A Probabilistic Design Method for Fatigue Life of Metallic Component

In the present study, a general probabilistic design framework is developed for cyclic fatigue life prediction of metallic hardware using methods that address uncertainty in experimental data and c...

[1]  Jon C. Helton,et al.  Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems , 2002 .

[2]  A. Saltelli,et al.  On the Relative Importance of Input Factors in Mathematical Models , 2002 .

[3]  Weiwen Peng,et al.  Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty , 2016, Reliab. Eng. Syst. Saf..

[4]  S. C. Saunders,et al.  A Statistical Model for Life-Length of Materials , 1958 .

[5]  A. Saltelli,et al.  Importance measures in global sensitivity analysis of nonlinear models , 1996 .

[6]  Hong-Zhong Huang,et al.  Probabilistic modeling of damage accumulation for time-dependent fatigue reliability analysis of railway axle steels , 2015 .

[7]  C. Fortuin,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory , 1973 .

[8]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

[9]  J. D. Eshelby The determination of the elastic field of an ellipsoidal inclusion, and related problems , 1957, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[10]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[11]  V. V. Bolotin,et al.  Early fatigue crack growth as the damage accumulation process , 2001 .

[12]  Ivo Babuska,et al.  Bayesian inference and model comparison for metallic fatigue data , 2015, 1512.01779.

[13]  Omar Ghattas,et al.  From SIAM News , Volume 43 , Number 10 , December 2010 Computer Predictions with Quantified Uncertainty , Part II , 2010 .

[14]  Hong-Zhong Huang,et al.  Probabilistic Low Cycle Fatigue Life Prediction Using an Energy-Based Damage Parameter and Accounting for Model Uncertainty , 2012 .

[15]  Shun-Peng Zhu,et al.  Probabilistic framework for multiaxial LCF assessment under material variability , 2017 .

[16]  J. Oden,et al.  Calibration and validation of coarse-grained models of atomic systems: application to semiconductor manufacturing , 2014 .

[17]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[18]  Katsuhiro Maekawa,et al.  Mechanical Properties of Ti–6Al–4V Titanium Alloy with Submicrocrystalline Structure Produced by Severe Plastic Deformation , 2005 .

[19]  Sai Hung Cheung,et al.  PARALLEL ADAPTIVE MULTILEVEL SAMPLING ALGORITHMS FOR THE BAYESIAN ANALYSIS OF MATHEMATICAL MODELS , 2012 .

[20]  Dan M. Frangopol,et al.  Probabilistic Fatigue Life Estimation of Steel Bridges by Using a Bilinear S-N Approach , 2012 .

[21]  A. Bahloul,et al.  Probabilistic approach for predicting fatigue life improvement of cracked structure repaired by high interference fit bushing , 2017 .

[22]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[23]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[24]  Billie F. Spencer,et al.  Stochastic modeling of fatigue crack growth , 1988 .

[25]  Paul T. Bauman,et al.  Real-time inference of stochastic damage in composite materials , 2014 .

[26]  A. Saltelli,et al.  Making best use of model evaluations to compute sensitivity indices , 2002 .

[27]  R. W. Lardner,et al.  A theory of random fatigue , 1967 .

[28]  Marco Giglio,et al.  Identification of damage parameters for Ti‐6Al‐4V titanium alloy using continuum damage mechanics , 2012 .

[29]  Mahmoud Farzin,et al.  Continuum damage mechanics application in low-cycle thermal fatigue , 2013 .

[30]  J. T. Hammer,et al.  Plastic Deformation and Ductile Fracture of Ti-6Al-4V under Various Loading Conditions , 2012 .

[31]  Saltelli Andrea,et al.  Sensitivity Analysis for Nonlinear Mathematical Models. Numerical ExperienceSensitivity Analysis for Nonlinear Mathematical Models. Numerical Experience , 1995 .

[32]  D. McDowell,et al.  Microstructure-sensitive computational modeling of fatigue crack formation , 2010 .

[33]  Nicole Apetre,et al.  Generalized probabilistic model allowing for various fatigue damage variables , 2017 .

[34]  Hong-Zhong Huang,et al.  Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys , 2013 .

[35]  Karl W. Schulz,et al.  The Parallel C++ Statistical Library 'QUESO': Quantification of Uncertainty for Estimation, Simulation and Optimization , 2011, Euro-Par Workshops.

[36]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[37]  Tianhong Yu Continuum damage mechanics models and their applications to composite components of aero-engines , 2016 .

[38]  Daniela Calvetti,et al.  Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing , 2007 .

[39]  J. Tinsley Oden,et al.  Selection, calibration, and validation of coarse-grained models of atomistic systems , 2015 .

[40]  J. Lemaître,et al.  Engineering Damage Mechanics: Ductile, Creep, Fatigue and Brittle Failures , 2005 .

[41]  Jean Lemaitre,et al.  A two scale damage concept applied to fatigue , 1999 .

[42]  J. Tinsley Oden,et al.  A Dynamic Data Driven Application System for Real-time Monitoring of Stochastic Damage , 2013, ICCS.

[43]  Ravi Shriram Yatnalkar,et al.  Experimental Investigation of Plastic Deformation of Ti-6Al-4V under Various Loading Conditions , 2010 .

[44]  Hongwei Shen,et al.  Probabilistic model on stochastic fatigue damage , 2000 .

[45]  E. Somersalo,et al.  Statistical and computational inverse problems , 2004 .

[46]  Jean Lemaitre,et al.  A Course on Damage Mechanics , 1992 .

[47]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[48]  G. C. Salivar,et al.  Statistical modeling of fatigue-crack growth in a nickel-base superalloy , 1983 .

[49]  Elizabeth A. Holm,et al.  The effect of microstructural representation on simulations of microplastic ratcheting , 2010 .

[50]  E. Kröner Zur plastischen verformung des vielkristalls , 1961 .

[51]  Paul T. Bauman,et al.  A computational framework for dynamic data‐driven material damage control, based on Bayesian inference and model selection , 2015 .

[52]  Michael M. Khonsari,et al.  Probabilistic simulation of fatigue damage and life scatter of metallic components , 2013 .

[53]  Ivo Babuška,et al.  Predictive Computational Science: Computer Predictions in the Presence of Uncertainty , 2017 .

[54]  Anne S. Kiremidjian,et al.  Stochastic modeling of fatigue crack growth , 1988 .