Numerical model reduction of the electro-chemically coupled ion transport

V. Tu, K. Runesson, F. Larsson, Ralf J¨anicke
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Abstract

This contribution concerns the multi-scale and multi-physics Finite Element Analysis of the electro-chemically coupled ion transport [1] . In particular, we are interested in predicting the electro-chemical performance of the Structural Battery Electrolyte (SBE) by utilizing computational homogenization and numerical model reduction [2] (NMR). A sub-scale Representative Volume Element (RVE) is generated for the two-scale modeling approach. It represents the random bicontinuous microstructure of an SBE (porous polymer skeleton filled with liquid electrolyte). The governing equations consist of Gauss’ law, mass balance of the pertinent ions and linear constitutive relations. Periodic boundary conditions are imposed on the RVE according to first order homogenization on the electrical and the chemical potential fields. The fully coupled electro-chemical problem is solved to obtain the macroscopic (homogenized) transient response. By solving the RVE problem for various loading cases, we obtain training data that are used for NMR based on a snapshot Proper Orthogonal Decomposition (POD). The end product of the NMR-POD framework is a surrogate model which replaces RVE computations. Since the surrogate model consists of a system of ODEs, it requires less computational effort to solve compared to the full RVE problem. The final goal is to investigate how the choice of training data and POD modes affect the simulation accuracy, and also quantify the speed-up by exploiting the surrogate model.
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电化学耦合离子输运的数值模型还原
这一贡献涉及电化学耦合离子输运的多尺度和多物理场有限元分析[1]。特别是,我们对利用计算均匀化和数值模型还原[2](NMR)来预测结构电池电解质(SBE)的电化学性能很感兴趣。针对双尺度建模方法,生成了子尺度代表性体元(RVE)。它代表了SBE(充满液体电解质的多孔聚合物骨架)的随机双连续微观结构。控制方程由高斯定律、相关离子的质量平衡和线性本构关系组成。根据电势场和化学势场的一阶均匀化,对RVE施加周期边界条件。解决了完全耦合的电化学问题,得到了宏观(均质)瞬态响应。通过求解不同加载情况下的RVE问题,得到了基于快照适当正交分解(POD)的核磁共振训练数据。NMR-POD框架的最终产品是替代RVE计算的代理模型。由于代理模型由一个ode系统组成,因此与完整的RVE问题相比,解决它所需的计算工作量更少。最终目标是研究训练数据和POD模式的选择如何影响仿真精度,并通过利用代理模型量化加速。
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