Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/54123
Título
A predictive hybrid reduced order model based on proper orthogonal decomposition combined with deep learning architectures
Autor
Año del Documento
2022
Editorial
Elsevier
Descripción
Producción Científica
Documento Fuente
Expert Systems with Applications, 2022, vol. 187, p. 115910
Zusammenfassung
Solving computational fluid dynamics problems requires using large computational resources. The computa-
tional time and memory requirements to solve realistic problems vary from a few hours to several weeks with
several processors working in parallel. Motivated by the need of reducing such large amount of resources
(improving the industrial applications in which fluid dynamics plays a key role), this article introduces a new
predictive Reduced Order Model (ROM) applied to solve fluid dynamics problems. The model is based on
physical principles and combines modal decompositions with deep learning architectures. The hybrid ROM,
reduces the dimensionality of a database via proper orthogonal decomposition (POD), extracting the dominant
features leading the flow dynamics of the problem studied. The number of degrees of freedom are reduced
from hundred thousands spatial points describing the database to a few (20–100) POD modes. Firstly, POD
divides the spatio-temporal data into spatial modes and temporal coefficients (or temporal modes). Next, the
temporal coefficients are integrated in time using convolutional or recurrent neural networks. The temporal
evolution of the flow is approximated after combining the spatial modes with the new temporal coefficients
computed. The model is tested in two complex problems of fluid dynamics, the three-dimensional wake of a
circular cylinder and a synthetic jet. The hybrid ROM uses data from the initial transient stage of numerical
simulations to predict the temporally converged solution of the flow with high accuracy. The speed-up factor
comparing the time necessary to obtain the predicted solution using the hybrid ROM and the numerical solver
is ∼140–348 in the synthetic jet and ∼2897–3818 in the three dimensional cylinder wake. The robustness shown
in the results presented and the data-driven nature of this ROM, make it possible to extend its application to
other fields (i.e. video and language processing, robotics, finances)
Materias Unesco
33 Ciencias Tecnológicas
12 Matemáticas
Palabras Clave
Reduced order models
Deep learning architectures
POD
Modal decompositions
Neural networks
Fluid dynamics
ISSN
0957-4174
Revisión por pares
SI
Patrocinador
Ministerio de Ciencia, Innovación y Universidades Proyectos de I+D+i ‘‘Retos investigación’’, (grant RTI2018-098958- B-I00)
Ministerio de Ciencia e Innovación y el Fondo Europeo de Desarrollo Regionales (FEDER) (grant PID2020-114173RB-I00)
Ministerio de Ciencia e Innovación y el Fondo Europeo de Desarrollo Regionales (FEDER) (grant PID2020-114173RB-I00)
Version del Editor
Propietario de los Derechos
© 2021 The Author(s)
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
Aparece en las colecciones
Dateien zu dieser Ressource
![Atribución 4.0 Internacional](/themes/Mirage2//images/creativecommons/cc-by.png)