Quantification of time distribution to initial long crack by reduced order modeling on microstructurally short fatigue crack growth model

H. Yuan, W. Zhang, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The current practice in fatigue crack growth assessment for metallic components usually starts with a sufficiently long initial crack, which is an strong assumption. However, especially for high cycle fatigue, the crack propagation at early stage towards the initial crack length can account for about 90% of the structural fatigue life. And considerable uncertainties in the fatigue life prediction come from the short crack regime especially the microstructurally short crack (MSC) regime where heterogeneous polycrystalline microstructure induce oscillating crack growth rate. Therefore the MSC growth and uncertainty from microstructural variability needs a better quantification for a certain material under different loading scenario. The present paper proposes a framework to quantify the time distribution to intial long crack (TDILC) by Sobol’ decomposition based reduced order modeling (SDROM) built upon microstructurally short fatigue crack growth model based on crystal plasticity finite element method (CPFEM) and fatigue indicator parameter (FIPs), in a faster and more tractable manner. The feasibility test is carried on a high strength low alloy steel aggregate of regular hexagonal grains with only orientation as random variable, under constant amplitude load. The effectiveness of the SDROM will also be discussed by comparing to brute force Monte Carlo Simulations.

Original languageEnglish (US)
Title of host publicationAIAA Non-Deterministic Approaches
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Edition209969
ISBN (Print)9781624105296
StatePublished - Jan 1 2018
EventAIAA Non-Deterministic Approaches Conference, 2018 - Kissimmee, United States
Duration: Jan 8 2018Jan 12 2018

Other

OtherAIAA Non-Deterministic Approaches Conference, 2018
CountryUnited States
CityKissimmee
Period1/8/181/12/18

Fingerprint

Fatigue crack propagation
Cracks
Fatigue of materials
Crack propagation
Decomposition
High strength steel
Random variables
Plasticity
Finite element method
Crystals
Microstructure
Uncertainty

ASJC Scopus subject areas

  • Building and Construction
  • Civil and Structural Engineering
  • Architecture
  • Mechanics of Materials

Cite this

Yuan, H., Zhang, W., & Liu, Y. (2018). Quantification of time distribution to initial long crack by reduced order modeling on microstructurally short fatigue crack growth model. In AIAA Non-Deterministic Approaches (209969 ed.). American Institute of Aeronautics and Astronautics Inc, AIAA.

Quantification of time distribution to initial long crack by reduced order modeling on microstructurally short fatigue crack growth model. / Yuan, H.; Zhang, W.; Liu, Yongming.

AIAA Non-Deterministic Approaches. 209969. ed. American Institute of Aeronautics and Astronautics Inc, AIAA, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yuan, H, Zhang, W & Liu, Y 2018, Quantification of time distribution to initial long crack by reduced order modeling on microstructurally short fatigue crack growth model. in AIAA Non-Deterministic Approaches. 209969 edn, American Institute of Aeronautics and Astronautics Inc, AIAA, AIAA Non-Deterministic Approaches Conference, 2018, Kissimmee, United States, 1/8/18.
Yuan H, Zhang W, Liu Y. Quantification of time distribution to initial long crack by reduced order modeling on microstructurally short fatigue crack growth model. In AIAA Non-Deterministic Approaches. 209969 ed. American Institute of Aeronautics and Astronautics Inc, AIAA. 2018
Yuan, H. ; Zhang, W. ; Liu, Yongming. / Quantification of time distribution to initial long crack by reduced order modeling on microstructurally short fatigue crack growth model. AIAA Non-Deterministic Approaches. 209969. ed. American Institute of Aeronautics and Astronautics Inc, AIAA, 2018.
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