Numerical modelling of granular cargo on bulk carriers in seaway

This paper outlines the development of a numerical model for granular cargo on bulk carriers. In order to study the vessel behaviour including the motion of the load, a monolithic approach is chosen to model the fully coupled problem. The formulation of the granular material therefore has to be fully Eulerian. A nonlinear elastic solid phase is implemented in the Finite Volume solver FreSCo+ following the approach of Richter et. al [19] and Sugyiama et. al [22]. The method is then verified with the help of different Fluid-Structure interaction test cases

GPU-accelerated large-eddy simulation of ship-ice interactions

This paper reports on the applicability of the Lattice Boltzmann based free surface flow solver elbe to the simulation of complex ship-ice interactions in marine engineering. In order to model the dynamics of these colliding rigid multi-body systems, elbe is coupled to the ODE physics engine. First, basic validations of the ODE collision and friction models are presented, particularly focusing on interacting triangle meshes that later will serve to describe the ice floes. Then, the basic methodology and initial validation of the fluid-structure coupling of elbe and ODE is presented. Finally, performance is addressed: As elbe uses graphics processing units (GPUs) to accelerate the numerical calculations, the coupled numerical tool allows for investigations of ship-ice interactions in very competitive computational time and on off-the-shelf desktop hardware

A next-generation CFD tool for large-eddy simulations on the desktop

Dive deep into the fascinating world of real-time computational fluid dynam- ics. We present details of our GPU-accelerated flow solver for the simulation of non-linear violent flows in marine and coastal engineering. The solver, the efficient lattice boltzmann environment elbe, is accelerated with recent NVIDIA graphics hardware and allows for three-dimensional simulations of complex flows in or near real-time. Details of the very ef- ficient numerical back end, the pre- and postprocessing tools and the integrated OpenGL visualizer tool will be discussed. Moreover, several applications with marine relevance demonstrate that elbe can be considered as prototype for next-generation CFD tools for simulation-based design (SBD) and interactive flow field monitoring on commodity hardware

Parametric-adjoint approach for the efficient optimization of flow-exposed geometries

Today, the optimization of ship hulls and appendages, including energy-saving devices, is typically undertaken by means of coupling parametric modelling (variable geometry) and Computational Fluid Dynamics (CFD). A relatively new approach is based on parameter-free solutions, solving the adjoint RANS equations for selected objective functions (like drag and lift). Combining parametric and parameter-free solutions is an emerging technique that helps to effectively optimize shapes without leaving the CAD domain of the model, making it easier to integrate in the overall design process. On the basis of the Computer Aided Engineering (CAE) software CAESES, a parametric- adjoint approach will be presented. The approach is built on concatenating so-called “design velocities” and “adjoint shape sensitivities”. Design velocities yield regions of influence from a pure geometric point of view within a given parametric model. Meanwhile, adjoint shape sensitivities show where and how changes of the surface affect the objective. Overlaying the surface distributions of both the design velocities and the adjoint shape sensitivities result in so-called “parametric sensitivities.” These help to understand the importance of all parameters wi hin the chosen model. This approach will be demonstrated on a practical hull form optimization example

A Scalable Algorithm for Shape Optimization with Geometric Constraints in Banach Spaces

This work develops an algorithm for PDE-constrained shape optimization based on Lipschitz transformations. Building on previous work in this field, the $p$-Laplace operator is utilized to approximate a descent method for Lipschitz shapes. In particular, it is shown how geometric constraints are algorithmically incorporated avoiding penalty terms by assigning them to the subproblem of finding a suitable descent direction. A special focus is placed on the scalability of the proposed methods for large scale parallel computers via the application of multigrid solvers. The preservation of mesh quality under large deformations, where shape singularities have to be smoothed or generated within the optimization process, is also discussed. It is shown that the interaction of hierarchically refined grids and shape optimization can be realized by the choice of appropriate descent directions. The performance of the proposed methods is demonstrated for energy dissipation minimization in fluid dynamics applications