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Proceedings Paper

Verification of recursive probabilistic integration (RPI) method for fatigue life management using non-destructive inspections
Author(s): Tzikang John Chen; Michael Shiao
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Paper Abstract

This paper verified a generic and efficient assessment concept for probabilistic fatigue life management. The concept is developed based on an integration of damage tolerance methodology, simulations methods1, 2, and a probabilistic algorithm RPI (recursive probability integration)3-9 considering maintenance for damage tolerance and risk-based fatigue life management. RPI is an efficient semi-analytical probabilistic method for risk assessment subjected to various uncertainties such as the variability in material properties including crack growth rate, initial flaw size, repair quality, random process modeling of flight loads for failure analysis, and inspection reliability represented by probability of detection (POD). In addition, unlike traditional Monte Carlo simulations (MCS) which requires a rerun of MCS when maintenance plan is changed, RPI can repeatedly use a small set of baseline random crack growth histories excluding maintenance related parameters from a single MCS for various maintenance plans. In order to fully appreciate the RPI method, a verification procedure was performed. In this study, MC simulations in the orders of several hundred billions were conducted for various flight conditions, material properties, and inspection scheduling, POD and repair/replacement strategies. Since the MC simulations are time-consuming methods, the simulations were conducted parallelly on DoD High Performance Computers (HPC) using a specialized random number generator for parallel computing. The study has shown that RPI method is several orders of magnitude more efficient than traditional Monte Carlo simulations.

Paper Details

Date Published: 1 April 2016
PDF: 13 pages
Proc. SPIE 9805, Health Monitoring of Structural and Biological Systems 2016, 98051U (1 April 2016); doi: 10.1117/12.2217960
Show Author Affiliations
Tzikang John Chen, Army Research Lab. (United States)
Michael Shiao, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 9805:
Health Monitoring of Structural and Biological Systems 2016
Tribikram Kundu, Editor(s)

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