SNL Data and Visualization: ML Projects at Sandia
Abstract not provided.
Abstract not provided.
Abstract not provided.
Proceedings of the 28th International Meshing Roundtable, IMR 2019
We describe new machine-learning-based methods to defeature CAD models for tetrahedral meshing. Using machine learning predictions of mesh quality for geometric features of a CAD model prior to meshing we can identify potential problem areas and improve meshing outcomes by presenting a prioritized list of suggested geometric operations to users. Our machine learning models are trained using a combination of geometric and topological features from the CAD model and local quality metrics for ground truth. We demonstrate a proof-of-concept implementation of the resulting work ow using Sandia's Cubit Geometry and Meshing Toolkit.
Abstract not provided.
Abstract not provided.
Additive manufacturing
This paper presents an end-to-end design process for compliance minimization based topological optimization of cellular structures through to the realization of a final printed product. Homogenization is used to derive properties representative of these structures through direct numerical simulation of unit cell models of the underlying periodic structure. The resulting homogenized properties are then used assuming uniform distribution of the cellular structure to compute the final macro-scale structure. A new method is then presented for generating an STL representation of the final optimized part that is suitable for printing on typical industrial machines. Quite fine cellular structures are shown to be possible using this method as compared to other approaches that use nurb based CAD representations of the geometry. Finally, results are presented that illustrate the fine-scale stresses developed in the final macro-scale optimized part and suggestions are made as to incorporate these features into the overall optimization process.
Abstract not provided.
Scientific Programming
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Proceedings of the 16th International Meshing Roundtable, IMR 2007
This paper describes a distributed-memory, embarrassingly parallel hexahedral mesh generator, pCAMAL (parallel CUBIT Adaptive Mesh Algorithm Library). pCAMAL utilizes the sweeping method following a serial step of geometry decomposition conducted in the CUBIT geometry preparation and mesh generation tool. The utility of pCAMAL in generating large meshes is illustrated, and linear speed-up under load-balanced conditions is demonstrated.
Abstract not provided.
Abstract not provided.
Abstract not provided.