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Volume 54 Issue 1
Feb.  2019
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Journal of Southwest Jiaotong University  > 2019  >  54(1): 106-112.
MEI Hanyu, WANG Qi, LIAO Haili, ZHANG Yan. Flutter Derivative Prediction of Flat Box Girder Based on Ensembled Neural Network[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 894-902. doi: 10.3969/j.issn.0258-2724.20200408
Citation: HU Jiexin, XIE Liyang, YU Haiyang, LIU Longxi, YIN Wei, HU Zhiyong. Virtual Experiments to Predict Bolster Fatigue Lifetime Based on FEM Model Validated by Static Tests[J]. Journal of Southwest Jiaotong University, 2019, 54(1): 106-112. doi: 10.3969/j.issn.0258-2724.20160536
shu

Virtual Experiments to Predict Bolster Fatigue Lifetime Based on FEM Model Validated by Static Tests

doi: 10.3969/j.issn.0258-2724.20160536
  • Received Date: 21 Jun 2016
  • Rev Recd Date: 07 Jul 2017
    • Available Online: 27 Sep 2017
  • Publish Date: 01 Feb 2019
    Abstract
  • To study the service safety of a metro train bolster, a virtual experiment method was used to evaluate fatigue lifetime, avoiding time-consuming and expensive fatigue tests. A static test of the bolster was designed based on the EN12663-1 standard, and the stresses and displacements on its surface were measured. An FEM model of the bolster was then established and analysed for comparison with the static test results; an FEM model that is consistent with the actual structure was thus determined. Finally, virtual experiments of the bolster under constant amplitude loads and variable amplitude loads were conducted using a Monte Carlo method. It is concluded that the fatigue reliability of this bolster with 107 loads cycles is 0.73 under constant amplitude loads and 0.81 under variable amplitude loads. This metro train bolster therefore meets the design and service requirements.

     

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    • References(15)
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