Collaborative Research: Calibrating Digital Twins in the Era of Big Data with Stochastic Optimization

合作研究：利用随机优化校准大数据时代的数字孪生

基本信息

批准号：
2226348
负责人：
Eunshin Byon
金额：
$ 28.31万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226348&HistoricalAwards=false
关键词：
Collaborative Research Calibrating Digital Twins

项目摘要

This project will contribute to the national prosperity by providing new calibration methods to generate value-producing opportunities for digital twins in many applications, including energy, healthcare, and manufacturing. A digital twin is a digital representation of a complex physical system that can be useful for monitoring, forecasting, and testing the system in a virtual world. Parameter calibration of digital twins with observational data is one of the most important steps in enabling them to closely replicate a physical system. Today, advanced data sensing and collection technologies provide massive data points from many components of a complex system. The success of this project will provide a means of robust estimation by efficient sampling from these large datasets, thereby significantly reducing the computational burden of calibration. The outreach activities of the project will improve workforce preparation through engagement with industrial practitioners, broaden participation through involvement of underrepresented students in research, and provide opportunities for K-12 students to learn about the field of data science.Quantitative methods established during this project for digital twin calibration will fully leverage the power of Big Data while addressing the research challenges brought forth by the size and complexity of the datasets. Specific research tasks include: development of stochastic optimization approaches reconciled with statistical theories that will optimally guide simulation experiments by identifying the best (smallest most informative) subsets of data for computational efficiency; extending the integrative optimization framework to be applicable for a wide range of calibration problems, including multi-dimensional, functional, and time-variant calibrations, with theoretical and practical implications; and seamless incorporation of input uncertainty with optimization to dramatically enhance the solution's robustness while maintaining computational tractability. The approach will be validated through real-word case studies in building energy systems and wind power systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将通过提供新的校准方法来为国家繁荣做出贡献，以在许多应用程序（包括能源，医疗保健和制造）中为数字双胞胎创造价值产生机会。数字双胞胎是复杂物理系统的数字表示，可用于监视，预测和测试虚拟世界中的系统。具有观察数据的数字双胞胎的参数校准是使它们能够密切复制物理系统的最重要步骤之一。如今，高级数据传感和收集技术提供了来自复杂系统许多组件的大量数据点。该项目的成功将通过从这些大型数据集中进行有效采样来提供强大估计的方法，从而大大减轻校准的计算负担。该项目的外展活动将通过与工业实践者的参与来改善劳动力的准备，通过参与不足的学生参与研究来扩大参与，并为K-12学生提供了学习数据科学领域的机会。该项目在该项目中建立的数字型校准量的质量方法可以使大量数据挑战和复杂性质疑的能力，从而使数字型校准的能力充分利用。特定的研究任务包括：随机优化方法与统计理论进行核对的开发，通过确定最佳（最小的信息）数据子集以提高计算效率，可以最佳地指导仿真实验；扩展集成优化框架适用于广泛的校准问题，包括具有理论和实际含义的多维，功能和时间变化的校准；并无缝掺入输入不确定性并优化，以显着增强解决方案的鲁棒性，同时保持计算障碍。该方法将通过在建筑能源系统和风能系统中的现实案例研究中得到验证。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。