A Decomposition Approach for Rigorous Treatment of Uncertainty in Manufacturing and Design

该奖项的研究目标是创造一种方法，使纳米结构材料的制造和设计过程能够整合到一个无缝的优化框架中。这种方法将允许系统地考虑制造和设计参数之间的相互依赖关系，并严格处理由于材料特性和制造缺陷引起的不确定性。当材料特征和器件的尺寸接近纳米级时，松散耦合设计和制造的传统策略就会变得不够用。这一缺陷通常会导致产品开发周期长、成品率低、制造成本高和设计不可靠。这里的优化框架将设计和优化过程紧密结合在一起。为了处理日益复杂的相互关联的设计和制造问题，本研究将探索将耦合问题严格分解为更简单、更容易处理的子问题的新策略。优化框架的开发将受到为充电电池设计和制造高能量密度电极的挑战的推动。这项研究工作将创造一种正式的方法，将新型纳米工程材料和设备快速有效地插入到广泛的技术中。通过将新的优化框架应用于大容量电池单元的设计，该项目将直接促进储能技术的进步，储能技术在寻求可持续能源方面发挥着关键作用。此外，该项目将为两名研究生和最多六名本科生提供一个独特的机会，他们将在设计优化、数值建模和不确定性量化之间的界面上接受严格的培训。毕业后，这些学生将拥有弥合设计和制造之间差距的知识和技能，并解决目前阻碍广泛类别纳米技术发展的根本问题。研究结果将广泛传播，以期产生最大影响。

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.