Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time

The rainflow counting algorithm for material fatigue is both simple to implement and extraordinarily successful for predicting material failure times. However, it neglects memory effects and time-ordering dependence, and therefore runs into difficulties dealing with highly intermittent or transient stochastic loads with heavy tailed distributions. Such loads appear frequently in a wide range of applications in ocean and mechanical engineering, such as wind turbines and offshore structures. In this work we employ the Serebrinsky-Ortiz cohesive envelope model for material fatigue to characterize the effects of load intermittency on the fatigue-crack nucleation time. We first formulate efficient numerical integration schemes, which allow for the direct characterization of the fatigue life in terms of any given load time-series. Subsequently, we consider the case of stochastic intermittent loads with given statistical characteristics. To overcome the need for expensive Monte-Carlo simulations, we formulate the fatigue life as an up-crossing problem of the coherent envelope. Assuming statistical independence for the large intermittent spikes and using probabilistic arguments we derive closed expressions for the up-crossing properties of the coherent envelope and obtain analytical approximations for the probability mass function of the failure time. The analytical expressions are derived directly in terms of the probability density function of the load, as well as the coherent envelope. We examine the accuracy of the analytical approximations and compare the predicted failure time with the standard rainflow algorithm for various loads. Finally, we use the analytical expressions to examine the robustness of the derived probability distribution for the failure time with respect to the coherent envelope geometrical properties.

[1]  P. Spanos,et al.  Harmonic wavelets based statistical linearization for response evolutionary power spectrum determination , 2012 .

[2]  T. Sapsis Statistics of Extreme Events in Fluid Flows and Waves , 2021 .

[3]  M. Ciavarella,et al.  On the connection between Palmgren‐Miner rule and crack propagation laws , 2018 .

[4]  Roman Garnett,et al.  Efficient Nonmyopic Active Search , 2017, ICML.

[5]  Vasilis K. Dertimanis,et al.  A substructure approach for fatigue assessment on wind turbine support structures using output-only measurements , 2017 .

[6]  Themistoklis P. Sapsis,et al.  Probabilistic Description of Extreme Events in Intermittently Unstable Dynamical Systems Excited by Correlated Stochastic Processes , 2014, SIAM/ASA J. Uncertain. Quantification.

[7]  Roman Garnett,et al.  Bayesian optimization for automated model selection , 2016, NIPS.

[8]  Enrico Zio,et al.  Estimation of the Functional Failure Probability of a Thermal Hydraulic Passive System by Subset Simulation , 2009 .

[9]  Kyung K. Choi,et al.  Integrating variable wind load, aerodynamic, and structural analyses towards accurate fatigue life prediction in composite wind turbine blades , 2016 .

[10]  M. Ortiz,et al.  A phenomenological cohesive model of ferroelectric fatigue , 2006 .

[11]  Costas Papadimitriou,et al.  Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating , 2018, Structural Health Monitoring.

[12]  J. Sørensen,et al.  Simulation of stochastic loads for fatigue experiments , 1989 .

[13]  K. Chernyshov Information-Theoretic Statistical Linearization , 2016 .

[14]  Themistoklis P. Sapsis,et al.  A probabilistic decomposition-synthesis method for the quantification of rare events due to internal instabilities , 2015, J. Comput. Phys..

[15]  A. Olsson,et al.  On Latin hypercube sampling for structural reliability analysis , 2003 .

[16]  Xun Huan,et al.  Simulation-based optimal Bayesian experimental design for nonlinear systems , 2011, J. Comput. Phys..

[17]  T. T. Soong,et al.  Random Vibration of Mechanical and Structural Systems , 1992 .

[18]  Peter Wolfsteiner,et al.  Fatigue assessment of non-stationary random vibrations by using decomposition in Gaussian portions , 2017 .

[19]  Yong-Gyo Lee,et al.  Ultimate Costs of the Disaster: Seven Years After the Deepwater Horizon Oil Spill , 2018 .

[20]  Suhail Ahmad,et al.  Dynamic Response and Fatigue Reliability Analysis of Marine Riser Under Random Loads , 2007 .

[21]  Kunio Kashino,et al.  Unscented statistical linearization and robustified Kalman filter for nonlinear systems with parameter uncertainties , 2014, 2014 American Control Conference.

[22]  LIFE PREDICTION ANALYSIS OF THICK ADHESIVE BOND LINES UNDER VARIABLE AMPLITUDE FATIGUE LOADING , 2018 .

[23]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[24]  Exact Distribution of Argmax (Argmin) , 2011 .

[25]  Michael Ortiz,et al.  A hysteretic cohesive-law model of fatigue-crack nucleation , 2005 .

[26]  Jaap Schijve,et al.  Fatigue of Structures and Materials in the 20th Century and the State of the Art. , 2003 .

[27]  H. Joo,et al.  Heavy-Tailed Response of Structural Systems Subjected to Stochastic Excitation Containing Extreme Forcing Events , 2016, Journal of Computational and Nonlinear Dynamics.

[28]  Sankaran Mahadevan,et al.  FATIGUE RELIABILITY ANALYSIS USING NONDESTRUCTIVE INSPECTION , 2001 .

[29]  Ky Khac Vu,et al.  Surrogate-based methods for black-box optimization , 2017, Int. Trans. Oper. Res..