost-efficient, and carbon-efficient layouts, explicitly engineered to withstand extreme environmental conditions under climate-change scenarios. To overcome fragmented workflows, prohibitive simulation cost, and limited adaptation to climate risk, the project combines fast frequency-domain screening with deep-learning surrogates inside a standards-aware, multi-objective optimiser. This approach broadens the design space, accelerates high-fidelity assessment, and keeps results auditable against certification criteria. Performance will be demonstrated on representative industrial cases by benchmarking against common single- and shared-mooring configurations, evidencing higher safety margins, lower cost, and reduced life-cycle carbon.
FLOW-Moor will release an open-source toolbox with a graphical user interface and reproducible examples. The fellowship is hosted at the University of Agder, with a secondment at SEMAR AS and a placement at NORCE to test the methodology on real design problems and support transfer to practice. The project strengthens the researcher’s independent leadership at the interface of offshore engineering and AI.
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Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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(opens in new window) HORIZON-MSCA-2025-PF
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4604 Kristiansand
Norway
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