用于菌丝体复合材料梯度多尺度优化的微观结构条件替代模型
Microstructure-Conditioned Surrogate Models for Graded Multiscale Optimization of Mycelium Composites
浏览论文内容
中文总结 AI 辅助
研究针对优化含可控微观结构的可持续材料需多尺度模拟及数据量大的问题，提出用超网络对混合物理-数据替代模型依微观结构变量条件设定的方法，实现多尺度模拟，优化圆盘降低应力，还能依制造变量设定网络，加速可持续材料和结构开发。
中文摘要 AI 辅助
新兴的可持续材料越来越依赖工程层次结构和微观结构来控制其性能和力学行为。优化这些具有可控微观结构的材料需要高效的多尺度模拟。微观尺度的数据驱动替代模型可以加速多尺度模拟，但即使对于固定的微观结构也需要大量数据。当考虑一系列微观结构时，如在多尺度优化中，训练替代模型需要更多数据。为克服这一挑战，我们使用超网络根据微观结构变量对混合物理-数据替代模型进行条件设定。这种方法能够准确预测菌丝体-木屑复合材料的多尺度力学行为，即使在小数据集上训练。条件替代模型使功能梯度结构的多尺度模拟变得可行，我们通过全FE^2模拟对其进行验证。我们优化了一个梯度多尺度圆盘，与具有随机微观结构的圆盘相比，峰值应力降低了42%。然后，我们更进一步，直接根据对微观结构有复杂影响的制造变量对网络进行条件设定。这是为实现所需宏观行为而设计微观尺度的实用途径。这一贡献突出了微结构的优势，并展示了条件替代模型如何实现其多尺度优化，这将加速未来可持续材料和结构的开发与设计。
英文摘要
Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavior. Optimizing these materials with controllable microstructures requires efficient multiscale simulations. Data-driven surrogate models for the microscale can accelerate multiscale simulations, but require large amounts of data even for a fixed microstructure. When a range of microstructures is considered, as is the case in multiscale optimization, even more data is needed to train a surrogate. To overcome this challenge, we condition a hybrid physics-data surrogate on microstructural variables using a hypernetwork. This approach enables accurate predictions of multiscale mechanical behavior for a mycelium-woodchip composite material, even when trained on small datasets. The conditioned surrogate makes multiscale simulations of functionally graded structures tractable, and we validate it against a full FE^2 simulation. We optimize a graded multiscale disk, and reduce the peak stress by 42% compared to one with a random microstructure. Then, we go one step further, conditioning the network directly on manufacturing variables that can have a complex influence on the microstructure. This is a practical route to engineer the microscale for desired macroscale behavior. This contribution highlights the benefits of microarchitectured structures and demonstrates how conditioned surrogate models enable their multiscale optimization, which will accelerate the development and design of future sustainable materials and structures.
