Fatigue damage prognosis of a cruciform structure under biaxial random and flight profile loading

The accurate estimation of fatigue life of metallic structural components in service environments is still a challenge for the aircraft designer or fleet manager. Majority of the current available fatigue life prediction models has deficiency to accurately predict damage under random or flight profile service loads. The inherent accuracy is due to the stochastic nature of crack propagation in metallic structure. In addition, currently no generic prediction model available accounting the load interaction effects due to variable loading. In the present paper we discus the use of a Generic Bayesian framework based Gaussian process approach to probabilistically predict the fatigue damage under complex random and flight profile loading.

[1]  Aditi Chattopadhyay,et al.  Adaptive Residual Useful Life Estimation of a Structural Hotspot , 2010 .

[2]  Wen-Fang Wu,et al.  Probabilistic models of fatigue crack propagation and their experimental verification , 2004 .

[3]  W. Elber.  The Significance of Fatigue Crack Closure , 1971 .

[4]  H. Richard,et al.  Experimental and numerical investigations on the influence of the loading direction on the fatigue crack growth , 2006 .

[5]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[6]  Sankaran Mahadevan,et al.  Stochastic fatigue damage modeling under variable amplitude loading , 2007 .

[7]  D. Chopp,et al.  Extended finite element method and fast marching method for three-dimensional fatigue crack propagation , 2003 .

[8]  P. Paris.  A rational analytic theory of fatigue , 1961 .

[9]  Ian P Bond,et al.  Fatigue life prediction under complex loading for XAS/914 CFRP incorporating a mechanical fastener , 2000 .

[10]  J. Schijve,et al.  Crack growth in aluminium alloy sheet material under flight-simulation loading: 1. A comparison between twist and minitwist falstaff and short falstaff 2. Effect of truncating high loads , 1985 .

[11]  A. Chattopadhyay,et al.  Real Time Damage State Estimation and Condition Based Residual Useful Life Estimation of a Metallic Specimen under Biaxial Loading , 2009 .

[12]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[13]  Christopher K. I. Williams.  Computing with Infinite Networks , 1996, NIPS.

[14]  James C. Newman.  FASTRAN-2: A fatigue crack growth structural analysis program , 1992 .

[15]  D. Mackay,et al.  Introduction to Gaussian processes , 1998 .

[16]  S. D. Manning,et al.  A simple second order approximation for stochastic crack growth analysis , 1996 .

[17]  Nam Phan,et al.  Aircraft life management using crack initiation and crack growth models – P-3C Aircraft experience , 2007 .

[18]  J N Yang,et al.  Stochastic Crack Propagation with Applications to Durability and Damage Tolerance Analyses , 1985 .

[19]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[20]  A. Chattopadhyay,et al.  Gaussian Process Time Series Model for Life Prognosis of Metallic Structures , 2009 .