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: 0540302
• 负责人: George Biros
• 金额: --
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540302&HistoricalAwards=false
• 关键词: Collaborative; Research; DDDAS; TMRP; MIPS; 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，以实时重建稀疏测量的危险事件，以及随之而来的概率预测危险的演变。该框架以四个阶段的形式区分，这些阶段在危险事件的生命周期内连续执行了动态性数据。 （1）测量：分布式传感器在指定的时间范围内提供动态测量，该测量将用于重建事件的初始条件。 （2）反转：在稀疏测量的驱动下，解决了一个反问题，以估计控制危险演变的方程的初始条件。 （3）预测：反转结果的统计分析允许对初始条件中不确定性的估计，这可以传播到危害演变及其不确定性的预测中。 （4）转向：传感器根据效率指数转移到新位置，该指数结合了反转相对于传感器位置的敏感性，预测的不确定性以及人口密度因子。 上面描述的措施不断应用iNvert-vert-Previct-trevict-Steer（MIPS）框架会导致对不确定性估计的不确定性估计以及修订的传感器部署策略进行更新的预测，以完善预测以减少其不确定性。开发的方法考虑了必须执行MIPS框架的两个决策制定尺度。急救人员需要秒至分钟的决策量表开始立即响应工作。 对于这样的时间尺度，以部分微分方程（PDE）形式的高保真模型过于强大。 取而代之的是，所提出的方法将构建PDE的减少阶模型，以促进MIPS框架的实时执行。 到小时的决策量表可以使用高保真，高分辨率的PDE模型对紧急官员进行更仔细和测量的响应。 为了实现此类模型的MIPS循环的快速执行，该项目将开发快速，可扩展，并行算法以进行反转和预测。 为了证明，评估，硬化，鲁棒性和由此产生的框架，将在特定的应用测试中进行验证：预测稀疏测量中有意或意外发出的大气污染物的城市/区域分散体。