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A new front-tracking Lagrangian model for the modeling of dynamic and post-dynamic recrystallization
arXiv - CS - Software Engineering 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:

动态和后动态再结晶建模的新的前跟踪拉格朗日模型

Florez等人在先前的著作(RealIMotion)中引入了一种模拟不断发展的多域问题的新方法。(2020年),并在各向同性晶粒生长(GG)的背景下并行发展,而没有考虑由于位错引起的储能(SE)的影响。该方法采用一种新的前沿跟踪方法,其中的独创性在于,不仅使晶粒之间的界面离散,而且它们的体积也被网格化,并且域的拓扑变化由对有限元(FE)进行的选择性局部重新网格化操作驱动)网格。在本文中,将对模型的进一步开发和研究进行介绍,主要是在考虑(GBM)SE的晶界迁移的模型开发中。将介绍新晶粒成核的进一步发展,从而可以对动态再结晶(DRX)和后动态再结晶(PDRX)现象进行建模。Florez等人已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,以便在考虑GG和SE的情况下验证模型的准确性。将针对DRX和PDRX机制评估计算性能,并将其与使用Level-Set(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,以便在考虑GG和SE的情况下验证模型的准确性。将针对DRX和PDRX机制评估计算性能,并将其与使用Level-Set(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,以便在考虑GG和SE的情况下验证模型的准确性。将针对DRX和PDRX机制评估计算性能,并将其与使用Level-Set(LS)公式的经典有限元(FE)框架进行比较。
更新日期:2020-09-20
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