Three-Dimensional Hydrostatic Curved Channel Flow Simulations Using Non-Staggered Triangular Grids

Non-staggered triangular grids have many advantages in performing river or ocean modeling with the finite-volume method. However, horizontal divergence errors may occur, especially in large-scale hydrostatic calculations with centrifugal acceleration. This paper proposes an unstructured finite-volume method with a filtered scheme to mitigate the divergence noise and avoid further influencing the velocities and water elevation. In hydrostatic pressure calculations, we apply the proposed method to three-dimensional curved channel flows. Approximations reduce the numerical errors after filtering the horizontal divergence operator, and the approximation is second-order accurate. Numerical results for the channel flow accurately calculate the velocity profile and surface elevation at different Froude numbers. Moreover, secondary flow features such as the vortex pattern and its movement along the channel sections are also well captured.

TriCCo v1.0.0 – a cubulation-based method for computing connected components on triangular grids

We present a new method to identify connected components on a triangular grid. Triangular grids are, for example, used in atmosphere and climate models to discretize the horizontal dimension. Because they are unstructured, neighbor relations are not self-evident and identifying connected components is challenging. Our method addresses this challenge by involving the mathematical tool of cubulation. We show that cubulation allows one to map the 2-d cells of the triangular grid onto the vertices of the 3-d cells of a cubic grid. The latter is structured and so connected components can be readily identified on the cubic grid by previously developed software packages. An advantage is that the cubulation, i.e., the mapping between the triangular and cubic grids, needs to be computed only once, which should be benifical for analysing many data fields for the same grid.We further implement our method in a python package that we name TriCCo and that is made available via pypi and gitlab. We document the package, demonstrate its application using cloud data from the ICON atmosphere model, and characterize its computational performance. This shows that TriCCo is ready for triangular grids with 100,000 cells, but that its speed and memory requirements need to be improved to analyse larger grids.