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 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 multi-fidelity 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.
翻译:金属合金与微粒孔隙的碎裂建模模型依赖多尺度损坏模拟,通常忽视制造过程中产生的孔隙空间变异性。 之所以如此简化,是因为在宏观部分明确模拟空间差异微结构的计算费用令人望而生畏, 因为在宏观部分明显模拟空间差异微结构。 为了应对这一挑战,打开多尺度材料断裂感设计门, 我们提议了一个数据驱动框架, 将机械减序模型( ROM) 与基于随机过程的校准方案结合起来。 我们的ROM 快速加速直接数字模拟( DNS), 方法是使用稳定的损坏算法, 并通过集群系统降低自由度。 由于集群会影响当地压力字段, 从而导致断裂反应, 我们通过基于潜在地图高地进程( LMGPPs) 构建多纤维随机进程来校准ROM的损坏参数。 我们使用LMGP 来校准一个机械减序模型的损坏参数, 作为微结构和组合的函数( i. deality) 加速直接数字模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟, 从而显示我们金属结构的多层设计结果。