Abstract for Raechel Portelli's talk
The CyberGIS and Geospatial Data Science program launched in Fall 2022, offering professionals master’s degree and certificate options. This program trains professionals in practical Geographic Information Science skills to help them support geospatial problem-solving. With their broad spectrum of experiences, faculty members within the program teach students to collect, classify, analyze, and model geospatial data at large scales using statistics, computer science, and machine learning. In this talk, I will introduce the core components of the program and ongoing activities within the Department of Geography and Geographic Information Science to obtain accreditation with the US Geospatial Intelligence Foundation.
https://newfrontiers.illinois.edu/news-and-events/the-future-of-geospatial-intelligence-education-at-university-of-illinois-urbana-champaign/
Abstract for Jinhui Yan's talk
Existing metal additive manufacturing (AM) models have difficulty handling the laser-metal inter-action and associated boundary conditions (BCs) that significantly influence part quality metrics, such as defect and surface roughness. This talk presents a sharp-diffusive interface computational framework for simulating multi-physics processes in metal AM, focusing on better handling gas-metal interface, where metal AM physics mainly takes place. The framework consists of two com-ponents. The first is a mixed interface-capturing/interface tracking method to explicitly track the gas-metal interface topological changes without mesh motion or remeshing. The second is an enriched immersed boundary method (EIBM) to impose the critical flow, heat, and phase transition Neumann BCs, which are enforced in a smeared manner in current AM models, on the gas-metal interface with strong property discontinuity.
I will demonstrate how the developed model elucidates the fundamental metal AM physics (e.g., melt pool dynamics, keyhole instability, and powder spattering) and predicts critical part quality-related quantities (e.g., defect and surface roughness). The proposed framework’s accuracy is assessed by thoroughly comparing the simulated results against experimental measurements from NIST and Argonne National Laboratory using in-situ high-speed, high-energy x-ray imaging. I will also report other important quantities that experiments cannot measure to show the frame-work's predictive capability.
https://newfrontiers.illinois.edu/news-and-events/a-mixed-sharp-diffusive-interface-computational-framework-for-laser-material-interaction-in-metal-additive-manufacturing/
