In this work we provide a combination of isogeometric analysis with reduced order modelling techniques, based on proper orthogonal decomposition, to guarantee computational reduction for the numerical model, and with free-form deformation, for versatile geometrical parametrization. We apply it to computational fluid dynamics problems considering a Stokes flow model. The proposed reduced order model combines efficient shape deformation and accurate and stable velocity and pressure approximation for incompressible viscous flows, computed with a reduced order method. Efficient offline–online computational decomposition is guaranteed in view of repetitive calculations for parametric design and optimization problems. Numerical test cases show the efficiency and accuracy of the proposed reduced order model. © 2016 The Author(s).
Isogeometric analysis-based reduced order modelling for incompressible linear viscous flows in parametrized shapes / Salmoiraghi, F.; Ballarin, Francesco; Heltai, Luca; Rozza, Gianluigi. - In: ADVANCED MODELING AND SIMULATION IN ENGINEERING SCIENCES. - ISSN 2213-7467. - 3:1(2016), pp. 1-22. [10.1186/s40323-016-0076-6]
Isogeometric analysis-based reduced order modelling for incompressible linear viscous flows in parametrized shapes
BALLARIN, FRANCESCO;Heltai, Luca;Rozza, Gianluigi
2016-01-01
Abstract
In this work we provide a combination of isogeometric analysis with reduced order modelling techniques, based on proper orthogonal decomposition, to guarantee computational reduction for the numerical model, and with free-form deformation, for versatile geometrical parametrization. We apply it to computational fluid dynamics problems considering a Stokes flow model. The proposed reduced order model combines efficient shape deformation and accurate and stable velocity and pressure approximation for incompressible viscous flows, computed with a reduced order method. Efficient offline–online computational decomposition is guaranteed in view of repetitive calculations for parametric design and optimization problems. Numerical test cases show the efficiency and accuracy of the proposed reduced order model. © 2016 The Author(s).File | Dimensione | Formato | |
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