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Detecting an obstacle immersed in a fluid by shape optimization methods

机译:通过形状优化方法检测浸入液体中的障碍物

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The paper presents a theoretical study of an identification problem by shape optimization methods. The question is to detect an object immersed in a fluid. Here, the problem is modeled by the Stokes equations and treated as a nonlinear least-squares problem. We consider both the Dirichlet and Neumann boundary conditions. Firstly, we prove an identifiability result. Secondly, we prove the existence of the first-order shape derivatives of the state, we characterize them and deduce the gradient of the least-squares functional. Moreover, we study the stability of this setting. We prove the existence of the second-order shape derivatives and we give the expression of the shape Hessian. Finally, the compactness of the Riesz operator corresponding to this shape Hessian is shown and the ill-posedness of the identification problem follows. This explains the need of regularization to numerically solve this problem.
机译:本文提出了一种基于形状优化方法的识别问题的理论研究。问题是要检测浸没在液体中的物体。在这里,该问题由斯托克斯方程建模,并被视为非线性最小二乘问题。我们同时考虑了Dirichlet和Neumann边界条件。首先,我们证明了可识别性结果。其次,我们证明了状态的一阶形状导数的存在,我们对其进行了刻画并推导出最小二乘函数的梯度。此外,我们研究了此设置的稳定性。我们证明了二阶形状导数的存在,并给出了形状Hessian的表达式。最后,显示了对应于该形状Hessian的Riesz算子的紧致性,并且随之出现了识别问题的不适定性。这解释了需要正规化以数字方式解决此问题。

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