TY - JOUR
T1 - Parametric POD-Galerkin Model Order Reduction for Unsteady-State Heat Transfer Problems
AU - Georgaka , Sokratia
AU - Stabile , Giovanni
AU - Rozza , Gianluigi
AU - J. Bluck , Michael
JO - Communications in Computational Physics
VL - 1
SP - 1
EP - 32
PY - 2019
DA - 2019/10
SN - 27
DO - http://doi.org/10.4208/cicp.OA-2018-0207
UR - https://global-sci.org/intro/article_detail/cicp/13312.html
KW - Proper orthogonal decomposition, finite volume approximation, Poisson equation for
pressure, inf-sup approximation, supremizer velocity space enrichment, Navier-Stokes equations.
AB - <p style="text-align: justify;">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.</p>