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Higher Order Regularity, Long Term Dynamics, and Data Assimilation for Magnetohydrodynamic Flows

机译:磁流体动力流的高阶正则性,长期动力学和数据同化

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摘要

First we consider various inviscid equations of fluid dynamics and show that if the initial data is analytic in the space variables, then the resulting flows extend as analytic functions of both space and time variables, with explicit estimates of the analyticity radius. We then consider higher order regularity of the viscous magnetohydrodynamic equations for an incompressible conductive fluid. We establish the Gevrey regularity of solutions when the initial data is in a Sobolev class, of possibly negative order, in two and three spatial dimensions. In particular, we show that solutions evolving from singular initial data instantaneously become analytic, with the analyticity radius eventually expanding in time. This in turn allows us to establish decay in higher order Sobolev norms. Finally, using a recently developed data assimilation algorithm based on linear feedback control, we show that when the initial data is unknown, sparse measurement data is sufficient for accurate reconstruction of magnetohydrodynamic flows. This algorithm convergences exponentially in time to the reference solution and moreover, the reconstruction is exact on the attractor.
机译:首先,我们考虑各种流体动力学的无粘性方程式,并表明,如果初始数据是在空间变量中进行分析的,则结果流将扩展为空间和时间变量的分析函数,并具有对分析半径的显式估计。然后,我们考虑了不可压缩的导电流体的粘性磁流体动力学方程的高阶正则性。当初始数据处于二维和三个空间维的Sobolev类中(可能为负序)时,我们建立解的Gevrey正则性。特别是,我们表明,从奇异初始数据演变而来的解决方案会立即变为解析性,而解析半径最终会随着时间的推移而扩大。反过来,这又使我们能够建立高阶Sobolev规范的衰减。最后,使用最近开发的基于线性反馈控制的数据同化算法,我们表明,当初始数据未知时,稀疏的测量数据足以准确重建磁流体动力流。该算法在时间上以指数方式收敛到参考解,此外,在吸引子上的重构是精确的。

著录项

  • 作者

    Hudson, Joshua.;

  • 作者单位

    University of Maryland, Baltimore County.;

  • 授予单位 University of Maryland, Baltimore County.;
  • 学科 Applied mathematics.;Fluid mechanics.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 144 p.
  • 总页数 144
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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