用于量化空间特拉斯连接复杂度的节点力需求的球谐形状描述符

Keith J. Lee, Renaud Danhaive, Caitlin T. Mueller
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引用次数: 1

摘要

空间特拉斯结构的连接在构件间轴向力的安全有效传递中起着至关重要的作用。对于离散连接,它们还可以通过充当将构件锁定在精确方向上的配准装置来提高构造效率。由于计算工作流程和对材料高效跨越系统的需求使得几何结构更加复杂的空间桁架成为可能,因此需要了解整体形式对连接需求的影响。对于具有不规则几何形状的大型结构,自定义单个节点以满足精确的构件方向和力需求可能是不可行的;相反,标准化所有连接会导致节点过大,并降低注册潜力。我们提出了一种通过节点力需求的变化来量化空间特拉斯设计复杂性的方法。通过将节点力表示为几何对象,我们利用为计算几何中的应用而开发的球面调和形状描述符,通过旋转和平移不变的固定长度向量来表征每个节点。我们通过特征向量在高维空间中的位置变化来定义空间特拉斯设计的复杂度得分,从而在早期设计探索过程中提供额外的性能指标。然后,我们通过对节点的特征向量进行聚类来减少复杂性,从而减少用于设计的唯一连接器的数量,同时最大限度地减少大规模标准化的影响。
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Spherical harmonic shape descriptors of nodal force demands for quantifying spatial truss connection complexity

The connections of a spatial truss structure play a critical role in the safe and efficient transfer of axial forces between members. For discrete connections, they can also improve construction efficiency by acting as registration devices that lock members in precise orientations. As more geometrically complex spatial trusses are enabled by computational workflows and the demand for material-efficient spanning systems, there is a need to understand the effects of global form on the demands at the connections. For large-scale structures with irregular geometry, customizing individual nodes to meet exact member orientations and force demands may be infeasible; conversely, standardizing all connections results in oversized nodes and a compromise in registration potential. We propose a method for quantifying the complexity of spatial truss designs by the variation in nodal force demands. By representing nodal forces as a geometric object, we leverage the spherical harmonic shape descriptor, developed for applications in computational geometry, to characterize each node by a rotation and translation-invariant fixed-length vector. We define a complexity score for spatial truss design by the variance in the positions of the feature vectors in higher-dimensional space, providing an additional performance metric during early stage design exploration. We then develop a pathway towards reducing complexity by clustering nodes with respect to their feature vectors to reduce the number of unique connectors for design while minimizing the effects of mass standardization.

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