TY - JOUR
T1 - An optimisation–based domain–decomposition reduced order model for the incompressible Navier-Stokes equations
AU - Prusak, Ivan
AU - Nonino, Monica
AU - Torlo, Davide
AU - Ballarin, Francesco
AU - Rozza, Gianluigi
PY - 2023
Y1 - 2023
N2 - The aim of this work is to present a model reduction technique in the framework of optimal control problems
for partial differential equations. We combine two approaches used for reducing the computational cost of the
mathematical numerical models: domain–decomposition (DD) methods and reduced–order modelling (ROM). In
particular, we consider an optimisation–based domain–decomposition algorithm for the parameter–dependent
stationary incompressible Navier–Stokes equations. Firstly, the problem is described on the subdomains coupled
at the interface and solved through an optimal control problem, which leads to the complete separation of the
subdomain problems in the DD method. On top of that, a reduced model for the obtained optimal–control
problem is built; the procedure is based on the Proper Orthogonal Decomposition technique and a further
Galerkin projection. The presented methodology is tested on two fluid dynamics benchmarks: the stationary
backward–facing step and lid-driven cavity flow. The numerical tests show a significant reduction of the
computational costs in terms of both the problem dimensions and the number of optimisation iterations in
the domain–decomposition algorithm.
AB - The aim of this work is to present a model reduction technique in the framework of optimal control problems
for partial differential equations. We combine two approaches used for reducing the computational cost of the
mathematical numerical models: domain–decomposition (DD) methods and reduced–order modelling (ROM). In
particular, we consider an optimisation–based domain–decomposition algorithm for the parameter–dependent
stationary incompressible Navier–Stokes equations. Firstly, the problem is described on the subdomains coupled
at the interface and solved through an optimal control problem, which leads to the complete separation of the
subdomain problems in the DD method. On top of that, a reduced model for the obtained optimal–control
problem is built; the procedure is based on the Proper Orthogonal Decomposition technique and a further
Galerkin projection. The presented methodology is tested on two fluid dynamics benchmarks: the stationary
backward–facing step and lid-driven cavity flow. The numerical tests show a significant reduction of the
computational costs in terms of both the problem dimensions and the number of optimisation iterations in
the domain–decomposition algorithm.
KW - Computational fluid dynamics
KW - Domain decomposition
KW - Optimal control
KW - Proper Orthogonal Decomposition
KW - Reduced order modelling
KW - Computational fluid dynamics
KW - Domain decomposition
KW - Optimal control
KW - Proper Orthogonal Decomposition
KW - Reduced order modelling
UR - http://hdl.handle.net/10807/253154
U2 - 10.1016/j.camwa.2023.09.039
DO - 10.1016/j.camwa.2023.09.039
M3 - Article
SN - 0898-1221
VL - 151
SP - 172
EP - 189
JO - COMPUTERS & MATHEMATICS WITH APPLICATIONS
JF - COMPUTERS & MATHEMATICS WITH APPLICATIONS
ER -