期刊文献+

复合材料径向和轴向螺栓连接失效预测方法及失效模式分析

The Failure Analysis of the Composite Bolted Radial and Axial Connection
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摘要 大型火箭舱段间的复合材料端框连接主要由径向螺栓连接和轴向螺栓连接组成,设计了不同铺层和几何尺寸的连接试验件,并通过试验获得了失效载荷和失效形式。然后,选择了3种渐进损伤方法进行了连接结构的失效预测,并将预测结果与试验结果进行比较,结果表明:采用基于细观力学的复合材料渐进损伤方法,15组径向连接试验件失效载荷计算误差最大为17.3%;4组轴向连接失效载荷计算误差最大7.4%;预测的失效形式与试验结果接近,为连接结构确定了合适的失效预测方法。采用数值分析结果揭示了复合材料单向层的主要失效模式。为复合材料端框连接的设计研究提供了可用的失效预测方法及详细的失效模式分析结果。 The composite end frame joints between large rocket compartments mainly consists of radial bolt connections and axial bolt connections.This article designed composite material radial joints and pull-off joints test pieces with different layers and geometric sizes,and conducted experiments to obtain failure loads and failure models.Three progressive damage methods were used to predict the failure of bolted joints,and the predicted results were compared with experimental results.The results showed that in the failure prediction results of the composite material progressive damage model proposed by our research group based on micromechanics,the calculation errors of 15 groups of the radial joints are below 17.3%and the calculation errors of the 4 groups of the axial joints are all below 7.4%.The predicted failure model is close to the experimental results,verifying the applicability of the model.Finally,the progressive damage method proposed by our group was used to reveal the failure mode of the joints.This article can provide available failure prediction methods for subsequent design research of end frame joints.
作者 刘丰睿 章凌 杨帆 石玉红 王鹤轩 赵丽滨 LIU Fengrui;ZHANG Ling;YANG Fan;SHI Yuhong;WANG Hexuan;ZHAO Libin(School of Astronautics,Beihang University,Beijing 100191,China;Beijing Institute of Astronautical Systems Engineering,Beijing 100076,China;School of Mechanical Engineering,Hebei University of Technology,Tianjin 300401,China)
出处 《宇航总体技术》 2023年第5期20-28,共9页 Astronautical Systems Engineering Technology
基金 基础研究项目(514010104)。
关键词 复合材料 螺栓连接 渐进损伤方法 失效载荷预测 失效模式分析 Composite material Bolted joints Progressive damage method Failure load prediction Failure model analysis
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