Abstract The application of structural topology optimization with complex engineering materials is largely hindered due to the complexity in phenomenological or physical constitutive modeling from experimental or computational material data sets. In this paper, we propose a new data-driven topology optimization (DDTO) framework to break through the limitation with the direct usage of discrete material data sets in lieu of constitutive models to describe the material behaviors. This new DDTO framework employs the recently developed data-driven computational mechanics for structural analysis which integrates prescribed material data sets into the computational formulations. Sensitivity analysis is formulated by applying the adjoint method where the tangent modulus of prescribed uniaxial stress-strain data is evaluated by means of moving least square approximation. The validity of the proposed framework is well demonstrated by the truss topology optimization examples. The proposed DDTO framework will provide a great flexibility in structural design for real applications.

中文翻译：

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一种用于结构优化的新型数据驱动拓扑优化框架

摘要 由于来自实验或计算材料数据集的现象学或物理本构建模的复杂性，结构拓扑优化在复杂工程材料中的应用在很大程度上受到阻碍。在本文中，我们提出了一种新的数据驱动拓扑优化（DDTO）框架，以突破直接使用离散材料数据集代替本构模型来描述材料行为的限制。这种新的 DDTO 框架采用最近开发的数据驱动计算力学进行结构分析，将规定的材料数据集集成到计算公式中。灵敏度分析是通过应用伴随方法制定的，其中规定的单轴应力应变数据的切线模量通过移动最小二乘近似法进行评估。桁架拓扑优化示例很好地证明了所提出框架的有效性。提出的 DDTO 框架将为实际应用的结构设计提供极大的灵活性。