• Scott Roberts, Sandia National Laboratories
• Nagi Mansour, University of Illinois
• David Noble, Sandia National Laboratories

Traditionally, CAD has been the primary method of describing the geometry of the components and systems that engineers seek to simulate and manufacture. Recently, largely in the quest for digital twins, demand has increased for simulation of as-built materials and components to address the inherent part-to-part manufacturing variability. The abundance and ease of collection of three-dimensional geometric descriptions, including x-ray computed tomography and laser scanning, has inspired scientists and engineers to perform simulations on computational domains derived directly from imaging data. However, the process of converting greyscale three-dimensional image data to a discretized domain suitable for simulation is often arduous and fraught with errors.

Additionally, technologies such as topology optimization, coupled with advanced/additive manufacturing techniques, have made it possible to design and create parts with no more than a faceted surface description of the part geometry. While a faceted STL file may suffice for manufacturing, it is not currently usable by most meshing and simulation workflows. Other implicit interface descriptions like level set and volume fractions are being employed to describe the geometry of topologically complex or evolving domains.
In this mini-symposium, we explore techniques for addressing the challenges involved in the domain discretization, meshing, and simulation of as-built parts as well as other alternate (non-CAD) geometry descriptions. Topics include, but are not limited to:
• Computed tomography reconstruction techniques
• Geometry creation from point cloud data
• Image segmentation, labelling, and part identification
• Geometric feature identification and detection
• Generation of CAD from image/faceted data
• Discretization/meshing of faceted or voxel data
• Discretization/meshing of implicit interfaces
• Numerical algorithms for solving multi-physics problems on as-built geometries
• Geometric uncertainty quantification and propagation
• High performance computing applied to as-built geometries
• Applications of the above techniques to engineering applications
• Machine learning-based approaches for all of the above topics