Vanderbilt University
Engineering Capability Brief

Health Monitoring System for Hot Aerospace Components

R. F. Guratzsch and S. Mahadevan
Civil and Environmental Engineering, Vanderbilt University
VU Station B 351831, Nashville, TN 37235; 615-322-3040; fax 615-322-3365
E-mail: sankaran.mahadevan@Vanderbilt.edu

pic Introduction: Structural health monitoring (SHM) systems that report in real-time a flight vehicle's condition in terms of reactions, stresses, and displacements, are central to meeting the demanding goals of increasing flight vehicle safety and reliability, while reducing vehicle operating and maintenance costs. SHM systems must be small, lightweight, energy efficient, and the most reliable sub-system on board the flight structure in order to make incorporation into existing flight vehicle designs possible with minimal impact on performance. The structural behavior of flight vehicles is inherently random due to the uncertainties in the flight environment and a probabilistic structural analysis that includes the uncertainties associated with geometry, loads, and material properties is vital toward the success of the structural design. This includes the development of a finite element model, incorporating uncertainty quantification methods, and performing the necessary post-processing analysis. In addition, SHM sensors need to be optimally placed in order for the SHM system to detect with high probability and reliability any structural damage before it becomes critical. While many advances have been made in terms of sensor technology, damage detection algorithms, structural reliability, and deterministic sensor placement optimization (SPO) schemes, much additional research needs to be focused on probabilistic modeling, probabilistic analysis and design, as well as on SPO under uncertainty.

pic Sample Application: This study, sponsored by the U.S. Air Force, develops a methodology for the probabilistic analysis and optimization of SHM systems for next generation flight vehicles known as space operations vehicles (SOV's). Specifically, this study's focus is the sensor placement optimization (SPO) under uncertainty for the SHM system of a thermal protection system (TPS) component. In concept, the fuselage of a SOV would be protected by a TPS consisting of a network of mechanically attached panels made from heat resistant materials such as carbon-carbon composites. The design of such aerospace systems must include the impact of the uncertainties in thermal, acoustic and mechanical vibration loads, as well as the uncertainties associated with geometry and material properties. An effective multidisciplinary design optimization approach must be incorporated.

Methodology: This research defines a methodology for integrating advances in various individual disciplines for optimum design of sensor layouts of SHM systems under uncertainty. The proposed method includes the following steps: (1) structural simulation and model validation, (2) probabilistic analyses, (3) damage detection, and (4) SPO. For most realistic structures, the response due to various loads cannot be determined via a closed-form function of the input variables. The response of the structure under consideration must be computed through numerical procedures such as a finite element method (FEM). Probabilistic FEM analyses of analytical models incorporate uncertainty via the substitution of discretized random fields and processes as model parameters. From the probabilistic FEM analysis the stochastic nature of stresses, strains, and deformations are known. Additional analysis is needed to estimate the probability of correctly identifying the structural state of a component for a given sensor layout. This can be accomplished via any appropriate diagnostics signal analysis procedure (i.e. damage detection algorithm). The underlying idea of the SPO is to identify the sensor layout, which will maximize the probability of correctly classifying the structure as healthy or damaged.

ACKNOWLEDGEMENTS
This study is supported by funds from the National Science Foundation through the Vanderbilt University IGERT program on Risk and Reliability Engineering and is supported, in part, by the U.S. Air Force Research Laboratory.

 

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