论文标题
使用Reaktoro和Firedrake在异质多孔介质中加速反应性传输模拟
Accelerated reactive transport simulations in heterogeneous porous media using Reaktoro and Firedrake
论文作者
论文摘要
这项工作研究了Leal等人引入的按需机器学习（ODML）算法的性能。 （2020）当在异质多孔介质中应用于不同的反应转运问题时。设计ODML是为了加速反应性传输模拟中计算昂贵的地球化学反应计算。我们证明，ODML算法将这些计算加快了一到三个数量级。这种加速反过来又显着加速了整个反应性转运模拟。数值实验是通过实现两个开源软件包的耦合来执行的：Reaktoro（Leal，2015年）和Firedrake（Rathgeber等，2016）。
This work investigates the performance of the on-demand machine learning (ODML) algorithm introduced in Leal et al. (2020) when applied to different reactive transport problems in heterogeneous porous media. ODML was devised to accelerate the computationally expensive geochemical reaction calculations in reactive transport simulations. We demonstrate that the ODML algorithm speeds up these calculations by one to three orders of magnitude. Such acceleration, in turn, significantly accelerates the entire reactive transport simulation. The numerical experiments are performed by implementing the coupling of two open-source software packages: Reaktoro (Leal, 2015) and Firedrake (Rathgeber et al., 2016).
