Shape optimization in $W^{1,\infty}$ with geometric constraints: a study in distributed-memory systems
In this paper we present a shape optimization scheme which utilizes the alternating direction method of multipliers (ADMM) to approximate a direction of steepest descent in $W^{1,\infty}$. The followed strategy is a combination of the approaches presented in Deckelnick, Herbert, and Hinze, ESAIM: COCV 28 (2022) and Müller et al. SIAM SISC 45 (2023). This has appeared previously for relatively simple elliptic PDEs with geometric constraints which were handled using an ad-hoc projection. Here, however, the optimization problem is expanded to include geometric constraints, which are systematically fulfilled. Moreover, this results in a nonlinear system of equations, which is challenging from a computational perspective. Simulations of a fluid dynamics case study are carried out to benchmark the novel method. Results are given to show that, compared to other methods, the proposed methodology allows for larger deformations without affecting the convergence of the used numerical methods. The mesh quality is studied across the surface of the optimized obstacle, and is further compared to previous approaches which used descents in $W^{p,\infty}$. The parallel scalability is tested on a distributed-memory system to illustrate the potential of the proposed techniques in a more complex, industrial setting.