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首页> 外文期刊>Journal of Computational Physics >Reliable reduced-order models for time-dependent linearized Euler equations
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Reliable reduced-order models for time-dependent linearized Euler equations

机译：依赖时间的线性化Euler方程的可靠降阶模型

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摘要

Development of optimal reduced-order models for linearized Euler equations is investigated. Recent methods based on proper orthogonal decomposition (POD), applicable for high-order systems, are presented and compared. Particular attention is paid to the link between the choice of the projection and the efficiency of the reduced model. A stabilizing projection is introduced to induce a stable reduced-order model at finite time even if the energy of the physical model is growing. The proposed method is particularly well adapted for time-dependent hyperbolic systems and intrinsically skew-symmetric models. This paper also provides a common methodology to reliably reduce very large nonsymmetric physical problems.
机译：研究了线性Euler方程的最佳降阶模型的开发。提出并比较了适用于高阶系统的基于适当正交分解（POD）的最新方法。特别要注意投影的选择与简化模型的效率之间的联系。引入稳定投影以在有限的时间诱导稳定的降阶模型，即使物理模型的能量正在增长。所提出的方法特别适合于时间相关的双曲系统和固有的偏斜对称模型。本文还提供了一种可靠的方法来可靠地减少非常大的非对称物理问题。

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