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A new front-tracking Lagrangian model for the modeling of dynamic and post-dynamic recrystallization
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2020-09-17 , DOI: arxiv-2009.08368
Sebastian Florez, Karen Alvarado, Marc Bernacki

A new method for the simulation of evolving multi-domains problems has been introduced in previous works (RealIMotion), Florez et al. (2020) and further developed in parallel in the context of isotropic Grain Growth (GG) with no consideration for the effects of the Stored Energy (SE) due to dislocations. The methodology consists in a new front-tracking approach where one of the originality is that not only interfaces between grains are discretized but their bulks are also meshed and topological changes of the domains are driven by selective local remeshing operations performed on the Finite Element (FE) mesh. In this article, further developments and studies of the model will be presented, mainly on the development of a model taking into account grain boundary migration by (GBM) SE. Further developments for the nucleation of new grains will be presented, allowing to model Dynamic Recrystallization (DRX) and Post-Dynamic Recrystallization (PDRX) phenomena. The accuracy and the performance of the numerical algorithms have been proven to be very promising in Florez et al. (2020). Here the results for multiple test cases will be given in order to validate the accuracy of the model taking into account GG and SE. The computational performance will be evaluated for the DRX and PDRX mechanisms and compared to a classical Finite Element (FE) framework using a Level-Set (LS) formulation.

中文翻译:

用于动态和后动态再结晶建模的新前跟踪拉格朗日模型

Florez 等人在之前的作品 (RealIMotion) 中引入了一种用于模拟不断发展的多域问题的新方法。(2020) 并在各向同性晶粒生长 (GG) 的背景下进一步并行开发,不考虑由于位错导致的存储能量 (SE) 的影响。该方法包括一种新的前端跟踪方法,其中一个独创性是不仅晶粒之间的界面被离散化,而且它们的体积也被网格化,并且域的拓扑变化是由在有限元 (FE) 上执行的选择性局部重新网格化操作驱动的) 网。在本文中,将介绍该模型的进一步发展和研究,主要是关于 (GBM) SE 考虑晶界迁移的模型的发展。将介绍新晶粒成核的进一步发展,允许对动态再结晶 (DRX) 和动态再结晶 (PDRX) 现象进行建模。Florez 等人已经证明数值算法的准确性和性能非常有前途。(2020)。这里将给出多个测试用例的结果,以验证考虑 GG 和 SE 的模型的准确性。将针对 DRX 和 PDRX 机制评估计算性能,并与使用水平集 (LS) 公式的经典有限元 (FE) 框架进行比较。这里将给出多个测试用例的结果,以验证考虑 GG 和 SE 的模型的准确性。将针对 DRX 和 PDRX 机制评估计算性能,并与使用水平集 (LS) 公式的经典有限元 (FE) 框架进行比较。这里将给出多个测试用例的结果,以验证考虑 GG 和 SE 的模型的准确性。将针对 DRX 和 PDRX 机制评估计算性能,并与使用水平集 (LS) 公式的经典有限元 (FE) 框架进行比较。
更新日期:2020-09-18
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