CAREER: Next-Generation Shape Optimization of Geometrically Complex Artifacts
CAREER: Next-Generation Shape Optimization of Geometrically Complex Artifacts
批准号:
0745398
负责人:
Krishnan Suresh
金额:
$40.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-01-01 至 2013-08-31
中文摘要
该职业奖的研究目标是为几何复杂产品的形状优化建立下一代计算框架。现有的形状优化方法通常是缓慢和繁琐的,缺乏鲁棒性,特别是当一个产品是几何复杂的。因此,今天的产品模型在几何上简化了,在优化之前,通过特征删除。提出的框架将取代脆弱的几何简化方法与鲁棒和数学上健全的代数简化方法。将使用伴随校正方法来避免由于简化而出现的误差。此外,将探索新的特征灵敏度方法,以消除形状优化过程中重新分析的需要。提出的框架将应用于航空航天、汽车和精密机械行业的复杂工件,并对其进行测试。如果成功,这项研究的结果将对工业和社会产生重大影响,主要是通过设计高度优化,更便宜和环保的产品。在设计过程中可以探索更广泛的产品设计,从而产生新颖的新产品。在计算上,该框架将整合几何简化和形状优化,将这些研究领域与未来的发展潜力联系起来。作为测试平台,这项研究的结果将被威斯康星大学和威斯康星技术学院的研究生和本科生用于参加全国汽车设计挑战赛。研究成果也将通过技术选修课程和扩大两个校区学生的设计经验嵌入到本科课程中。在业界的支持下,参赛学生,特别是少数民族和妇女,将获得奖学金,以完成必要的CAD培训和参加全国性的比赛。
英文摘要
The research objective of this CAREER award is to establish a next-generation computational framework for shape optimization of geometrically complex products. Existing methods of shape optimization are typically slow and tedious, and lack robustness, especially when a product is geometrically complex. Product models are therefore geometrically simplified today, prior to optimization, through feature removal. The proposed framework will replace fragile geometric simplification methods with robust and mathematically sound algebraic simplification methods. An adjoint corrector method will be used to avoid errors that appear as a result of simplification. In addition, novel feature sensitivity methods will be explored that will eliminate the need for reanalysis during shape optimization. The proposed framework will be applied and tested against complex artifacts found in the aerospace, automobile and precision machinery industries.If successful, the results of this research will have a major impact on industry and society, primarily through the design of highly optimized, less expensive and environmentally friendly products. A wider range of product designs can be explored during the design process which will lead to novel new products. Computationally, this framework will integrate geometric simplification and shape optimization, linking these research areas with the potential for future advances. As a test bed, the results of this research will be used by graduate and undergraduate students from the University of Wisconsin and Wisconsin Technical Colleges to compete in national car design challenges. The research results will also be embedded within the undergraduate curricula through technical elective offerings and expanded student design experiences at both campuses. With support from industry, participating students, especially minority and women, will receive scholarships to complete necessary CAD training and take part in nation-wide competitions.
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项目类别:--
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