Accelerating urban scale simulations leveraging local spatial 3D structure
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
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INVESTIGACIONMetadata
Title
Accelerating urban scale simulations leveraging local spatial 3D structureAuthor (s)
Date
2022-06-15Publisher
ElsevierISSN
1877-7503Bibliographic citation
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.Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
Is part of
Journal of Computational Science, 62 (2022) 101741Funder Name
Generalitat Valenciana
Project code
APOSTD/2020/026
Rights
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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