Analog Computation Assessment of the Risk of Structural Failure Due to Crack Growth Under Random Loading

Abstract

Risks of fracture were computed from a Monte Carlo simulation of the Paris equation for crack growth rate. The simulation covered a total number of load events approximately equivalent to the estimated useful service life for a typical fighter airplane. The simulation was implemented by development of a computation technique based on the analog computer. Approximately 200 simulations were run for each of 11 assumed initial crack sizes, with the loading represented by a random noise signal filtered through a track-hold circuit to match a stress range exceedance distribution derived from an acceleration peak exceedance curve for the typical aircraft fleet. The resulting data were sampled at four fractions of simulated service life and then reduced to four final crack size histograms for each initial crack size. The raw data histograms were used to estimate parameter values for three-parameter Weibull distributions for final crack size in each case, and a regression analysis was performed to correlate the distributions for risk analysis. Mathematical risks of structural failure were computed and compared for several combinations of assumptions about the loading and the initial crack size distributions.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1975
Accession Number
ADA025266

Entities

People

  • John F. Mccarthy Jr.
  • Oscar Orringer
  • Richard F. Harris

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Databases
  • Frequency
  • Generators
  • Information Science
  • Materials
  • Monte Carlo Method
  • Probability
  • Random Variables
  • Regression Analysis
  • Reliability
  • Risk Analysis
  • Standards

Readers

  • Mechanical Engineering/Mechanics of Materials.
  • Statistical inference.