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首页> 外文期刊>Journal of Mathematical Analysis and Applications >A reduced-order extrapolated Crank-Nicolson finite spectral element method based on POD for the 2D non-stationary Boussinesq equations
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A reduced-order extrapolated Crank-Nicolson finite spectral element method based on POD for the 2D non-stationary Boussinesq equations

机译:基于POD的减少的外推曲柄 - 尼古尔森有限谱元方法,用于2D非静止Boussinesq方程

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In this paper, we mainly utilize proper orthogonal decomposition (POD) to reduce the order for the coefficient vector of the classical Crank-Nicolson finite spectral element (CCNFSE) method of the two-dimensional (2D) non-stationary Boussinesq equations about vorticity-stream functions so that the reduced-order method maintains all the advantages of the CCNFSE method. Toward this end, we first establish a CCNFSE format with the second-order time accuracy for the two-dimensional (2D) non-stationary Boussinesq equations about vorticity-stream functions and analyze the existence, stability, and convergence of the CCNFSE solutions. And then, by POD, we establish a reduced-order extrapolated Crank-Nicolson finite spectral element (ROECNFSE) method and analyze the existence, stability, and convergence of the ROECNFSE solutions as well as offer the flowchart for seeking ROECNFSE solutions. Finally, we use two sets of numerical experiments to validate that the numerical computational consequences are accorded with the theoretical ones such that the effectiveness and feasibility of the ROECNFSE method are further verified. Both theory and method in this paper is new and completely different from the existing reduced-order methods. (C) 2018 Elsevier Inc. All rights reserved.
机译:在本文中,我们主要利用适当的正交分解(POD)来减少关于Vorticity的二维(2D)非静止BoussinesQ方程的经典曲柄-Nicols有限谱元(CCNFSE)方法的系数矢量的顺序 - 流函数使减少顺序方法维护CCNFSE方法的所有优点。朝此结束,我们首先使用关于涡流流函数的二维(2D)非静止BoussinesQ方程的二阶时间精度来建立CCNFSE格式,并分析CCNFSE解决方案的存在,稳定性和收敛性。然后,通过POD,我们建立了一阶推断的曲柄 - 尼科尔森有限谱元素(Roecnfse)方法,并分析了Roecnfse解决方案的存在,稳定性和收敛,以及为寻求Roecnfse解决方案提供流程图。最后,我们使用两组数值实验来验证,符合理论上的数值计算后果,从而进一步验证了roecnfse方法的有效性和可行性。本文的理论和方法都与现有的减少阶方法完全不同。 (c)2018 Elsevier Inc.保留所有权利。

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