What Lies Beneath the Surface: Topological-Shape Optimization With the Kernel-Independent Fast Multipole Method
For engineers and researchers in computational design, this method offers a highly efficient and scalable approach to topology optimization, though it is an incremental improvement over existing boundary element methods.
The paper introduces a topology-shape optimization method using boundary integral formulation and kernel-independent fast multipole method, achieving excellent single-node performance and scalable parallelization. It demonstrates effectiveness on minimum compliance and metamaterial microstructure optimization.
The paper presents a new method for shape and topology optimization based on an efficient and scalable boundary integral formulation for elasticity. To optimize topology, our approach uses iterative extraction of isosurfaces of a topological derivative. The numerical solution of the elasticity boundary value problem at every iteration is performed with the boundary element formulation and the kernel-independent fast multipole method. Providing excellent single node performance, scalable parallelization and the best available asymptotic complexity, our method is among the fastest optimization tools available today. The performance of our approach is studied on few illustrative examples, including the optimization of engineered constructions for the minimum compliance and the optimization of the microstructure of a metamaterial for the desired macroscopic tensor of elasticity.