Hierarchical Design of Negative Stiffness Metamaterials Using a Bayesian Network Classifier
Hierarchical Design of Negative Stiffness Metamaterials Using a Bayesian Network Classifier
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使用贝叶斯网络分类器的负刚度超材料的分层设计
DOI：
10.1115/1.4032774
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发表时间：
2016
影响因子：
3.3
通讯作者：
David Shahan
中科院分区：
文献类型：
--
作者：
Jordan Matthews;Timothy Klatt;C. Morris;C. Seepersad;M. Haberman;David Shahan
提出了一种基于集合的多层次设计方法。将该方法应用于负刚度超材料的设计，其力学性能和损耗性能均优于传统复合材料。负刚度超材料的性能来源于其内部结构，特别是通过将小体积分数的负刚度夹杂物嵌入到连续的主体材料中。通过设计实现这些材料的高刚度和损失涉及管理设计变量之间在一系列长度范围内的复杂相互依赖关系。建立了从微尺度负刚度夹杂的结构到介观超材料的有效性质，再到说明性的大尺度部件的性能的长度尺度的分层材料模型。贝叶斯网络分类器(BNC)用于在每个分层建模级别映射设计空间的前景区域，并且这些映射被相交以识别可能提供期望的系统性能的多级别解决方案集。该方法特别适合于高效、自上而下、性能驱动的多层设计，而不是自下而上、试错的多层建模。
A set-based approach is presented for exploring multilevel design problems. The approach is applied to design negative stiffness metamaterials with mechanical stiffness and loss properties that surpass those of conventional composites. Negative stiffness metamaterials derive their properties from their internal structure, specifically by embedding small volume fractions of negative stiffness inclusions in a continuous host material. Achieving high stiffness and loss from these materials by design involves managing complex interdependencies among design variables across a range of length scales. Hierarchical material models are created for length scales ranging from the structure of the microscale negative stiffness inclusions to the effective properties of mesoscale metamaterials to the performance of an illustrative macroscale component. Bayesian network classifiers (BNCs) are used to map promising regions of the design space at each hierarchical modeling level, and the maps are intersected to identify sets of multilevel solutions that are likely to provide desirable system performance. The approach is particularly appropriate for highly efficient, top-down, performance-driven, multilevel design, as opposed to bottom-up, trial-and-error multilevel modeling.