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将放松的静力学假设的随机原始方程解释为三维随机纳维-斯托克斯方程的高阶近似

Interpretation of stochastic primitive equations with relaxed hydrostatic assumption as a higher order approximation of 3D stochastic Navier-Stokes

Univ Rennes, CNRS, IRMAR UMR 6625(雷恩大学,法国国家科学研究中心,IRMAR UMR 6625);Univ Rennes, INRIA, IRMAR UMR 6625(雷恩大学,法国国家信息与自动化研究所,IRMAR UMR 6625)

Arnaud Debussche, Étienne Mémin, Antoine Moneyron

AI总结:

本文通过放松静力学假设,将随机原始方程作为三维随机纳维-斯托克斯方程的高阶近似进行研究。

摘要:

本文研究了三维纳维-斯托克斯方程的随机表示的解对它们的原始方程对应解的收敛性。我们的分析涵盖了弱收敛和强收敛两种情形,分别对应刚性 lid 和"完全周期"边界条件。此外,我们探讨了在随机原始方程中放松静力学假设的影响,通过保留martingale项作为静力学平衡的偏差来实现。这种修改模型通过特定的渐近缩放获得,仅在随机框架内可访问,能够在保持原始方程形式的同时捕捉非静力学效应。所得到的广义静力学模型在使用合适滤波器对额外项进行正则化时已被证明是良好的。在此设置下,我们证明该模型在适当缩放的噪声下为三维纳维-斯托克斯方程提供了高阶近似。

英文摘要:

In this paper, we investigate the convergence of solutions of a stochastic representation of the three-dimensional Navier-Stokes equations to those of their primitive equations counterpart. Our analysis covers both weak and strong convergence regimes, corresponding respectively to rigid-lid and "fully periodic" boundary conditions. Furthermore, we explore the impact of relaxing the hydrostatic assumption in the stochastic primitive equations by retaining martingale terms as deviations from hydrostatic equilibrium. This modified model, obtained through a specific asymptotic scaling accessible only within the stochastic framework, captures non-hydrostatic effects while remaining within the primitive equations formalism. The resulting generalized hydrostatic model has been shown to be well-posed when the additional terms are regularized using a suitable filter for divergence-free noises under suitable assumptions. Within this setting, we demonstrate that the model provides a higher-order approximation of the 3D Navier-Stokes equations for appropriately scaled noises.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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