arXivDaily arXiv每日学术速递 周一至周五更新
arXiv math/0702339math.AP
收藏

Anti-symmetric哈密尔顿算子(II):Navier-Stokes方程和其他非线性演化的变分解

Anti-symmetric Hamiltonians (II): Variational resolutions for Navier-Stokes and other nonlinear evolutions

Nassif Ghoussoub, Abbas Moameni

摘要:

非线性自对偶变分原理建立在前面的文件[8] -虽然足够好,很容易适用于许多固定的非线性偏微分方程-但没有涵盖的情况下,非线性发展,如Navier-Stokes方程。其中一个原因是相关路径空间上的自对偶泛函不满足禁止性条件。我们在这里证明了这样的原理对于I(u)= \int_0^T \Big [ L(t,u(t),\dot {u}(t)+Λu(t))+< Λu(t),u(t)> \Big ] dt + \ell(u(0)- u(T),\frac {u(T)+ u(0)}{2})形式的泛函仍然成立,其中$L$(分别为,$\ell$)是状态空间上的反自对偶拉格朗日量(分别为,边界空间),而$Λ$是路径空间上合适的非线性算子。因此,我们提供了一个变分公式和决议涉及非线性算子的发展方程,如Navier-Stokes方程(在2和3维)与各种边界条件。在2维中,我们恢复了相应的初值问题以及周期和反周期的众所周知的解决方案,而在3维中,我们得到的Leray解决方案的初值问题,而且解决方案满足$u(0)=αu(T)$对于任何给定的$α$在$(-1,1)$。我们的方法是相当普遍的,并适用于许多其他情况。

英文摘要:

The nonlinear selfdual variational principle established in a preceeding paper [8] -- though good enough to be readily applicable in many stationary nonlinear partial differential equations -- did not however cover the case of nonlinear evolutions such as the Navier-Stokes equations. One of the reasons is the prohibitive coercivity condition that is not satisfied by the corresponding selfdual functional on the relevant path space. We show here that such a principle still hold for functionals of the form I(u)= \int_0^T \Big [ L (t, u(t),\dot {u}(t)+Λu(t)) +< Λu(t), u(t) > \Big ] dt +\ell (u(0)- u(T), \frac {u(T)+ u(0)}{2}) where $L$ (resp., $\ell$) is an anti-selfdual Lagrangian on state space (resp., boundary space), and $Λ$ is an appropriate nonlinear operator on path space. As a consequence, we provide a variational formulation and resolution to evolution equations involving nonlinear operators such as the Navier-Stokes equation (in dimensions 2 and 3) with various boundary conditions. In dimension 2, we recover the well known solutions for the corresponding initial-value problem as well as periodic and anti-periodic ones, while in dimension 3 we get Leray solutions for the initial-value problems, but also solutions satisfying $u(0)=αu(T)$ for any given $α$ in $(-1,1)$. Our approach is quite general and does apply to many other situations.

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。

↑