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Dynamic Data Driven Applications Systems (DDDAS) for Structural Awareness
Abstract
Structural health monitoring (SHM) seeks methods to characterize damage detection of systems during operation. One method for SHM is the Dynamic Data Driven Applications Systems (DDDAS) paradigm that integrates high-dimensional first principle models, real-time sensing, and control architectures. Over the last two decades, many DDDAS SHM results support space, air, and network awareness. In this paper, we highlight the opportunities to enhance developments towards systems, structures and materials awareness. In materials awareness, the goal is to use physics and computational modeling to support micro-properties analysis when only macro measurements are available. For structures modeling, the dynamic methods support a finite-element model optimization when only a subset of the sensor mesh has data. As a third example, systems awareness utilizes the environment to model structure properties, where the contextual information is generally available, but not precise enough for nanoanalysis. Future structures awareness can leverage DDDAS methods by capturing models of composites, metamaterials, and nanomaterials combined with advances in artificial intelligence/machine learning for prognosis, sensing, and control.
DOI
10.12783/shm2019/32299
10.12783/shm2019/32299