A multiphase fluid-structure interaction (FSI) framework using open-source software has been developed, utilising components able to run on high-performance computing platforms. A partitioned approach is employed, ensuring a separation of concerns (fluid, structure, and coupling), allowing design flexibility and robustness while reducing future maintenance effort. Multiphase FSI test cases have been simulated and compared with published results and show good agreement. This demonstrates the ability of this multiphase FSI framework in simulating complex and challenging cases involving a free liquid surface.

An important phenomenon in a wide range of scientific and engineering disciplines is the interaction between multiphase flow and elastic structures, such as an aircraft wing with a sloshing fuel tank (Gambioli et al., 2019, 2020; Mastroddi et al., 2019, 2020; Titurus et al., 2019; Saltari et al., 2021) and the impact of ocean waves on elastic ocean structures (Gomes et al., 2020). Accurately simulating a multiphase fluid-structure interaction (FSI) can help reveal the mechanisms behind important and complex real-world phenomena, allowing for important design considerations, such as how to protect an elastic structure from fatigue or failure (Botha and Hindley, 2015) or how to achieve active/passive control of a system (Ducoin et al., 2012). There is significant demand to develop an efficient and open-source numerical tool for the investigation of such phenomena. Because of the nonlinear, time-dependent, and multiphysical nature of these various multiphase FSI problems, a simulation tool that is both robust and highly scalable (in parallel computing terms) is challenging. There are notable commercial FSI solvers. However, few of them can achieve both numerical robustness and high scalability while also being able to tackle multiphase FSI problems. Commercial software, such as ANSYS (Rao, 2003) and COMSOL (Curtis et al., 2013), provide fully coupled FSI simulations. Compared with commercial software, open-source codes have advantages in removing parallel scaling-related costs (among other benefits such as enabling the implementation of bespoke solvers or algorithms, as well as transparency).