z-logo
open-access-imgOpen Access
A Kohn-Vogelius formulation to detect an obstacle immersed in a fluid
Author(s) -
Fabien Caubet,
Marc Dambrine,
Djalil Kateb,
Chahnaz Zakia Timimoun
Publication year - 2013
Publication title -
inverse problems and imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.755
H-Index - 40
eISSN - 1930-8345
pISSN - 1930-8337
DOI - 10.3934/ipi.2013.7.123
Subject(s) - hessian matrix , mathematics , shape optimization , inverse problem , mathematical analysis , regularization (linguistics) , bounded function , compact space , boundary (topology) , physics , finite element method , computer science , artificial intelligence , thermodynamics
International audienceThe aim of our work is to reconstruct an inclusion ω immersed in a fluid flowing in a larger bounded domain Ω via a boundary measurement on ∂Ω. Here the fluid motion is assumed to be governed by the Stokes equations. We study the inverse problem of reconstructing ω thanks to the tools of shape optimization by minimizing a Kohn-Vogelius type cost functional. We first characterize the gradient of this cost functional in order to make a numerical resolution. Then, in order to study the stability of this problem, we give the expression of the shape Hessian. We show the compactness of the Riesz operator corresponding to this shape Hessian at a critical point which explains why the inverse problem is ill-posed. Therefore we need some regularization methods to solve numerically this problem. We illustrate those general results by some explicit calculus of the shape Hessian in some particular geometries. In particular, we solve explicitly the Stokes equations in a concentric annulus. Finally, we present some numerical simulations using a parametric method. © 2013 American Institute of Mathematical Sciences

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom