承载力
结构工程
有限元法
绳子
钢丝绳
方位(导航)
极限载荷
极限荷载
承重
流离失所(心理学)
负载测试
计算机科学
工程类
心理学
人工智能
心理治疗师
作者
Juraj Hroncek,Pavel Maršálek,David Rybanský,Martin Šotola,Lukas Drahorad,Michal Lesňák,Martin Fusek
出处
期刊:Materials
[Multidisciplinary Digital Publishing Institute]
日期:2023-05-16
卷期号:16 (10): 3756-3756
被引量:5
摘要
Steel-wire rope is a mechanical component that has versatile uses and on which human lives depend. One of the basic parameters that serve to describe the rope is its load-bearing capacity. The static load-bearing capacity is a mechanical property characterized by the limit static force that the rope is able to endure before it breaks. This value depends mainly on the cross-section and the material of the rope. The load-bearing capacity of the entire rope is obtained in tensile experimental tests. This method is expensive and sometimes unavailable due to the load limit of testing machines. At present, another common method uses numerical modeling to simulate an experimental test and evaluates the load-bearing capacity. The finite element method is used to describe the numerical model. The general procedure for solving engineering tasks of load-bearing capacity is by using the volume (3D) elements of a finite element mesh. The computational complexity of such a non-linear task is high. Due to the usability of the method and its implementation in practice, it is necessary to simplify the model and reduce the calculation time. Therefore, this article deals with the creation of a static numerical model which can evaluate the load-bearing capacity of steel ropes in a short time without compromising accuracy. The proposed model describes wires using beam elements instead of volume elements. The output of modeling is the response of each rope to its displacement and the evaluation of plastic strains in the ropes at selected load levels. In this article, a simplified numerical model is designed and applied to two constructions of steel ropes, namely the single strand rope 1 × 37 and multi-strand rope 6 × 7-WSC.
科研通智能强力驱动
Strongly Powered by AbleSci AI