Identification of Equivalent Damage Growth Parameters for General Crack Geometry
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Analytical damage growth equations, such as Paris law, need the stress intensity factor for predicting damage growth. Analytical expressions for the stress intensity factor are available only for simple crack locations, geometries and loading conditions. Therefore, actual damage growth requires numerical solution, such as by finite elements. However, for estimating the uncertainty in remaining useful life (RUL), thousands of simulations of crack growth must be undertaken, which is computationally expensive. Here, an estimate of the error associated with RUL estimation based on an analytical stress intensity factor that does not consider the effects of boundary conditions, crack location or complex geometry is introduced. An effective damage parameter is identified which, although different from the true value, results in accurate damage growth prediction. Actual damage growth is simulated using the extended finite element method (XFEM) to model the effects of crack location and geometry on the relationship between crack size and stress intensity factor. The XFEM data are then perturbed with noise to simulate measurements. The damage growth parameter is then identified using least square filtered Bayesian (LSFB) method. The identified parameter can then be used with the model to estimate the RUL. Examples include center and edge cracks in a plate that experiences both horizontal and vertical finite effects and stress concentration caused by the presence of holes. For these examples, it is found that the RUL estimates are accurate even when an inaccurate stress intensity factor model is used.
How to Cite
##plugins.themes.bootstrap3.article.details##
Fracture Mechanics, prognosis, extended finite element method, parameter identification, Bayesian inference, least square
Coppe, A., Haftka, R. T., Kim, N. H. (2010). Least Squares-Filtered Bayesian Updating for Remaining Useful Life Estimation, in Proceeding of AIAA Non- Deterministic Approaches Conference, Orlando, FL.
Daux, C., Moёs, N., Dolbow, J., Sukumar, N., Belytschko, T . (2000). Arbitrary Branched and Intersecting Cracks with the Extended Finite Element Method, International Journal for Numerical Methods in Engineering, vol. 48, pp. 1741-1760.
Li, G., Yuan, F. G., Haftka, R. T., Kim, N. H. (2009). Bayesian Segmentation for Damage Images Using MRF Prior, Sensors and Smart Structures Technologies for Civil, Mechanical and Aerospace Systems, vol. 7292, pp. 72920J-12.
Moёs, N., Dolbow, J., Belytschko, T. (1999). A Finite Element Method for Crack Growth without Remeshing, International Journal of Numerical Methods in Engineering, vol. 46, pp. 131-150.
Mukamai, Y . (1987) Stress Intensity Factors Handbook, New York, NY: Pergamon Press.
Pais, M., Kim, N. H., Davis, T. (2010). Reanalysis of the Extended Finite Element Method for Crack Initiation and Propagation, in Proceedings of AIAA Structures, Structural Dynamics,
and Materials Conference, Orlando, FL.
Paris, P. C., Tada, H., Donald, J. K. (1999) Service Load Fatigue Damage – A Historical Perspective, International Journal of Fatigue, vol. 21, pp. 35-46.
Sheppard, J. W., Kaufman, M. A., Inc, A., Annapolis, M. D. (2005). Bayesian Diagnosis and Prognosis Using Instrument Uncertainty, IEEE Autotestcon, pp. 417-423.
Shih, C., Asaro, R. (1988). Elastic-plastic Analysis of Crack on Bimaterial Interface, Part I: Small Scale Yield, Journal of Applied Mechanics, vol. 55, pp. 299-316.
Tanaka, K. (1974). Fatigue Crack Propagation from a Crack Inclined to the Cyclic Tension Axis, Engineering Fracture Mechanics, vol. 5, pp. 594- 507.
Wang, L., Yuan, F. G. (2005). Damage Identification in a Composite Plate Using Prestack Reverse-Time Migration Technique, Structural Health Monitoring, vol. 4, pp. 195-211.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.