A Multi-Scale Structural Health Monitoring Approach for Damage Detection, Diagnosis and Prognosis in Aerospace Structures

Abstract

This project has developed multi-scale methods for structural health monitoring to better understand, analyze and quantify the progression of damage at multiple length scales. Benchmark experiments were performed to relate nonlinearity measured with ultrasonic Lamb waves to plastic strain and fatigue life. Theory was developed and validated to predict second harmonic generation for specific mode/frequency pairs. A suite of advanced imaging methods was developed and demonstrated for detecting, locating and characterizing damage using both spatially distributed arrays and guided wavefield measurements. A model-based parameter estimation method was developed and validated to estimate dispersion curves, propagation loss, transducer distances and transducer transfer functions using minimal a priori information. The multi-scale finite element method was developed to bridge a fine-scale mesh around a defect and a coarse-scale discretization of the entire domain. It was validated by comparing scattering of guided waves from damage in a plate with analytical and numerical solutions, and was shown to produce very high quality results using an order of magnitude less computational resources compared to traditional methods.

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Document Details

Document Type
Technical Report
Publication Date
Jan 20, 2012
Accession Number
ADA563824

Entities

People

  • Jennifer E. Michaels
  • Laurence J. Jacobs
  • Massimo Ruzzene

Organizations

  • Georgia Tech Research Corporation

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Damage Detection
  • Detection
  • Engineering
  • Fatigue Life
  • Finite Element Analysis
  • Frequency
  • Materials
  • Measurement
  • Phase Velocity
  • Physics
  • Scattering
  • Second Harmonic Generation
  • Structural Health Monitoring
  • Transducers
  • Transfer Functions
  • Ultrasounds
  • Wave Propagation

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Structural Dynamics.

Technology Areas

  • Space