ITR/AP COLLABORATIVE RESEARCH: Real Time Optimization for Data Assimilation and Control of Large Scale Dynamic Simulations

ITR/AP 合作研究：大规模动态模拟数据同化和控制的实时优化

• 批准号: 0121207
• 负责人: David Keyes
• 金额: $ 80.5万
• 依托单位: Old Dominion University Research Foundation
• 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: 2001-10-01 至 2004-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0121207&HistoricalAwards=false
• 关键词: ITR; AP; COLLABORATIVE; RESEARCH; Real

This project will create and apply algorithms and software tools for on-line simulations that continuously (1) assimilate sensor data from dynamic physical processes, and (2) generate optimal strategies for their control. A number of critical industrial, scientific, and societal problems stand to benefit from this research such as aerodynamics, energy, geophysics, infrastructure, manufacturing, medicine, chemical process and environmental applications; two of these will be the focus of the current research. In these and many other cases, the underlying models have become capable of sufficient fidelity to yield meaningful predictions, provided unknown parameters (typically initial/boundary conditions, material coefficients, sources, or geometry) can be estimated appropriately using observational data.The critical step is the solution of a large-scale nonlinear optimization problem that is constrained by the simulation equations, typically PDEs or their reduced order models. A data assimilation phase will seek to minimize the mismatch between sensor data and model-based predictions by adjusting unknown parameters of the PDE simulation, and the optimal control phase will find an optimal control strategy based on the updated model.Despite advances in hardware, networks, parallel PDE solvers, large-scale optimization algorithms, and real-time ODE optimization, significant algorithmic and software challenges must be overcome before the ultimate goal of real-time PDE data assimilation and optimal control can be realized. Needed are fundamentally new PDE optimization algorithms that must: (1) run sufficiently quickly to permit decision-making at time scales of interest; (2) scale to the large numbers of variables and constraints that characterize PDE optimization and processors that characterize high-end systems; (3) adjust to different solution accuracy requirements; (4) target time-dependent objectives and constraints; (5) tolerate incomplete, uncertain, or errant data; (6) be capable of bootstrapping current solutions; (7) yield meaningful results when terminated prematurely; and (8) be robust in the face of ill-posedness.To create, apply, and disseminate the enabling technologies for real-time PDE data assimilation and optimal control, the project will: (1) Develop algorithms and tools for real-time data assimilation and optimal control that meet the above specifications for a class of important applications. (2) Implement and publicly distribute these algorithms within an object-oriented framework that incorporates problem structure, interfaces easily with high performance PDE solver libraries fosters applicability of our tools to a broad range of real-time data assimilation and optimal control problems, and enables extension of the algorithms without interfering with applications. (3) Apply these algorithms and tools to two critical environmental and industrial problems: modeling and control of chemical vapor deposition (CVD) reactors and of wildland firespread. (4) Interact and work with other user communities to ensure that the algorithms and software we produce are useful across a broad range of applications.

该项目将创建并应用算法和软件工具，以连续（1）从动态物理过程中吸收传感器数据，以及（2）为其控制生成最佳策略。 许多关键的工业，科学和社会问题将从这项研究中受益，例如空气动力学，能源，地球物理学，基础设施，制造，医学，化学过程和环境应用；其中两个将是当前研究的重点。在这些和许多其他情况下，潜在的模型已经具有足够的忠诚度来产生有意义的预测，提供的未知参数（通常是初始/边界条件，材料系数，源或几何形状）可以适当地使用观察数据进行适当估计。关键步骤是大规模的非线性优化问题的解决方案，即通过模型来构建模型或典型的构建式，或者典型地构建了典型的pd pds pd pds pds pd pd pds。 A data assimilation phase will seek to minimize the mismatch between sensor data and model-based predictions by adjusting unknown parameters of the PDE simulation, and the optimal control phase will find an optimal control strategy based on the updated model.Despite advances in hardware, networks, parallel PDE solvers, large-scale optimization algorithms, and real-time ODE optimization, significant algorithmic and software challenges must be overcome before实时PDE数据同化和最佳控制的最终目标可以实现。所需的是从根本上是新的PDE优化算法，必须：（1）足够快地运行以允许在感兴趣的时间范围内决策； （2）缩放大量的变量和约束，这些变量和约束表征了PDE优化和高端系统表征的处理器； （3）适应不同的解决方案精度要求； （4）目标时间依赖的目标和约束； （5）忍受不完整，不确定或错误数据； （6）能够引导电流解决方案； （7）过早终止时产生有意义的结果； （8）面对不适当的性能，要创建，应用和传播实时PDE数据同化和最佳控制的能力技术，该项目将：（1）开发算法和工具，以实时数据同化和最佳控制，以符合上述重要应用程序类别的规格。 （2）将这些算法实施并在以对象为导向的框架内公开分发，该算法结合了问题结构，与高性能PDE求解器库轻松接口，可以使我们的工具适用于广泛的实时数据同化和最佳控制问题，并启用算法扩展而无需与应用程序进行干扰。 （3）将这些算法和工具应用于两个关键的环境和工业问题：化学蒸气沉积（CVD）反应堆的建模和控制和Wildland Firespread。 （4）与其他用户社区进行互动和合作，以确保我们生产的算法和软件在广泛的应用程序中很有用。