Collaborative Research: DDDAS-TMRP: MIPS: A Real-Time Measurement Inversion Prediction Steering Framework for Hazardous Events

合作研究：DDDAS-TMRP：MIPS：危险事件实时测量反演预测指导框架

• 批准号: 0929947
• 负责人: George Biros
• 金额: --
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0929947&HistoricalAwards=false
• 关键词: Collaborative; Research; DDDAS; TMRP; MIPS

The project will develop a multiscale, data-driven, high performance computational framework for real-time reconstruction of hazardous events from sparse measurements, and consequent probabilistic prediction of the evolution of the hazard. The framework is distinguished by four phases that are performed continually with dynamically-obtained data over the lifetime of the hazardous event. (1) Measurement: Distributed sensors provide dynamic measurements over a specified time horizon that will be used to reconstruct the initial conditions of the event. (2) Inversion: Driven by the sparse measurements, an inverse problem is solved to estimate the initial conditions for the equations governing the evolution of the hazard. (3) Prediction: Statistical analysis of the inversion results permits estimation of the uncertainty in the initial conditions, which is propagated into a prediction of the evolution of the hazard and its uncertainty. (4) Steering: Sensors are steered to new locations based on an effectivity index that incorporates sensitivities of the inversion with respect to sensor location, estimated uncertainty in the prediction, and population density factors. Continual application of the measure-invert-predict-steer (MIPS) framework described above results in updated predictions of the evolving hazard with built-in uncertainty estimates, as well as revised sensor deployment strategies that refine the predictions to reduce their uncertainty. The methods developed consider two time scales of decision making at which the MIPS framework must execute. The seconds-to-minutes decision-making scale is required by first responders to begin immediate response efforts. For such time scales, high-fidelity models in the form of partial differential equations (PDEs) are too formidable. Instead, the proposed methods will construct reduced-order models of the PDEs to facilitate realtime execution of the MIPS framework. The minutes-to-hours decision-making scale permits more careful and measured response by emergency officials using high-fidelity, high-resolution PDE models. To enable rapid execution of the MIPS cycle for such models, the project will develop fast, scalable, parallel algorithms for inversion and prediction. To demonstrate, assess, harden, robustify, and the resulting framework, will be validated on a specific application testbed: prediction of the urban/regional dispersion of intentionally- or accidentally-released atmospheric contaminants from sparse measurements.

该项目将开发一个多尺度、数据驱动、高性能的计算框架，用于从稀疏测量中实时重建危险事件，并随后对危险演变进行概率预测。该框架的特点是四个阶段，连续进行动态获得的数据在整个生命周期的危险事件。(1)测量单位：分布式传感器在指定的时间范围内提供动态测量，这些测量将用于重建事件的初始条件。(2)倒置：由稀疏测量驱动，求解逆问题以估计控制危险演化的方程的初始条件。(3)预测：反演结果的统计分析允许估计初始条件的不确定性，这是传播到灾害的演变及其不确定性的预测。(4)转向：传感器被引导到新的位置的基础上的有效性指数，结合相对于传感器位置的反演的灵敏度，在预测中估计的不确定性，和人口密度因素。 上述测量-反演-预测-转向（MIPS）框架的持续应用导致对具有内置不确定性估计的演变危险的更新预测，以及改进预测以减少其不确定性的修订的传感器部署策略。开发的方法考虑两个时间尺度的决策MIPS框架必须执行。第一响应者需要秒到分钟的决策尺度来开始立即响应工作。 对于这样的时间尺度，偏微分方程（PDE）形式的高保真模型过于强大。 相反，所提出的方法将构建降阶模型的偏微分方程，以促进实时执行的MIPS框架。 分钟到小时的决策规模允许应急官员使用高保真度，高分辨率PDE模型做出更仔细和更有分寸的反应。 为了能够快速执行MIPS周期，该项目将开发快速，可扩展的并行算法用于反演和预测。 为了证明，评估，硬化，robustify，以及由此产生的框架，将在一个特定的应用测试平台上进行验证：从稀疏测量中预测有意或意外释放的大气污染物的城市/区域扩散。