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

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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.