Chand K, Rosenberger H, Sanderse B
Centrum Wiskunde & Informatica, Science Park 123, Amsterdam, The Netherlands.
Chaos. 2024 Feb 1;34(2). doi: 10.1063/5.0168857.
The present work presents a stable proper orthogonal decomposition (POD)-Galerkin based reduced-order model (ROM) for two-dimensional Rayleigh-Bénard convection in a square geometry for three Rayleigh numbers: 104 (steady state), 3×105 (periodic), and 6×106 (chaotic). Stability is obtained through a particular (staggered-grid) full-order model (FOM) discretization that leads to a ROM that is pressure-free and has skew-symmetric (energy-conserving) convective terms. This yields long-time stable solutions without requiring stabilizing mechanisms, even outside the training data range. The ROM's stability is validated for the different test cases by investigating the Nusselt and Reynolds number time series and the mean and variance of the vertical temperature profile. In general, these quantities converge to the FOM when increasing the number of modes, and turn out to be a good measure of accuracy. However, for the chaotic case, convergence with increasing numbers of modes is relatively difficult and a high number of modes is required to resolve the low-energy structures that are important for the global dynamics.
本研究针对正方形几何结构中二维瑞利 - 贝纳德对流,给出了一种基于稳定的本征正交分解(POD)-伽辽金降阶模型(ROM),该模型适用于三个瑞利数:10⁴(稳态)、3×10⁵(周期性)和6×10⁶(混沌态)。通过一种特殊的(交错网格)全阶模型(FOM)离散化获得稳定性,这种离散化得到的ROM无压力项且具有反对称(能量守恒)对流项。这使得即使在训练数据范围之外,也能得到长时间稳定的解,而无需稳定机制。通过研究努塞尔数和雷诺数时间序列以及垂直温度剖面的均值和方差,验证了不同测试案例下ROM的稳定性。一般来说,随着模态数量增加,这些量会收敛到FOM,并且结果表明它们是衡量精度的良好指标。然而,对于混沌情况,随着模态数量增加收敛相对困难,需要大量模态来解析对全局动力学很重要的低能量结构。