Accelerating urban scale simulations leveraging local spatial 3D structure
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Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
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Título
Accelerating urban scale simulations leveraging local spatial 3D structureAutoría
Fecha de publicación
2022-06-15Editor
ElsevierISSN
1877-7503Cita bibliográfica
Iserte, S., Macías, A., Martínez-Cuenca, R., Chiva, S., Paredes, R., & Quintana-Ortí, E. S. (2022). Accelerating urban scale simulations leveraging local spatial 3D structure. Journal of Computational Science, 101741.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This paper presents a hybrid methodology for accelerating Computational Fluid Dynamics (CFD) simulations intertwining inferences from deep neural networks (DNN). The strategy leverages the local spatial data of the ... [+]
This paper presents a hybrid methodology for accelerating Computational Fluid Dynamics (CFD) simulations intertwining inferences from deep neural networks (DNN). The strategy leverages the local spatial data of the velocity field to leverage three-dimensional convolutional kernels within DNN. The hybrid workflow is composed of two-step cycles where CFD solvers calculations are utilized to feed predictive models, whose inferences, in turn, accelerate the simulation of the fluid evolution compared with traditional CFD. This approach has proved to reduce 30% time-to-solution in an urban scale study case, which leads to generating massive datasets at a fraction of the cost. [-]
Publicado en
Journal of Computational Science, 62 (2022) 101741Entidad financiadora
Generalitat Valenciana
Código del proyecto o subvención
APOSTD/2020/026
Derechos de acceso
1877-7503/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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