Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO117: Towards a global map of atmospheric boundary layer properties needed for accurate modelling of wind resource and turbine interactions
Miguel Fernandes, Senior Data Scientist, DNV
Abstract
Understanding site‑specific atmospheric conditions is essential for wind resource assessment. Temperature and turbulence profiles through and above the atmospheric boundary layer influence ambient flow modelling and, crucially, turbine interactions such as wakes and blockage. High‑resolution mesoscale‑to‑microscale model chains provide detailed boundary conditions for Computational Fluid Dynamics (CFD) simulations, but these workflows require extensive computation and are not standardized. Measurements to validate the predicted profiles are rarely available, making it difficult to scale analyses across large offshore development zones. Our study explores cost‑effective, scalable alternatives for defining atmospheric conditions for turbine interaction modelling. We test two fast approaches against a high‑resolution (1km) baseline model chain: (i) deriving profiles from ERA5 reanalysis data by estimating temperature and turbulence from pressure levels, and (ii) running low (27km) and mid (3km) resolution Weather Research and Forecasting (WRF) simulations. Vertical profiles are averaged per-stability class and per-direction, with stability classes defined using the Monin–Obukhov length and/or the Bulk Richardson number. The profiles are inputs to steady‑state turbine interaction models, including a machine‑learning surrogate of a CFD model, used to predict annual energy production and interaction losses. We evaluate differences in stability classification, key variables (e.g. hub‑height turbulence intensity, shear, and boundary layer height) and energy yield using derived conditions compared to a baseline. By quantifying trade‑offs between model fidelity, cost, and predictive accuracy, we aim to show that simplified, yet physically meaningful, atmospheric characterizations can support multi‑fidelity modelling pipelines. The insights could make advanced turbine interaction models more accessible for developers and operators, enabling consistent mapping of atmospheric conditions across offshore wind zones. We will present a new, initially free, open‑access API based tool developed by DNV for easily obtaining stability classifications at any location in the world from ERA5 data.
