Adversarial image-to-image model to obtain highly detailed wind fields from mesoscale simulations in mountainous and urban areas

Autores/as

DOI:

https://doi.org/10.26754/jjii3a.202410582

Resumen

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.

Descargas

Los datos de descargas todavía no están disponibles.

Descargas

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