ITR: Simulation and Optimization of Thermal Manufacturing of Materials Under Uncertainty: Application to Optical Fiber Drawing

ITR：不确定条件下材料热制造的模拟和优化：在光纤拉丝中的应用

• 批准号: 0112822
• 负责人: Ranga Pitchumani
• 金额: $ 40.91万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0112822&HistoricalAwards=false
• 关键词: ITR; Simulation; Optimization; Thermal; Manufacturing

Advanced materials form the backbone of our everyday life, including the hardware that supports the information technology (IT) enterprise. Thermal manufacturing of these materials involves strongly coupled transport phenomena that occur at multiple temporal and spatial scales. Process simulation models, based on description of the governing physical phenomena, play an important role in guiding process understanding and development. However, their complete potential remains unrealized in practice, for two principal reasons. First, a fundamental gap exists between simulation capabilities and practice in that whereas practical processes are carried out amidst a cloud of impreciseness and uncertainty, the simulation models are deterministic in the way they treat the process variables. Secondly, process simulations are often computationally tedious owing to the need for a rigorous resolution of the physical phenomena at multiple scales. The computational demands are tremendously intensified when the ability to account for process uncertainty is embedded in the simulation framework, and further, when the simulation models are used in an optimization endeavor.The research seeks to develop advanced computational methods aimed at addressing the foregoing challenges. A stochastic modeling framework will be developed for incorporating the effects of process uncertainties in the simulations. Towards addressing the challenge of enabling rapid and efficient computations, agent-based computing strategies in a heterogeneous environment and an innovative portfolio-based technique for large-scale optimization under uncertainty will be developed. The advanced computational methods will be applied to an optical fiber manufacturing process, which is an important process in the optical networking industry and typifies the complexities of the multiscale physical phenomena involved in general thermal manufacturing processes. The methodologies may be readily applied to other materials processing applications.

先进材料构成了我们日常生活的支柱，包括支持信息技术（IT）企业的硬件。这些材料的热制造涉及在多个时间和空间尺度上发生的强耦合传输现象。过程模拟模型是描述过程中主要物理现象的基础，在指导过程的理解和开发中起着重要的作用。然而，由于两个主要原因，它们的全部潜力在实践中仍未实现。首先，模拟能力和实践之间存在着根本性的差距，因为实际过程是在一片不精确和不确定的云中进行的，而模拟模型在处理过程变量的方式上是确定性的。其次，由于需要在多个尺度上对物理现象进行严格的解析，过程模拟通常在计算上是繁琐的。当考虑过程不确定性的能力被嵌入到仿真框架中时，以及当仿真模型被用于优化奋进时，计算需求被极大地加强，本研究旨在开发旨在解决上述挑战的先进计算方法。将开发一个随机建模框架，用于在模拟中纳入过程不确定性的影响。为了应对挑战，使快速和有效的计算，基于代理的计算策略在异构环境和创新的组合为基础的技术，在不确定性下的大规模优化将开发。先进的计算方法将被应用到光纤制造过程中，这是一个重要的过程中的光网络行业和典型的复杂性的多尺度物理现象涉及一般的热制造过程。所述方法可容易地应用于其它材料处理应用。