About the project
Objective
The project will develop a novel class of data-driven reduced-order models (ROM) that can represent wind farm flow dynamics with high-level accuracy, while being fast enough to support operational run-time analyses. The central aim is to bridge the gap between detailed computational fluid dynamics (CFD) simulations and the simpler models typically used in operational contexts, by employing CFD data to develop and train new machine-learning based models. The research will follow a modular and progressive strategy, starting from single turbine wake representation and then extending to farm-level interactions modeling.
Background
Wind farms operate in atmospheric conditions that vary across a wide range of spatial and temporal scales. At the farm scale, wakes develop and interact in ways that are difficult to capture with standard superimposition-based engineering models, especially if the site includes strong dependencies on terrain complexities, stability-driven variability, or farm-scale phenomena such as global blockage, which can contribute to systematic misprediction in terms of power forecasting and operating strategies.
High-fidelity CFD, for example large-eddy simulation, can capture these interactions at wind-farm scale, but the computational cost makes it impractical for real-time monitoring and frequent predictive analyses. By contrast, existing ROMs are often built on semi-empirical or engineering approximations that represent wakes through superposition of velocity deficits, deflections, and added turbulence. Although computationally efficient, these models often fail to capture complex wake-wake interactions, terrain-induced flow effects, stability dependent variability, and farm-scale phenomena such as blockage. Moreover, they are rarely designed to ingest real operational data, such as supervisory control and data acquisition (SCADA) logs, which encode the actual operating states of a wind farm. This motivates the need to bridge the gap between accurate but expensive simulations and fast but less reliable wake models, enabling near-real-time representations that can support forecasting, optimization, and future control strategies.
About the Digital Futures Postdoc Fellow
Filippo De Girolamo is a mechanical engineer and researcher working at the intersection of computational fluid dynamics and machine learning for wind energy applications. He holds a PhD in Energy and Environment from Sapienza University of Rome in Italy, with research focused on wind turbine flows and data-driven modeling of wake dynamics in offshore environments. He has developed experience across multiple wind energy problems, including wake and turbulence modeling with Large-Eddy Simulation, data-driven wake decomposition via unsupervised learning, and SCADA-based diagnostics for wind farm monitoring. He also carried out a research visit at the University of Texas at Dallas, working on high-fidelity simulations of real wind farms in complex terrain.
Main supervisor
Prof. Dan Henningson, KTH
Co-supervisor
Prof. Hedvig Kjellström, KTH
