In the present paper we propose reduced order methods as a reliable strategy to efficiently solve parametrized optimal control problems governed by shallow waters equations in a solution tracking setting. The physical parametrized model we deal with is nonlinear and time dependent: this leads to very time consuming simulations which can be unbearable, e.g., in a marine environmental monitoring plan application. Our aim is to show how reduced order modelling could help in studying different configurations and phenomena in a fast way. After building the optimality system, we rely on a POD-Galerkin reduction in order to solve the optimal control problem in a low dimensional reduced space. The presented theoretical framework is actually suited to general nonlinear time dependent optimal control problems. The proposed methodology is finally tested with a numerical experiment: the reduced optimal control problem governed by shallow waters equations reproduces the desired velocity and height profiles faster than the standard model, still remaining accurate.

Strazzullo, M., Ballarin, F., Rozza, G., POD-Galerkin model order reduction for parametrized nonlinear time-dependent optimal flow control: An application to shallow water equations, <<JOURNAL OF NUMERICAL MATHEMATICS>>, 2022; 30 (1): 63-84. [doi:10.1515/jnma-2020-0098] [https://hdl.handle.net/10807/202828]

POD-Galerkin model order reduction for parametrized nonlinear time-dependent optimal flow control: An application to shallow water equations

Ballarin, Francesco;
2022

Abstract

In the present paper we propose reduced order methods as a reliable strategy to efficiently solve parametrized optimal control problems governed by shallow waters equations in a solution tracking setting. The physical parametrized model we deal with is nonlinear and time dependent: this leads to very time consuming simulations which can be unbearable, e.g., in a marine environmental monitoring plan application. Our aim is to show how reduced order modelling could help in studying different configurations and phenomena in a fast way. After building the optimality system, we rely on a POD-Galerkin reduction in order to solve the optimal control problem in a low dimensional reduced space. The presented theoretical framework is actually suited to general nonlinear time dependent optimal control problems. The proposed methodology is finally tested with a numerical experiment: the reduced optimal control problem governed by shallow waters equations reproduces the desired velocity and height profiles faster than the standard model, still remaining accurate.
2022
Inglese
Strazzullo, M., Ballarin, F., Rozza, G., POD-Galerkin model order reduction for parametrized nonlinear time-dependent optimal flow control: An application to shallow water equations, <<JOURNAL OF NUMERICAL MATHEMATICS>>, 2022; 30 (1): 63-84. [doi:10.1515/jnma-2020-0098] [https://hdl.handle.net/10807/202828]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/202828
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