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: 0352334
• 负责人: David Keyes
• 金额: --
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-27 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0352334&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.

该项目将创建和应用在线模拟的算法和软件工具，不断（1）从动态物理过程中吸收传感器数据，（2）生成最佳控制策略。 许多关键的工业，科学和社会问题将从这项研究中受益，如空气动力学，能源，物理学，基础设施，制造业，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，基本模型已经能够足够逼真地产生有意义的预测，提供未知参数（通常是初始/边界条件，材料系数，源或几何形状）可以使用观测数据进行适当的估计。关键步骤是解决由模拟方程约束的大规模非线性优化问题，通常是偏微分方程或它们的降阶模型。数据同化阶段将通过调整PDE模拟的未知参数来寻求最小化传感器数据和基于模型的预测之间的不匹配，并且最优控制阶段将基于更新的模型找到最优控制策略。尽管在硬件，网络，并行PDE求解器，大规模优化算法和实时ODE优化方面取得了进展，在实现实时偏微分方程数据同化和最佳控制的最终目标之前，必须克服重要的算法和软件挑战。需要的是基本上新的PDE优化算法，其必须：（1）运行足够快以允许在感兴趣的时间尺度上进行决策;（2）缩放到表征PDE优化的大量变量和约束以及表征高端系统的处理器;（3）调整到不同的解精度要求;（4）针对时间相关的目标和约束;（5）容忍不完整的、不确定的或错误的数据;（6）能够引导当前的解决方案;（7）在过早终止时产生有意义的结果;（8）在面对不适定性时具有鲁棒性。为了创建、应用和传播实时PDE数据同化和最优控制的使能技术，该项目将：（1）为一类重要应用开发满足上述规范的实时数据同化和最优控制算法和工具。(2)在一个面向对象的框架内实现和公开分发这些算法，该框架包含问题结构，易于与高性能PDE求解器库接口，可促进我们的工具适用于广泛的实时数据同化和最优控制问题，并在不干扰应用程序的情况下扩展算法。(3)将这些算法和工具应用于两个关键的环境和工业问题：化学气相沉积（CVD）反应器和荒地火灾蔓延的建模和控制。(4)与其他用户社区互动和合作，以确保我们生产的算法和软件在广泛的应用中有用。