Data-Driven Calibration of Multifidelity Multiscale Fracture Models Via Latent Map Gaussian Process
Data-Driven Calibration of Multifidelity Multiscale Fracture Models Via Latent Map Gaussian Process
复制标题
通过潜图高斯过程对多保真多尺度断裂模型进行数据驱动校准
DOI：
10.1115/1.4055951
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发表时间：
2023
影响因子：
3.3
通讯作者：
Bostanabad, Ramin
中科院分区：
文献类型：
--
作者：
Deng, Shiguang;Mora, Carlos;Apelian, Diran;Bostanabad, Ramin
具有微观孔隙的金属合金的断裂建模依赖于多尺度损伤模拟，其通常忽略制造引起的孔隙度的空间变异性。这种简化是因为显式建模空间变化的微观结构在宏观部分的计算费用过高。为了应对这一挑战，并打开多尺度材料的可感知设计的大门，我们提出了一个数据驱动的框架，该框架将机械降阶模型（ROM）与基于随机过程的校准方案相结合。我们的ROM通过使用稳定的损伤算法和通过聚类系统地减少自由度来大大加速直接数值模拟（DNS）。由于聚类影响局部应变场，因此断裂响应，我们校准ROM通过构建一个多保真度的随机过程的基础上潜在的地图高斯过程（LMGPs）。特别地，我们使用LMGP来校准ROM的损伤参数作为微观结构和聚类的函数（即，保真度）级别，使得ROM忠实地替代DNS。我们展示了我们的框架在预测多尺度金属部件的空间变化的孔隙率的损伤行为的应用。我们的研究结果表明，微观结构的孔隙率可以显着影响宏观组件的性能，因此必须考虑在设计过程中。
Fracture modeling of metallic alloys with microscopic pores relies on multiscale damage simulations which typically ignore the manufacturing-induced spatial variabilities in porosity. This simplification is made because of the prohibitive computational expenses of explicitly modeling spatially varying microstructures in a macroscopic part. To address this challenge and open the doors for the fracture-aware design of multiscale materials, we propose a data-driven framework that integrates a mechanistic reduced-order model (ROM) with a calibration scheme based on random processes. Our ROM drastically accelerates direct numerical simulations (DNS) by using a stabilized damage algorithm and systematically reducing the degrees of freedom via clustering. Since clustering affects local strain fields and hence the fracture response, we calibrate the ROM by constructing a multifidelity random process based on latent map Gaussian processes (LMGPs). In particular, we use LMGPs to calibrate the damage parameters of an ROM as a function of microstructure and clustering (i.e., fidelity) level such that the ROM faithfully surrogates DNS. We demonstrate the application of our framework in predicting the damage behavior of a multiscale metallic component with spatially varying porosity. Our results indicate that microstructural porosity can significantly affect the performance of macro-components and hence must be considered in the design process.