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Philip Caplan

Publications and source records attributed to Philip Caplan.

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An advancing-ridge approach for recovering boundary $(d-1)$-simplices in $d$-dimensional meshes

Boundary-conforming four-dimensional meshes are essential for being able to run spacetime numerical simulations about complex, moving three-dimensional geometries. Specifically, a mesh of pentatopes is needed in which the tetrahedral faces of this mesh conform to the boundary of the domain. In the three-dimensional setting, a common approach consists of generating a constrained Delaunay tetrahedralization. Implementations of this approach are mature, but it is unclear how it extends to the four-dimensional setting, particularly in how the local mesh operations are scheduled to recover the constraints. This paper develops a new algorithm for recovering boundary constraints which is simple to implement in any dimension. The algorithm is primarily an advancing-front approach and uses a constrained cavity operator to incrementally insert constraints into the mesh. Compared to existing advancing-front approaches, which advance from a front of $(d-1)$-simplices (faces), the proposed approach advances from a front of $(d-2)$-simplices, called ridges. Steiner vertices can be added to the boundary when the front stalls and several examples in $3d$ demonstrate the ability of this algorithm to recover a complete representation of the input surface. For the four-dimensional geometries studied here, the algorithm generally recovers at least 99% of the input tetrahedralization with this advancing ridge procedure. For some simpler domains, complete conformity with the input tetrahedralization is achieved by adding Steiner vertices, thereby demonstrating the ability to produce boundary-conforming four-dimensional meshes. The design and efficiency of the underlying cavity operator implementation is also evaluated, showing that 30 million pentatopes can be created in about 1.5 minutes, and 300 million pentatopes in about 15 minutes on a workstation laptop.

cs.CE

A Lagrangian method for solving the spherical shallow water equations using power diagrams

Numerical simulations of the air in the atmosphere and water in the oceans are essential for numerical weather prediction. The state-of-the-art for performing these fluid simulations relies on an Eulerian viewpoint, in which the fluid domain is discretized into a mesh, and the governing equations describe the fluid motion as it passes through each cell of the mesh. However, it is unclear whether a Lagrangian viewpoint, in which the fluid is discretized by a collection of particles, can outperform Eulerian simulations in global atmospheric simulations. To date, Lagrangian approaches have shown promise, but tend to produce smoother solutions. In this work, a new Lagrangian method is developed to simulate the atmosphere in which particles are represented with spherical power cells. We introduce an efficient algorithm for computing these cells which are then used to discretize the spherical shallow water equations. Mass conservation is enforced by solving a semi-discrete optimal transport problem and a semi-implicit time stepping procedure is used to advance the solution in time. We note that, in contrast to previous work, artificial viscosity is not needed to stabilize the simulation. The performance of the spherical Voronoi diagram calculation is first assessed, which shows that spherical Voronoi diagrams of 100 million sites can be computed in under 2 minutes on a single machine. The new simulation method is then evaluated on standard benchmark test cases, which shows that momentum and energy conservation of this new method is comparable to the latest Lagrangian approach for simulating the spherical shallow water equations.

physics.flu-dyn