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学术报告-Multiscale Topology Optimization and De-homogenization

2022年10月24日 17:28  点击:[]

10月25日下午15:30,王博教授和阎军教授邀请了丹麦技术大学的Ole Sigmund教授给做一个学术报告,请关注报告信息,欢迎师生参加交流。


Zoom ID: 858-3631-1439

Password: 808294


报告题目:Multiscale Topology Optimization and De-homogenization

报告人:Ole Sigmund,Department of Civil and Mechanical Engineering, Technical University of Denmark


Professor Ole Signumd is a professor (faculty since 1997) at the Technical University of Denmark. He is currently a VILLUM Investigator supported by the VILLUM Foundation. 2004-2010 he served as the Chairman of the Danish Center for Applied Mathematics and Mechanics (DCAMM) and as the elected President of the International Society for Structural and Multidisciplinary Optimization (ISSMO) in the period 2011-2015 (and Executive Member 2015-2023). He was elected member of the Royal Danish Academy of Sciences and Letters in 2008 and the Danish Academy of Technical Sciences (ATV) in 2003.

报告内容:Advances in Additive Manufacturing (AM) are making it possible to realize almost any imaginable geometries in a wide range of materials. In parallel, Topology Optimization (TO) is a freeform geometry optimization tool that makes it possible to take advantage of the geometrical freedom provided by AM, in order to generate lightweight structures with extreme mechanical performances. One option, made possible by AM, is to use multiple scales in the form of so-called infill structures to improve mechanical response. Interestingly, use of isotropic porous infill structure actually degrades mechanical stiffness. Infill structure is only superior for stiffness if anisotropic and graded. Alternatively, isotropic infill structures have been postulated to improve buckling stability of structures but this has not yet been systematically investigated.

The talk will give an overview of recent activities within multiscale TO and subsequent de-homogenization for multiple loading cases, as well as accounting for local and global buckling stability. The talk will also briefly touch on the use of artificial intelligence (AI) approaches in TO and for multiscale TO and de-homogenization in particular.

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