GOALI: A Novel Strategy for Large-Scale Industrial Topology Optimization
GOALI: A Novel Strategy for Large-Scale Industrial Topology Optimization
批准号:
1232508
负责人:
Krishnan Suresh
金额:
$24.26万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-09-01 至 2016-08-31
中文摘要
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英文摘要
The research objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) award is to develop a novel and powerful topology optimization method. The research will be carried through close collaboration with Mercury Marine (WI), the industrial partner for this award. The specific tasks are: (1) to identify a set of topology optimization problems that are representative of the challenges faced by the industry today; (2) to significantly improve the speed and reliability of topology optimization; (3) to focus on product realization and commercialization. In addition to collaborating with the industrial partner, the research will leverage student-designed SAE Formula cars at UW-Madison to put the topology optimization algorithms to a rigorous test.If successful, the research will provide a fresh stimulus to the mathematically rich discipline of topology optimization. It will further strengthen the theory of topological sensitivity by applying it to complex problems of engineering interest. It will explore and exploit relationship between existing topology optimization methods and new method. Through industrial interaction, the research will create better understanding, formulation and solution of practical topology optimization problems. The broader impacts of the research are: (1) better optimization techniques will lead to reduced physical prototyping, and reduced material usage; (2) undergraduates and graduate students at UW-Madison will be exposed to topology optimization through case-studies, and summer co-ops at Mercury-Marine, and (3) the impact of the industrial interaction is on budding engineers who will be exposed to a unique blend of research, industrial experience and formal education. The research is transformative in that it suggests a fundamentally new direction for topology optimization that has not been investigated by the research community.
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