Active Clean Energy Engineering

Digital twinning of next-generation massive-scale offshore metal wind support structures

In plain English

AI plain-English summary

Offshore wind towers are buckling in ways engineers cannot yet fully predict, and this Fellowship aims to build digital twins of them to find out why. The problem is that modern offshore wind support structures are enormous, thin-walled steel shells—essentially giant cans—whose behaviour under stress is poorly understood. Current design standards rely on conservative safety margins, which means using far more steel than might actually be needed. This Fellowship will create the first open-source computer models capable of simulating these structures’ complex, nonlinear buckling, then validate those models against real-world data from towers planned for construction in 2025. If successful, the research could slash the cost of offshore wind. A single tower saved from failure saves roughly £2 million, while a 10% reduction in steel across a 100-tower installation saves around £10 million—not counting carbon costs. The work will also produce the first terabyte-scale datasets of digital twin inputs, a new open-source solver, and a permanent data journal for the engineering community. The immediate impact is on infrastructure design and construction costs, not daily life directly—but cheaper offshore wind means cheaper renewable energy for everyone.

View original technical description
This Fellowship will enable the rigorous study of a fragile structural form that has long left the comfortable confines of the laboratory scale and is increasingly critical to our renewable energy independence. This Fellowship will, for the first ever time: develop open-source solvers for the high-performance simulation of structural systems with sharply nonlinear behaviour suffering from numerical deterioration in partnership with the Edinburgh Parallel Computing Centre (EPCC); develop protocols for the digital twinning of massive shell structures where the quality of the twinned midsurface is paramount and sub-mm geometric features can be critical, in partnership with reality capture specialists Leica Geosystems (LGS); gather the first terabyte-sized datasets of digital twin inputs representing state-of-the-art offshore wind support structures based on unprecedented access to facilities planned for construction starting in 2025 granted by project partners Siemens Gamesa Renewable Energy (SGRE), ScottishPower Renewables (SPR) and COWI; complete the scientific understanding of the nonlinear response of very long tubular structural forms prone to ovalisation phenomena; generate extensive datasets of synthetic buckling resistances of digitally-twinned shells; calibrate actual safety margins of current and future planned offshore wind support structures and disseminate this within the international Eurocode design framework; ('Plus') found a permanent indexed data journal to accumulate empirical and numerical dataset pairs for the wider computational engineering community to validate simulations used in research and safety-critical design. The open-source software development will push the boundaries of computational structural engineering and support an emerging research culture increasingly employing digital twinning. The financial benefits of quantifying actual safety margins of current and future-scale offshore wind support structures are significant: a single modern tower saved from failure saves ~£2M, while even a ~10% reduction in steel saves ~£10M across a 100-tower offshore installation (assuming ~£1k / tonne for structural steel, not including carbon cost).

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Researchers

Adam Jan Sadowski (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Multi-scale Unified Surrogates for Turbulence and Wake Interaction in Next-generation Digital Twins
New Phase Field Models for Unravelling Multi-Physics Material Degradation Challenges (NEWPHASE)
Structural life-cycle enhancement of next-generation onshore and offshore wind farms
Offshore Wind Foundation Diagnostic Monitoring System (OFDiMoS)
Advanced analysis of metal wind turbine support towers

Original classification

Fellowship

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