An approach to developing damage prognosis (DP) solution that is being developed at
Los Alamos National Laboratory (LANL) is summarized in this paper. This approach integrates
aadvanced sensing technology, data interrogation procedures for state awareness, novel model
validation and uncertainty quantification techniques, and reliability-based decision-making
algorithms in an effort to transition the concept of damage prognosis to actual practice. In parallel
with this development, experimental efforts are underway to deliver a proof-of-principle technology
demonstration. This demonstration will assess impact damage and predict the subsequent fatigue
damage accumulation in a composite plate. Although the project focus will be DP for composite
materials, most of this technology can generalize to many other applications. The unique aspects of
this approach discussed herein include: 1) multi-length scale damage models analyzed on tera-scale
computer platforms that discretize composites on an individual lamina level, 2) integration of
advanced sensors with Los Alamoss flight-hardened data acquisition system, 3) damage detection
based on a statistical pattern recognition approach, and 4) reliability-based metamodels with
quantified uncertainty that can be deployed on microprocessors integrated with the sensing system
for autonomous damage prognosis.
Issue Date
2011-05-18
Keywords
Damage detection; damage prognosis; structural health monitoring; model validation; uncertainty quantification; reliability analysis; impedance method