论文标题
压力数据驱动的变异多尺度减少订单模型
Pressure Data-Driven Variational Multiscale Reduced Order Models
论文作者
论文摘要
在本文中,我们开发了数据驱动的封闭/校正项,以增加流体流量降低订单模型(ROM)的压力和速度精度。具体而言,我们提出了第一个基于数据的数据驱动的多尺度ROM,其中我们使用可用数据来构建动量方程和连续性方程的闭合/校正项。我们对二维流动在RE = 50000处的二维流动的数值研究表明,与标准ROM相比,与原始数据驱动的变异性多尺度相比,新型压力数据驱动的变异多尺度的产量比标准驱动的速度更重要,而不是标准驱动的速度和压力更重要的速度,而没有压力的变异性多头形(I.E.E.E.E.E.E.E.E.E.E.E.E.ESON.ES)。特别是,我们的数值结果表明,在动量方程中添加闭合/校正项可显着提高速度和压力近似值,而在连续性方程中添加闭合/校正项仅改善压力近似值。
In this paper, we develop data-driven closure/correction terms to increase the pressure and velocity accuracy of reduced order models (ROMs) for fluid flows. Specifically, we propose the first pressure-based data-driven variational multiscale ROM, in which we use the available data to construct closure/correction terms for both the momentum equation and the continuity equation. Our numerical investigation of the two-dimensional flow past a circular cylinder at Re=50000 in the marginally-resolved regime shows that the novel pressure data-driven variational multiscale ROM yields significantly more accurate velocity and pressure approximations than the standard ROM and, more importantly, than the original data-driven variational multiscale ROM (i.e., without pressure components). In particular, our numerical results show that adding the closure/correction term in the momentum equation significantly improves both the velocity and the pressure approximations, whereas adding the closure/correction term in the continuity equation improves only the pressure approximation.