A Study of Analog Programming for Prediction of Crack Growth in Aircraft Structures Subjected to Random Loads

Abstract

Results of a program to study an analog approach to risk analysis of random-load crack growth are presented. The two major objectives were to implement certain specific simulations of crack growth on hybrid analog/digital hardware, and to develop an improved approach to the modeling of random loads. Under the first objective, all but two of the specific simulations were implemented and verified. One not implemented required hardware unavailable at the installation utilized for the simulations. The other was identified as not condusive to analog simulation. These simulations utilized a 'damage parameter' (rather than crack size itself as the random variable) to provide well behaved and stable analog behavior. Under the second objective, a method of generating load statistics by direct inspection of large quantities of flight data was developed. In the course of this development, the applicability of estimation theory to the present problem was identified. The techniques of estimation theory, applied to analysis of damage in terms of an appropriately chosen damage parameter, promise to provide improved efficiency and accuracy in aircraft fatigue damage risk analysis.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1977
Accession Number
ADA055789

Entities

People

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

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Aircrafts
  • Airframes
  • Computational Fluid Dynamics
  • Computational Science
  • Databases
  • Differential Equations
  • Information Science
  • Linear Systems
  • Mathematical Filters
  • Mathematical Models
  • Mechanics
  • Monte Carlo Method
  • Probability Density Functions
  • Standards
  • Statistical Analysis

Readers

  • Computational Modeling and Simulation
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Structural Health Monitoring of Composite Structures.