This project will be used to store the simulations from different Compute projects from Vera and/or Alvis. The files mainly consist of STARCCM+ simulation files and data exported from those simulations. The overarching goal of this research is to utilize high-performance computing (HPC) resources to advance numerical investigations in applied hydrodynamics, with a particular emphasis on computational fluid dynamics (CFD). By leveraging large-scale computational simulations, the project seeks to deepen our understanding of the complex flow physics that govern the behavior of marine vehicles. Through accurate and efficient modeling of fluid–structure interactions, turbulence, and free-surface dynamics, the research aims to improve the performance, energy efficiency, and environmental sustainability of marine craft. At the core of this initiative is the reduction of hydrodynamic resistance and flow physics understanding around hydrofoils. Simulations conducted on HPC clusters will enable the detailed resolution of flow fields around hydrofoils and hulls, providing insights that are tricky to obtain through experiments alone. The project aims to develop tools and gather knowledge that will make the operation of foiling vessels safer and more efficient. By coupling CFD solvers with optimization algorithms, we aim to identify configurations that minimize energy losses and enhance lift to drag performance in various operating conditions. Using Reynolds-Averaged Navier–Stokes (RANS) models, this study examines the mutual influence between hydrofoils, free-surface interactions including waves and surface proximity. HPC simulations will capture the separation, stall, ventilation and other phenomena that comes into play in the design and operation of foiling vessels in dynamic situations. By providing data essential for developing control strategies and efficient foil geometries, the ultimate goal of the project is to improve the safety of high-speed hydrofoil craft. Through these studies, the project will establish a computational framework capable of accurately predicting hydrodynamic performance while significantly reducing the need for costly experimental testing and at the same time increase our understanding of the flow physics. The outcomes are expected to contribute to the design of next-generation marine vehicles that are faster, more efficient, and environmentally sustainable.