A parametric reduced order model based on proper orthogonal decomposition with Galerkin projection has been developed and applied for the modeling of heat transport in T-junction pipes which are widely found in nuclear power reactor cooling systems. Thermal mixing of different temperature coolants in T-junction pipes leads to temperature fluctuations and this could potentially cause thermal fatigue in the pipe walls. The novelty of this paper is the development of a parametric ROM considering the three dimensional, incompressible, unsteady Navier-Stokes equations coupled with the heat transport equation in a finite volume regime. Two different parametric cases are presented in this paper: parametrization of the inlet temperatures and parametrization of the kinematic viscosity. Different training spaces are considered and the results are compared against the full order model. The first test case results to a computational speed-up factor of 374 while the second test case to one of 211.

Parametric pod-galerkin model order reduction for unsteady-state heat transfer problems / Georgaka, S.; Stabile, G.; Rozza, G.; Bluck, M. J.. - In: COMMUNICATIONS IN COMPUTATIONAL PHYSICS. - ISSN 1991-7120. - 27:1(2020), pp. 1-32. [10.4208/cicp.OA-2018-0207]

Parametric pod-galerkin model order reduction for unsteady-state heat transfer problems

Stabile G.;Rozza G.;
2020

Abstract

A parametric reduced order model based on proper orthogonal decomposition with Galerkin projection has been developed and applied for the modeling of heat transport in T-junction pipes which are widely found in nuclear power reactor cooling systems. Thermal mixing of different temperature coolants in T-junction pipes leads to temperature fluctuations and this could potentially cause thermal fatigue in the pipe walls. The novelty of this paper is the development of a parametric ROM considering the three dimensional, incompressible, unsteady Navier-Stokes equations coupled with the heat transport equation in a finite volume regime. Two different parametric cases are presented in this paper: parametrization of the inlet temperatures and parametrization of the kinematic viscosity. Different training spaces are considered and the results are compared against the full order model. The first test case results to a computational speed-up factor of 374 while the second test case to one of 211.
COMMUNICATIONS IN COMPUTATIONAL PHYSICS
27
1
1
32
10.4208/cicp.OA-2018-0207
https://global-sci.org/intro/article_detail/cicp/13312.html
https://arxiv.org/abs/1808.05175v3
Georgaka, S.; Stabile, G.; Rozza, G.; Bluck, M. J.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11767/115081
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