Boundary-Aware Rectilinear Grid: Accurate Approximation of Unstructured Dataset into Rectilinear Grid with Solid Boundary Handling Capabilities

Computational fluid dynamics simulations produce increasingly large datasets that are often defined over unstructured grids with solid boundaries. Though unstructured grids allow for the flexible representation of this geometry and the refinement of the grid resolution, they suffer from high storage cost, non-trivial spatial queries, and low reconstruction smoothness. On the other hand, rectilinear grids do not have these drawbacks, but they cannot represent complex boundaries. We present in this paper a technique for the high-quality approximation of large unstructured datasets with solid boundaries onto modified rectilinear grids that we endow with boundary handling capabilities. The resulting data representation can accommodate challenging boundaries while supporting high-order reconstruction kernels with a much-reduced memory footprint. As such, our data representation enjoys all the benefits of conventional rectilinear grids while addressing their fundamental geometric limitations. We demonstrate the proposed approach on several CFD datasets and show that our method achieves an accurate and high-quality approximation of simulation datasets.