Reduced-order kinetic plasma models using principal component analysis: Model formulation and manifold sensitivity
Reduced-order kinetic plasma models using principal component analysis: Model formulation and manifold sensitivity
复制标题
DOI：
10.1103/physrevfluids.2.073201
复制
发表时间：
2017-07-24
影响因子：
2.7
通讯作者：
Parente, Alessandro
中科院分区：
文献类型：
--
作者：
Bellemans, Aurelie;Magin, Thierry;Parente, Alessandro
等离子体流动涉及数百种物质和数千种不同时间尺度的反应，导致需要求解非常大的控制方程组。利用现有的计算资源，模拟非平衡等离子体混合物中的大型反应系统仍然是一个挑战。主成分分析（PCA）提供了一个通用的，相当简单的和自动化的方法来减少大的动力学机制，通过主变量的选择。这项工作显示了如何适应和应用PCA分数技术，它有其起源于燃烧领域，碰撞辐射模型。我们已经成功地将这种技术应用于氩等离子体，减少了超过90%的控制方程组，导致一个重要的加速计算和降低计算成本。
Plasma flows involve hundreds of species and thousands of reactions at different time scales, resulting in a very large set of governing equations to solve. Simulating large reacting systems in nonequilibrium plasma mixtures remains a challenge with the currently available computational resources. Principal component analysis (PCA) offers a general and rather simple and automated method to reduce large kinetic mechanisms by principal variable selection. This work shows how to adapt and apply the PCA-scores technique, which has its origin in the combustion field, to a collisional-radiative model. We have successfully applied this technique to argon plasmas, reducing the set of governing equations by more than 90%, leading to an important speed-up of the calculation and a reduction of computational cost.