论文标题
数据驱动的拓扑拓扑优化，使用潜在可变高斯流程进行多类微结构
Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process
论文作者
论文摘要
数据驱动的方法正在成为一种有前途的方法，用于具有更高效率的多尺度结构的拓扑设计。但是，现有数据驱动的方法主要集中在一类微观结构上，而无需考虑多个类以适应空间变化的所需属性。关键的挑战是在满足一系列属性时，缺乏不同类别的微观结构之间的固有排序或距离度量。为了克服这一障碍，我们扩展了新开发的潜伏高斯工艺（LVGP）模型，以创建用于分类的微观结构库的多响应LVGP（MR-LVGP）模型，以定性微观结构概念和定量微结构设计变量为混合式的内置添加。 MR-LVGP模型基于其对响应的集体影响将混合变量嵌入连续的设计空间中，从而对不同几何类别和微观结构的材料参数之间的相互作用提供了实质性的见解。使用此模型，我们可以轻松地在不同的微观结构概念之间获得连续和可区分的过渡，从而可以呈现梯度信息以进行多尺寸拓扑优化。我们通过多尺寸拓扑优化与基质微观结构来证明其好处。设计示例表明，由于微型和宏观结构的一致负载转移路径，考虑多类微结构可以改善性能。
The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or distance measure between different classes of microstructures in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) models to create multi-response LVGP (MR-LVGP) models for the microstructure libraries of metamaterials, taking both qualitative microstructure concepts and quantitative microstructure design variables as mixed-variable inputs. The MR-LVGP model embeds the mixed variables into a continuous design space based on their collective effects on the responses, providing substantial insights into the interplay between different geometrical classes and material parameters of microstructures. With this model, we can easily obtain a continuous and differentiable transition between different microstructure concepts that can render gradient information for multiscale topology optimization. We demonstrate its benefits through multiscale topology optimization with aperiodic microstructures. Design examples reveal that considering multiclass microstructures can lead to improved performance due to the consistent load-transfer paths for micro- and macro-structures.
