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Multiscale DDDAS Framework for Damage Prediction in Aerospace Composite Structures with Emphasis on Unmanned Aerial Vehicles

MARCO SIMONE PIGAZZINI, ARTEM KOROBENKO, YURI BAZILEVS

Abstract


In recent years, there has been a significant increase in the use of Unmanned Aerial Vehicles by the US military. UAVs are expected to fly a large number of long (48 or more hours) missions, and operate without failure. Furthermore, in order to increase the durability of these vehicles and to decrease weight, composite materials are currently experiencing a widespread adoption in applications related both to military and civilian aerospace structures. As a result, in order to decrease costs associated with the operation, maintenance, and, in some cases, loss of these vehicles, it is desirable to have a Dynamically Data-Driven Application System framework that can reliably predict the onset and progressions of structural damage in geometrically and materially complex aerospace composite structures operating in the environments typical of UAVs. In this work we present a multiscale DDDAS framework for damage prediction in aerospace structures with emphasis on self-aware air vehicles


DOI
10.12783/shm2017/14034

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