Accelerating urban scale simulations leveraging local spatial 3D structure
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INVESTIGACIONMetadades
Títol
Accelerating urban scale simulations leveraging local spatial 3D structureAutoria
Data de publicació
2022-06-15Editor
ElsevierISSN
1877-7503Cita bibliogràfica
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.Tipus de document
info:eu-repo/semantics/articleVersió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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. [-]
Publicat a
Journal of Computational Science, 62 (2022) 101741Entitat finançadora
Generalitat Valenciana
Codi del projecte o subvenció
APOSTD/2020/026
Drets d'accés
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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