DDDAS-TMRP: A Dynamic Data Driven System for Structural Health Monitoring and Critical Event Prediction

在过去的二十年里，计算科学和工程的许多方面都取得了巨大的进步。 高保真度的数学模型，高阶近似方法，和更快的解决方案的算法已经开发了许多应用。 计算速度的障碍也被硬件制造商打破。 因此，建模和仿真的潜力，以减少设计周期时间和提高系统的性能是公认的今天在几乎每一个工程领域。 然而，对于许多复杂的结构系统，即使是最精细的计算模型仍然必然是不完美的，原因有很多。 动态数据驱动应用系统（DDDAS）概念是一种独特的范例，用于利用成熟的计算和传感器网络技术来补偿模型缺陷和不可预见的系统演变和刺激条件，减轻设计缺陷对长期和短期系统安全的影响，并为维护计划和危机管理提供明智的决策。并通过模拟和实验室实验证明了DDDAS的健康监测，故障预测和危机管理的复杂结构系统在不同的时间尺度上运行。 该项目将促进以下方面的进展：（一）应用：开发用于生成混合分析，计算和数据更新模型的逆算法，这些模型代表各种时间尺度下退化系统的行为;（二）应用测量系统和方法：开发在高度动态环境中保护自主传感器网络基础设施的方法，在存在系统损坏或退化的情况下保持路由能力和时间同步，传感器评分和自一致性检查，以及用于减少模拟器-传感器节点通信的动态模型;（iii）数学和统计算法：开发用于退化系统在线识别、损伤跟踪、稳定自适应降阶建模和近实时计算的创新算法，以及（iv）系统软件基础设施：开发概念验证软件体系结构，该体系结构封装了上述领域的所有进展，并进一步解决了动态在运行时选择和优化异构计算资源上的异构功能组件。开发实时的，数据驱动的，冗余开发计划，用于解决嵌入在多学科系统中的有线和无线传感器网络的服务质量下降，作为操作条件的函数。拟议的研究的一个独特之处是一套试验台和相关的实验室实验，不仅是为了证明设想的DDDAS，而且还支持该项目的一个强有力的教育组成部分。

During the last two decades, great strides have been achieved in many aspects of computational sciences and engineering. Higher-fidelity mathematical models, higher-order approximation methods, and faster solution algorithms have been developed for many applications. Computing speed barriers have also been shattered by hardware manufacturers. As a result, the potential of modeling and simulation for reducing design-cycle time and enhancing system performance is recognized today in almost every field of engineering. However, for many complex structural systems, even the most elaborate computational models remain bound to be imperfect for numerous reasons. The Dynamic Data Driven Application Systems (DDDAS) concept is a unique paradigm for exploiting maturing computational and sensor networking technologies to compensate for model deficiencies and unforeseen system evolution and stimulus conditions, mitigate the effect of design imperfections on long-term as well as short-term system safety, and enable informed decision for maintenance planning and crisis management.This project will design, implement, build, and demonstrate by simulations as well as by laboratory experiments a DDDAS for health monitoring, failure prediction, and crisis management of complex structural systems operating in various time-scales. The project will enable advances in: (i) Applications: Development of inverse algorithms for generating hybrid analytical, computational, and data-updated models representing the behavior of degrading systems at various time-scales; (ii) Application Measurement Systems and Methods: Development of methods for preserving autonomous sensor network infrastructure in highly dynamic environments, preserving routing capabilities and time synchronization in the presence of system damage or degradation, sensor scoring and self-consistency checks, and dynamic models for reducing simulator-sensor node communication; (iii) Mathematical and Statistical Algorithms: Development of innovative algorithms for on-line identification of degrading systems, damage tracking, stable adaptive reduced-order modeling, and near real-time computing, and (iv) Systems Software Infrastructure: Development of a proof of concept software architecture that encapsulates all the advances in the above areas and furthermore addresses the issue of dynamically selecting and optimizing at run-time components of heterogeneous functionality over heterogeneous computing resources. Development of real-time, data driven, redundancy exploitation schemes for addressing the service quality degradation of wired and wireless sensor networks embedded in multidisciplinary systems, as a function of operating conditions. A unique feature of the proposed research is a set of test-beds and associated laboratory experiments conceived not only to demonstrate the envisioned DDDAS, but also to support a strong educational component of the project.