2022
DOI: 10.1051/m2an/2022044
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Driving bifurcating parametrized nonlinear PDEs by optimal control strategies: application to Navier–Stokes equations with model order reduction

Abstract: This work deals with optimal control problems as a strategy to drive bifurcating solution of nonlinear parametrized partial differential equations towards a desired branch. Indeed, for these governing equations, multiple solution configurations can arise from the same parametric instance. We thus aim at describing how optimal control allows to change the solution profile and the stability of state solution branches. First of all, a general framework for nonlinear optimal control problem is presented in order t… Show more

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Cited by 14 publications
(1 citation statement)
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“…Despite this, since the high‐fidelity models usually require unaffordable computational costs due to fine mesh discretization and repeated evaluations, we also focused on the application of reduced order models (ROM) 12‐17 . In particular, we employed the Reduced Basis (RB) method, 18,19 which has been already used to trace the bifurcating behavior of many problems in fluid‐dynamics, 20‐23 with further applications to other fields such as quantum mechanics, 24 computational mechanics 25 and bio‐mathematics 26 . This method is based on projecting the nonlinear governing equations on a reduced space of much lower dimension.…”
Section: Introductionmentioning
confidence: 99%
“…Despite this, since the high‐fidelity models usually require unaffordable computational costs due to fine mesh discretization and repeated evaluations, we also focused on the application of reduced order models (ROM) 12‐17 . In particular, we employed the Reduced Basis (RB) method, 18,19 which has been already used to trace the bifurcating behavior of many problems in fluid‐dynamics, 20‐23 with further applications to other fields such as quantum mechanics, 24 computational mechanics 25 and bio‐mathematics 26 . This method is based on projecting the nonlinear governing equations on a reduced space of much lower dimension.…”
Section: Introductionmentioning
confidence: 99%