Posters - WindEurope Annual Event 2024

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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 the academic community. We look forward to seeing you there!

PO205: Site specific CFD fine tuning of forest and thermal effects & comparison with other flow models.

Giorgio Crasto, Senior Engineer at the Technology Excellence Tools of the Wind-CoE, Enel Green Power


The present study describes a way to take advantage of the forest and thermal modelling capabilities of CFD RANS model to improve accuracy and keep processing time into reasonable limits. Results obtained are compared with other flow models typically used in wind farm development and optimization. For the study purposes a complex terrain site has been selected characterized by a system of ridges, escarpments and valleys covered with a significant presence of a tall forest. Five met masts have been erected at significant locations, generally at peaks of hills. The CFD simulations were run with WindSim, which makes use of the general purpose CFD solver PHOENICS to integrate the RANS equations. The pressure-velocity coupling is the General Collocated Velocity (GCV) method, like the SIMPLEC algorithm. Turbulence is closed with the RNG k-epsilon model. The forest has been modelled in the CFD with and without a canopy model, with cells within the canopy exerting drag forces proportional to the squared wind speed. A further comparison is applied between CFD runs with and without accounting for thermal effects by means of the MOL (Monin-Obukhov Length). The MOL selected for each directional sector is obtained with a novel classification methodology. The goodness of the simulations is evaluated through a wind speed cross-prediction analysis using a RMSE (Root Mean Squared Error) and a MAE (Mean Absolute Error) as metrics. The results of the four parametrizations of the CFD model are compared with those coming from a WRF (Weather Research and Forecasting) model coupled with a microscale model, and a single microscale model. The comparison allowed to identify the most accurate simulation and, for the CFD model, the optimal settings of the forest modelling and the MOL classification.

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