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Multiobjective optimal control methods for the Navier-Stokes equations using reduced order modeling. (English) Zbl 1415.76221

Summary: In a wide range of applications it is desirable to optimally control a dynamical system with respect to concurrent, potentially competing goals. This gives rise to a multiobjective optimal control problem where, instead of computing a single optimal solution, the set of optimal compromises, the so-called Pareto set, has to be approximated. When the problem under consideration is described by a partial differential equation (PDE), as is the case for fluid flow, the computational cost rapidly increases and makes its direct treatment infeasible. Reduced order modeling is a very popular method to reduce the computational cost, in particular in a multi query context such as uncertainty quantification, parameter estimation or optimization. In this article, we show how to combine reduced order modeling and multiobjective optimal control techniques in order to efficiently solve multiobjective optimal control problems constrained by PDEs. We consider a global, derivative free optimization method as well as a local, gradient-based approach for which the optimality system is derived in two different ways. The methods are compared with regard to the solution quality as well as the computational effort and they are illustrated using the example of the flow around a cylinder and a backward-facing-step channel flow.

MSC:

76D55 Flow control and optimization for incompressible viscous fluids
35Q30 Navier-Stokes equations
49K20 Optimality conditions for problems involving partial differential equations
49M05 Numerical methods based on necessary conditions
90C29 Multi-objective and goal programming
93C20 Control/observation systems governed by partial differential equations

Software:

FEniCS; SyFi; OpenFOAM
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Full Text: DOI arXiv

References:

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