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首页> 外文期刊>Bulletin of the American Physical Society >APS -APS March Meeting 2017 - Event - Hierarchical Multiscale Simulation: Scale-Bridging for Shock Response of Energetic Materials
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APS -APS March Meeting 2017 - Event - Hierarchical Multiscale Simulation: Scale-Bridging for Shock Response of Energetic Materials

机译：APS -APS 2017年3月会议-活动-分级多尺度模拟：含能材料冲击响应的尺度桥接

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As part of a multiscale modeling effort, we present progress on a challenge in continuum-scale modeling: the direct incorporation of complex molecular level processes in the constitutive evaluation. We use a concurrent scale-bridging approach, with a hierarchical multiscale framework running in parallel to couple a particle-based model (the “lower scale”) to the constitutive response in a finite-element multi-physics simulation (the “upper scale”). In this approach, many orders of magnitude in length scale separate the lower and upper scales, and the lower scale is able to be used in the constitutive model for all elements of the upper scale. Molecular level response includes the constitutive equation of state and non-equilibrium chemistry. Response dependent upon stochastic microstructure, such as porosity, and challenges for scale-bridging in time are also discussed.The lower scale simulations of hexahydro-1,3,5-trinitro-s-triazine (RDX) use a force-matched coarse-grain model and dissipative particle dynamics methods, and the upper scale simulates Taylor anvil and plate impact experiments. Results emphasize use of machine learning (via Gaussian process regression, or “kriging”) that accelerates time to solution, and its comparison to fully on-the-fly runs. In collaboration with: Kenneth Leiter, Richard Becker, Jaroslaw Knap, John Brennan, US Army Research Laboratory.
机译：作为多尺度建模工作的一部分，我们介绍了连续尺度建模中一个挑战的进展：在组成性评估中直接纳入复杂的分子水平过程。我们使用并行比例桥接方法，其中并行运行的分层多尺度框架将基于粒子的模型（“较低尺度”）耦合到有限元多物理场模拟（“较高尺度”）中的本构响应。 ）。在这种方法中，长度刻度的许多数量级将下部刻度和上部刻度分开，并且下部刻度能够在本构模型中用于上部刻度的所有元素。分子水平响应包括状态本构方程和非平衡化学。还讨论了依赖于随机微观结构（例如孔隙率）的响应以及及时结垢的挑战。六氢-1,3,5-三硝基-s-三嗪（RDX）的小规模模拟使用力匹配的粗晶粒模型和耗散粒子动力学方法，上部模拟泰勒砧和板撞击实验。结果强调使用机器学习（通过高斯过程回归或“ kriging”）来加快求解速度，并将其与实时运行进行比较。 与Kenneth Leiter，Richard Becker，Jaroslaw Knap合作，约翰·布伦南（John Brennan），美国陆军研究实验室。

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