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Multi-scale Unified Surrogates for Turbulence and Wake Interaction in Next-generation Digital Twins

Project description

Multi-scale digital twin for wind farm optimisation

Wind farm simulators overlook crucial fluid-structure interactions and wake turbulence, leading to inaccurate power output and fatigue load predictions. Supported by the Marie Skłodowska-Curie Actions programme, the MUSTWIND project will develop a multi-scale digital twin for next-generation wind farms, supporting Europe’s 2050 climate goals. It will use advanced simulations and machine learning to generate detailed datasets on wake turbulence and fluid-structure interactions. The initiative aims to enable predictive control of power output and maintenance while reducing operational costs. By combining high-performance computing with experimental validation, the project represents a significant step towards optimising wind energy and accelerating the renewable energy transition in Europe.

Objective

This project develops a groundbreaking multi-scale digital twin for next-generation wind farms, directly supporting Europe's 2050 climate neutrality goals under the Green Deal. Current wind farm simulators fail to capture critical fluid-structure interactions and wake turbulence dynamics, leading to poor predictions of power output and fatigue loads. This research addresses these limitations by integrating high-fidelity Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) with two-way fluid-structure interaction (FSI) coupling and machine learning.
The 24-month fellowship at Imperial College London's Turbulence Simulation Group, under Prof. Sylvain Laizet's supervision, pursues four key objectives: (1) generating high-resolution datasets of wake turbulence and FSI under misaligned, intermittent inflows; (2) characterising multi-scale physics through advanced diagnostics including vortex identification and fatigue analysis; (3) developing Bayesian-trained surrogate models for dynamic actuator-line corrections and reduced-order FSI coupling; and (4) integrating these surrogates into a farm-scale solver with real-time optimisation capabilities.
The methodology combines computational excellence with experimental validation, utilising European Tier-0 HPC facilities and wind tunnel data. Three interconnected work packages progress from high-fidelity data acquisition through physical analysis to surrogate model development, enabling predictive control of power output, fatigue loads, and maintenance scheduling.
Expected impacts span scientific advancement (first validated multi-scale wind farm digital twin), economic benefits (reduced O&M costs, improved LCOE), and societal gains (accelerated renewable energy transition, job creation). The project exemplifies interdisciplinary innovation, merging CFD, machine learning, and experimental validation to transform wind energy optimisation, positioning Europe at the forefront of digital twin technologies.

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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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Coordinator

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 260 347,92
Address
SOUTH KENSINGTON CAMPUS EXHIBITION ROAD
SW7 2AZ London
United Kingdom

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Region
London Inner London — West Westminster
Activity type
Higher or Secondary Education Establishments
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Total cost

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