A Decomposition Approach for Rigorous Treatment of Uncertainty in Manufacturing and Design
A Decomposition Approach for Rigorous Treatment of Uncertainty in Manufacturing and Design
批准号:
1201207
负责人:
Kurt Maute
金额:
$39.0万
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:
2012-05-01 至 2016-04-30
中文摘要
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英文摘要
The research objective of this award is to create an approach that enables integration of manufacturing and design processes of nano-structured materials into a seamless optimization framework. This approach will allow considering systematically the interdependency of manufacturing and design parameters, and rigorously treating uncertainty due to material properties and manufacturing imperfections. Conventional strategies that loosely couple design and manufacturing become inadequate when the size of material features and devices approaches nano-meter scales. This deficiency typically results in lengthy product development cycles, low yield, high manufacturing costs, and unreliable designs. The optimization framework here will tightly couple design and optimization processes. To handle the increased complexity of the interconnected design and manufacturing problem, this research will explore new strategies for rigorously decomposing the coupled problem into simpler, tractable sub-problems. The development of the optimization framework will be driven by the challenges of designing and manufacturing high-energy-density electrodes for re-chargeable battery cells. This research effort will create a formal approach for the fast and efficient insertion of novel nano-engineered materials and devices into a broad range of technologies. Through the application of the new optimization framework to the design of high-capacity battery cells, this project will directly contribute to advancing energy storage technology, which plays a crucial role in the quest for sustainable energy. Additionally, this project will provide a unique opportunity for two graduate students and up to six undergraduate students who will receive a rigorous training at the interface between design optimization, numerical modeling, and uncertainty quantification. Upon graduation, these students will have the knowledge and skills to bridge the gaps between design and manufacturing, and to tackle fundamental issues currently hampering the development of a broad class of nano-enabled technologies. Results of the research will be broadly disseminated for maximum impact.
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