论文标题
层流和动荡流的机器学习适应：高阶不连续的Galerkin求解器的应用
Machine learning adaptation for laminar and turbulent flows: applications to high order discontinuous Galerkin solvers
论文作者
论文摘要
我们提出了一种基于机器学习的网状精炼技术，以稳定而不稳定。 Otmani等人提出的聚类技术。 ARXIV：2207.02929 [Physics.flu-Dyn]用于标记经过圆柱体的粘性和湍流区域，在RE = 40（稳定的层流流）和RE = 3900（不稳定的湍流）。在这个聚类的区域内，我们增加了多项式顺序，以表明可以获得与均匀精制的网格相似的准确性水平。该方法是有效的，因为聚类成功地识别了两个流动区域，即粘性/湍流主导区域（包括边界层和唤醒）和一个无粘性/无旋转区域（电势流动区域）。该框架中使用的数据是使用高阶不连续的Galerkin求解器生成的，从而可以在群集区域的每个元素中局部完善多项式顺序（p-Refinement）。对于稳定的层流测试案例，我们能够将计算成本降低32％，而对于不稳定的湍流案例，最高可将计算成本降低到33％。
We present a machine learning-based mesh refinement technique for steady and unsteady flows. The clustering technique proposed by Otmani et al. arXiv:2207.02929 [physics.flu-dyn] is used to mark the viscous and turbulent regions for the flow past a cylinder at Re=40 (steady laminar flow) and Re=3900 (unsteady turbulent flow). Within this clustered region, we increase the polynomial order to show that it is possible to obtain similar levels of accuracy to a uniformly refined mesh. The method is effective as the clustering successfully identifies the two flow regions, a viscous/turbulent dominated region (including the boundary layer and wake) and an inviscid/irrotational region (a potential flow region). The data used within this framework are generated using a high-order discontinuous Galerkin solver, allowing to locally refine the polynomial order (p-refinement) in each element of the clustered region. For the steady laminar test case we are able to reduce the computational cost up to 32% and for the unsteady turbulent case up to 33%.
