基于修正 Sobol 灵敏度模型的拨穗授粉机疲劳寿命影响因素分析研究

Translated title of the contribution: Research on the Influencing Factors of the Plucking Pollinator Fatigue Life Based on Modified Sobol Sensitivity Model

Lin Zhu*, Jianchun Qiu, Min Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In view of the complex and diverse influence of external factors on the structural fatigue life of ear plucking pollinator, the influence relationship between multi working condition factors and fatigue life was analyzed based on the modified Sobol sensitivity model. Firstly, the sensitivity model based on response surface correction is derived by fusing the optimal polynomial response surface function and Sobol sensitivity algorithm. On this basis, combined with the steps of multi-factor experimental design and range verification, multi-body dynamics analysis, structural statics analysis and fatigue life analysis, a sensitivity quantitative analysis method between the fatigue life and influencing factors of the clamping device of the ear plucking pollinator is proposed. Finally, the sensitivity relationship between the influencing factors of working conditions and the fatigue life of the clamping device is analyzed and studied. The results show that the wind speed has the greatest influence on the fatigue life of the clamping device, the forward speed has the least influence, and the coupling degree between the rice field density and the wind speed is obvious. Thus, an idea is provided for the accurate prediction of structural fatigue life.

Translated title of the contributionResearch on the Influencing Factors of the Plucking Pollinator Fatigue Life Based on Modified Sobol Sensitivity Model
Original languageChinese (Traditional)
Pages (from-to)189-196
Number of pages8
JournalJixie Gongcheng Xuebao/Journal of Mechanical Engineering
Volume58
Issue number16
DOIs
Publication statusPublished - Aug 2022
Externally publishedYes

Keywords

  • ear plucking pollinator
  • fatigue life
  • multiple influencing factors
  • numerical simulation
  • sensitivity model

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