Adversarial image-to-image model to obtain highly detailed wind fields from mesoscale simulations in mountainous and urban areas
DOI:
https://doi.org/10.26754/jjii3a.202410582Resumen
The characterisation of wind is of great interest in multiple disciplines such as city planning, pedestrian comfort and energy generation. We propose a conditional Generative Adversarial Network (cGAN), based on the Pix2Pix model [1], that can generate detailed local wind fields in areas with complex orography or an urban layout, which are comparable in level of detail to those from Computational Fluid Dynamics (CFD) simulations, from coarser Numerical Weather Prediction (NWP) data.
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Publicado
2024-07-17
Cómo citar
Milla Val, J., Montañés, C., & Fueyo, N. (2024). Adversarial image-to-image model to obtain highly detailed wind fields from mesoscale simulations in mountainous and urban areas. Jornada De Jóvenes Investigadores Del I3A, 12. https://doi.org/10.26754/jjii3a.202410582
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Artículos (Tecnologías Industriales)
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Derechos de autor 2024 Jaime Milla Val, Carlos Montañés, Norberto Fueyo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.