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

CORDIS · observation · Publication date unknown

s. 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 developme

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR