We present a coupled simulation approach for fluid–structure–acoustic interactions (FSAI) as an example for strongly surface coupled multi-physics problems. In addition to the multi-physics character, FSAI feature multi-scale properties as a further challenge. In our partitioned approach, the problem is split into spatially separated subdomains interacting via coupling surfaces. Within each subdomain, scalable, single-physics solvers are used to solve the respective equation systems. The surface coupling between them is realized with the scalable open-source coupling tool preCICE described in the “Partitioned Fluid–Structure– Acoustics Interaction on Distributed Data: Coupling via preCICE”. We show how this approach enables the use of existing solvers and present the overall scaling behavior for a three-dimensional test case with a bending tower generating acoustic waves.We run this simulation with different solvers demonstrating the performance of various solvers and the flexibility of the partitioned approach with the coupling tool preCICE. An efficient and scalable in-situ visualization reducing the amount of data in place at the simulation processors before sending them over the network or to a file system completes the simulation environment.@en

The partitioned simulation of fluid-structure interactions offers great flexibility in terms of exchanging flow and structure solver and using existing established codes. However,it often suffers from slow convergence and limited parallel scalability. Quasi-Newton or accelerated fixed-point iterations are a very efficient way to solve the convergence issue. At the same time,they stabilize and speed up not only the standard staggered fluid-structure coupling iterations,but also the variant with simultaneous execution of flow and structure solver that is fairly inefficient if no acceleration methods for the underlying fixed-point iteration are used. In this chapter,we present a review on combinations of iteration patterns (parallel and staggered) and of quasi-Newton methods and compare their suitability in terms of convergence speed,robustness,and parallel scalability. Some of these variants use the so-called manifold mapping that yields an additional speedup by using an approach that can be interpreted as a generalization of the multi-level idea.@en