Robust generation method of a signed distance function for preprocessing of cartesian-grid-based CFD

Abstract

In practical computational fluid dynamics simulations around industrial products with complex surface shapes, the robustness of preprocessing to “dirty” geometry is an important issue. The dirty STL (Standard Triangle Language) data contains errors such as gaps between facets, overlapping of facets, and flipping of normal vectors. These errors in the STL data are difficult to avoid in 3D modeling of complex geometry. Using a Cartesian grid is advantageous to the boundary-fitted grid in the aspect of preprocessing for dirty STL files. In this study, a robust and automatic generation method of a signed distance function for the preprocessing of Cartesian grid solvers is proposed. To ensure robustness to the complex and dirty STL data, the proposed method uses information of all STL facets to determine each grid point. The proposed preprocessing method is verified by numerical simulation of the flow around the NASA common research model.