An inverse problem formulation of the immersed‐boundary method. (8th February 2020)

• Record Type: Journal Article
• Authors: Yan, Jianfeng; Hicken, Jason E.
• Is Part Of: International journal for numerical methods in fluids. Volume 92:Number 9(2020)
• Page Start: 1037
• Page End: 1057
• Publication Date: 2020-02-08
• Subjects: immersed‐boundary method -- inverse problem -- PDE‐constrained optimization; Fluid dynamics -- Mathematics -- Periodicals; 532
• DOI: 10.1002/fld.4816
• Languages: English
• ISSNs: 0271-2091

Abstract

Abstract: We formulate the immersed‐boundary method (IBM) as an inverse problem. A control variable is introduced on the boundary of a larger domain that encompasses the target domain. The optimal control is the one that minimizes the mismatch between the state and the desired boundary value along the immersed target‐domain boundary. We begin by investigating a naïve problem formulation that we show is ill‐posed: in the case of the Laplace equation, we prove that the solution is unique, but it fails to depend continuously on the data; for the linear advection equation, even solution uniqueness fails to hold. These issues are addressed by two complimentary strategies. The first strategy is to ensure that the enclosing domain tends to the true domain, as the mesh is refined. The second strategy is to include a specialized parameter‐free regularization that is based on penalizing the difference between the control and the state on the boundary. The proposed inverse IBM is applied to the diffusion, advection, and advection‐diffusion equations using a high‐order discontinuous Galerkin discretization. The numerical experiments demonstrate that the regularized scheme achieves optimal rates of convergence and that the reduced Hessian of the optimization problem has a bounded condition number, as the mesh is refined. Abstract : We formulate the immersed‐boundary method as an inverse problem, in which a control variable is introduced on the boundary of the computational domain to minimize the mismatch between the state and the desired boundary value. The inverse problem is stabilized by including a specialized parameter‐free regularization. The method is applied to the diffusion, advection, and advection‐diffusion equations, and the numerical experiments show optimal accuracy and bounded condition number of the reduced Hessian of the optimization problem.