李 仲;葛 森;张积广;肖迎春;白生宝. 多处损伤壁板的剩余强度研究[J]. 工程力学, 2009, 26(增刊Ⅰ): 144-147.
引用本文: 李 仲;葛 森;张积广;肖迎春;白生宝. 多处损伤壁板的剩余强度研究[J]. 工程力学, 2009, 26(增刊Ⅰ): 144-147.
LI Zhong;GE Sen;ZHANG Ji-guang;XIAO Ying-chun;BAI Sheng-bao. STUDY ON RESIDUAL STRENGTH OF PANELS WITH MULTIPLE SITE DAMAGES[J]. Engineering Mechanics, 2009, 26(增刊Ⅰ): 144-147.
Citation: LI Zhong;GE Sen;ZHANG Ji-guang;XIAO Ying-chun;BAI Sheng-bao. STUDY ON RESIDUAL STRENGTH OF PANELS WITH MULTIPLE SITE DAMAGES[J]. Engineering Mechanics, 2009, 26(增刊Ⅰ): 144-147.

多处损伤壁板的剩余强度研究

STUDY ON RESIDUAL STRENGTH OF PANELS WITH MULTIPLE SITE DAMAGES

  • 摘要: 多处损伤(MSD)严重危害老龄飞机的结构完整性。进行了多处损伤铝合金壁板的剩余强度试验研究。试验件包括加筋板和平板两种壁板,含有不同尺寸的主裂纹和MSD裂纹,裂纹为钼丝切割预制的穿透裂纹。壁板承受拉伸载荷直到破坏,通过载荷传感器和计算机记录破坏载荷。以试验数据为基础,提出了一种改进的评估多处损伤壁板剩余强度模型。用塑性区连通准则和提出的改进剩余强度模型分别计算了多处损伤壁板的剩余强度。与试验结果的比较表明:对不同的主裂纹长度、MSD裂纹长度,以及不同的主裂纹与相邻的MSD裂纹之间的韧带长度b,塑性区连通准则和改进的剩余强度模型预测结果的最大误差分别为53.63%和23.08%,而平均误差则分别为20.74%和8.6%。可以看出,对不同的裂纹尺寸,改进的剩余强度模型大大提高了预测结果的精度,可以直接应用于工程实践。

     

    Abstract: Multiple side damage (MSD) is considered a primary threat to aircraft structural safety. The experimental investigations on the residual strength of the aluminum stiffened and flat panels with multiple site damage were carried out. The panels were pre-cracked with saw cut. Each specimen was subjected to a monotonically increasing tensile load until failure occurrs and the failure load was recorded. The residual strengths of these panels in different crack sizes were obtained. Based on these experimental results, an improved model was presented in this paper. The residual strengths of those aluminum panels with multiple site damages were evaluated with the plastic zone linkup model and the improved plastic zone linkup model respectively. It was shown that for the panels with different lead crack sizes and different MSD cracks as well as different ligament lengths, the maximum errors were 53.63% and 23.08% respectively for plastic zone linkup criterion and the improved model, the average errors were 20.74% and 8.6% respectively for these two models, showing the notable improvement in prediction with the improved model.

     

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