GAMES Webinar 2018-61期（Siggraph 2018论文）| 谢浩然（日本尖端科技研究生院），臧光明（阿卜杜拉国王科技大学）
【GAMES Webinar 2018-61期（Siggraph 2018论文）】
报告时间：2018年8月23日（星期四）晚8:00 – 8:45（北京时间）
报告题目：Precomputed Panel Solver for Aerodynamics Simulation
In this talk, we introduce an efficient and versatile numerical aerodynamics model for general three-dimensional geometry shapes in potential flow. The proposed model has low computational cost and achieves an accuracy of moderate fidelity for the aerodynamic loads for a given glider shape. In the geometry preprocessing steps of our model, lifting-wing surfaces are recognized, and wake panels are generated automatically along the trailing edges. The proposed aerodynamics model improves the potential theory-based panel method. Furthermore, a new quadratic expression for aerodynamic forces and moments is proposed. It consists of geometry dependent aerodynamic coefficient matrices and has a continuous representation for the drag/lift-force coefficients. Our model enables natural and real-time aerodynamics simulations combined with general rigid-body simulators for interactive animation. We also present a design system for original gliders. It uses an assembly-based modeling interface and achieves interactive feedback by leveraging the partwise precomputation enabled by our method. We illustrate that one can easily design various flyable gliders using our system.
报告时间：2018年8月23日（星期四）晚8:45 – 9:30（北京时间）
报告题目：Space-time Tomography for Continuously Deforming Objects
X-ray computed tomography (CT) is a valuable tool for analyzing objects with interesting internal structure or complex geometries that are not accessible with optical means. Unfortunately, tomographic reconstruction of complex shapes requires a multitude (often hundreds or thousands) of projections from different viewpoints. Such a large number of projections can only be acquired in a time-sequential fashion. This significantly limits the ability to use x-ray tomography for either objects that undergo uncontrolled shape change at the time scale of a scan, or else for analyzing dynamic phenomena, where the motion itself is under investigation.In this work, we present a non-parametric space-time tomographic method for tackling such dynamic settings. Through a combination of a new CT image acquisition strategy, a space-time tomographic image formation model, and an alternating, multi-scale solver, we achieve a general approach that can be used to analyze a wide range of dynamic phenomena. We demonstrate our method with extensive experiments on both real and simulated data.
Guangming is currently a Ph.D student at King Abdullah University of Sciences and Technology (KAUST) in the Visual Computing Center under the supervision of Prof. Wolfgang Heidrich and Prof. Peter Wonka. His research interests lie in the area of computational imaging, numerical optimization, machine learning, and visual computing. He is currently working on space-time reconstruction, variational methods for motion optimization and animation.
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