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、数据驱动的高性能计算框架，用于根据稀疏测量实时重建危险事件，并对危险演变进行随后的概率预测。该框架的特点是有四个阶段，这些阶段在危险事件的生命周期内使用动态获取的数据持续执行。 (1) 测量：分布式传感器提供指定时间范围内的动态测量，用于重建事件的初始条件。 (2) 反演：在稀疏测量的驱动下，求解反演问题来估计控制危险演化方程的初始条件。 (3) 预测：反演结果的统计分析可以估计初始条件的不确定性，并将其传播到灾害及其不确定性演变的预测中。 (4) 引导：根据有效性指数将传感器引导到新位置，该有效性指数结合了传感器位置反演的敏感性、预测中估计的不确定性以及人口密度因素。 上述测量-反向-预测-引导 (MIPS) 框架的持续应用可以通过内置的不确定性估计来更新不断变化的危险的预测，以及修订传感器部署策略，从而细化预测以减少其不确定性。开发的方法考虑了 MIPS 框架必须执行的决策制定的两个时间尺度。急救人员需要以秒到分钟的决策规模来开始立即响应工作。 对于这样的时间尺度，偏微分方程（PDE）形式的高保真模型太强大了。 相反，所提出的方法将构建 PDE 的降阶模型，以促进 MIPS 框架的实时执行。 从几分钟到几小时的决策规模允许应急官员使用高保真、高分辨率偏微分方程模型做出更仔细、更慎重的反应。 为了能够快速执行此类模型的 MIPS 周期，该项目将开发快速、可扩展、并行的反演和预测算法。 为了演示、评估、强化、稳健，以及由此产生的框架，将在特定的应用测试平台上进行验证：通过稀疏测量预测有意或无意释放的大气污染物的城市/区域扩散。