摘要
箱形梁广泛应用于轨道交通中的支撑件,是底架及转向架构架的主要承载结构.文中根据常用箱形梁各关键结构尺寸,对其惯性矩、抗弯截面系数进行理论计算;采用正交分析法,设计了 6个因素各按5个水平的L25正交表,对箱形梁主要影响因素进行敏感性分析.结果表明:相对于形心轴1-1抗弯截面系数参数敏感性由高至低依次为总高、上盖板厚、立板厚、立板宽、盖板宽、下盖板厚;相对于形心轴2-2抗弯截面系数参数敏感性由高至低依次为总高、立板厚、立板宽、上盖板厚、下盖板厚、盖板宽.在设计过程中重点关注总高、上盖板厚度、立板厚度、立板宽等尺寸,修改上述4种参数,截面抗弯截面系数会得到明显增加.最后对比某结构尺寸相同的箱形梁与工字梁抗弯截面系数,箱形梁在每米增加45.730％质量前提下,相对形心轴1-1抗弯截面系数比工字梁要大20.374％,相对形心轴2-2抗弯截面系数比工字梁要大289.785％,箱形梁安全性更高.
Abstract
The box beam which is widely used in railway transit industry as the main bearing structure of the underframe and the bogie frame.According to the commonly-used box beams'key structural dimensions,both the moment of inertia and the flex-ural section coefficient are calculated.The orthogonal analysis method is applied to design an L25 orthogonal table with 6 factors at 5 levels each,in order to perform the sensitivity analysis on the box beam's main influencing factors.The results show that in re-lation to the 1-1 axis,the flexural section coefficient's parameter sensitivity from high to low is total height,thickness of upper cover,thickness of vertical plate,width of vertical plate,width of cover,and thickness of lower cover.In relation to the 2-2 ax-is,the flexural section coefficient's parameter sensitivity from high to low is total height,thickness of vertical plate,width of ver-tical plate,thickness of upper cover,thickness of lower cover,and width of cover.The design focuses on total height,thickness of upper cover,thickness of vertical plate,width of vertical plate and other dimensions.By modifying these four parameters,the flexural section coefficient will significantly increase.Finally,efforts are made to compare the flexural section coefficients of a box beam and an Ⅰ-typed beam with the same structural size.When the mass of the box beam increases by 45.730％per meter along the 1-1 axis,the flexural section coefficient is 20.374％larger than that of the Ⅰ-shaped beam.Along the 2-2 axis,the flexural section coefficient is 289.785％larger than that of the Ⅰ-shaped beam,leading to an improvement in safety.
NETL
NSTL
万方数据