2024-03-28T13:30:50Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/541232022-07-20T19:01:05Zcom_10324_1191com_10324_931com_10324_894col_10324_1379
Abadía Heredia, R.
López Martín, Manuel
Carro Martínez, Belén
Arribas Sánchez, Juan Ignacio
Pérez, José Miguel
Le Clainche, Soledad
2022
Producción Científica
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)
application/pdf
https://uvadoc.uva.es/handle/10324/54123
eng
Elsevier
33 Ciencias Tecnológicas
12 Matemáticas
A predictive hybrid reduced order model based on proper orthogonal decomposition combined with deep learning architectures
info:eu-repo/semantics/article
TEXT
UVaDOC. Repositorio Documental de la Universidad de Valladolid
Hispana