华南理工大学学报(自然科学版) ›› 2024, Vol. 52 ›› Issue (9): 62-71.doi: 10.12141/j.issn.1000-565X.230523

• 机械工程 • 上一篇    下一篇

细长桁架臂卸载反弹动力学行为仿真及实验

付玲1(), 刘洋2(), 刘延斌1, 尹莉1   

  1. 1.起重机械关键技术全国重点实验室,湖南 长沙 410013
    2.湖南师范大学 工程与设计学院,湖南 长沙 410081
  • 收稿日期:2023-08-17 出版日期:2024-09-25 发布日期:2023-12-27
  • 通信作者: 刘洋(1981—),男,博士,副教授,主要从事起重机动力学及振动抑制技术研究。 E-mail:liuyang@hunnu.edu.cn
  • 作者简介:付玲(1967—),女,博士,研究员级高级工程师,主要从事工程机械结构可靠性研究。E-mail: ful@zoomlion.com
  • 基金资助:
    国家留学基金资助项目(201908430262);建设机械关键技术国家重点实验室开放基金项目(SKLCM2022-02)

Simulation and Experiment of the Dynamic Behavior of Slender Truss Boom the During Unloading Rebound

FU Ling1(), LIU Yang2(), LIU Yanbin1, YIN Li1   

  1. 1.State Key Laboratory of Crane Technology,Changsha 410013,Hunan,China
    2.College of Engineering and Design,Hunan Normal University,Changsha 410081,Hunan,China
  • Received:2023-08-17 Online:2024-09-25 Published:2023-12-27
  • Contact: 刘洋(1981—),男,博士,副教授,主要从事起重机动力学及振动抑制技术研究。 E-mail:liuyang@hunnu.edu.cn
  • About author:付玲(1967—),女,博士,研究员级高级工程师,主要从事工程机械结构可靠性研究。E-mail: ful@zoomlion.com
  • Supported by:
    the Foundation of the China Scholarship Council(201908430262)

摘要:

细长桁架臂是桁架臂起重机的关键工作部件,卸载反弹冲击是威胁细长桁架臂起重机结构安全的重要工况。为研究细长桁架臂在卸载冲击下的动力学行为,采用刚柔耦合多体仿真方法和起重机卸载冲击实验方法探讨了桁架臂卸载反弹条件下动应力的变化规律,推算了细长桁架臂卸载冲击动载系数。以装备细长桁架臂的动臂塔机为对象,采用刚柔耦合方法建立了包含载荷模型和结构动态特性模型的动臂塔机精细化仿真模型,分析了桁架臂反弹振动引起的动应力变化,发现了桁架臂卸载反弹动应力的分布规律。根据不同臂长的起重机起升性能表,研究了仰角-动应力关系曲线与起升性能曲线之间的关系,发现起重臂的臂中出现最大动应力所对应的起重机突然卸载工况。根据起重机卸载冲击仿真结果,建立了基于仿真预测的起重机卸载冲击实验方法,实施了细长桁架臂系列载荷卸载反弹实验。桁架臂动应力实验值与仿真值之间的误差小于13%,证明精细化模型仿真是求解桁架臂卸载冲击响应的有效工具。通过模型仿真进一步预测了极限工况下细长桁架臂卸载冲击动载系数,发现起重机设计规范中关于卸载冲击动载系数的相关规定存在缺陷,探讨了桁架臂的长细比对卸载冲击动载系数的影响,为细长桁架臂的结构优化设计提供依据。

关键词: 刚柔耦合模型, 虚拟仿真, 卸载冲击实验, 动应力, 极限工况预测

Abstract:

The slender truss boom is a key working component of the crane with truss boom, and unloading rebound impact is an important working condition that threatens the safety of slender truss boom crane. To address the dynamic behavior of slender truss booms under unloading impact, this paper used rigid flexible coupling multi-body simulation method and crane unloading impact experimental method to explore the variation law of dynamic stress under unloading rebound conditions of the truss boom, and the unloading impact dynamic load coefficient was calculated based on the dynamic stress of the truss boom. A refined simulation model of a boom tower crane equipped with slender truss booms was established using a rigid flexible coupling method, which includes load model and structural dynamic characteristic model. It analyzed the dynamic stress changes caused by the rebound vibration of the truss boom, and the distribution law of peak dynamic stress during unloading rebound of truss booms was discovered. According to the lifting performance table of cranes with different boom lengths, the relationship between the elevation stress relationship curve and the lifting performance curve was studied, and the sudden unloading condition of the crane corresponding to the maximum stress in the middle of the boom occurred was found. Based on the simulated results of crane unloading impact, a crane unloading impact experiment method based on simulation prediction was established. The sudden unloading impact experiment of the series of boom tower cranes was carried out. The error between the experimental and simulated values of the truss boom dynamic stress is less than 13%, proving that refined model simulation is an effective tool for solving the unloading impact dynamic response of truss boom. Through model simulation, the unloading impact dynamic load coefficient of the slender truss boom under critical situations was further predicted. It finds that there are defects in the relevant regulations on unloading impact dynamic load coefficient in the current crane design specifications. The impact of the truss boom slenderness ratio on the unloading impact dynamic load coefficient was explored, providing a basis for the optimization design of key crane structures.

Key words: rigid flexible coupling model, virtual simulation, unloading impact test, dynamic stress, limit condition prediction

中图分类号: