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。基于对控制物理现象的描述的过程仿真模型在指导过程理解和开发方面发挥着重要作用。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，仿真能力与实践之间存在根本性差距，因为实际过程是在不精确和不确定的情况下进行的，而仿真模型处理过程变量的方式是确定性的。其次，由于需要在多个尺度上严格解析物理现象，过程模拟通常在计算上很繁琐。当仿真框架中嵌入了解释过程不确定性的能力时，并且进一步，当仿真模型用于优化工作时，计算需求就会大大增加。该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，以将过程不确定性的影响纳入模拟中。为了解决实现快速高效计算的挑战，将开发异构环境中基于代理的计算策略以及用于不确定性下大规模优化的基于组合的创新技术。先进的计算方法将应用于光纤制造过程，光纤制造过程是光网络行业的重要过程，代表了一般热制造过程中涉及的多尺度物理现象的复杂性。该方法可以容易地应用于其他材料加工应用。