Your browser does not support the canvas element.

Jincheng Yang

The University of Chicago

%--Paired Delimiters-- $ \newcommand{\abs}[1]{\left\lvert #1 \right\rvert} \newcommand{\ang}[1]{\left\langle #1 \right\rangle} \newcommand{\bkt}[1]{\left\lbrack #1 \right\rbrack} \newcommand{\nor}[1]{\left\lVert #1 \right\rVert} \newcommand{\pth}[1]{\left( #1 \right)} \newcommand{\set}[1]{\left\lbrace #1 \right\rbrace} \newcommand{\abset}[1]{\abs{\set{#1}}} \newcommand{\ptset}[1]{\pth{\set{#1}}} $ %--Operators-- $ \newcommand{\grad}{\nabla} \newcommand{\La}{\Delta} \renewcommand{\div}{\operatorname{div}} \newcommand{\curl}{\operatorname{curl}} \newcommand{\tr}{\operatorname{tr}} \newcommand{\Hess}{\operatorname{Hess}} \newcommand{\mm}{\mathcal{M}} \newcommand{\inv}{^{-1}} \newcommand{\tensor}{\otimes} \newcommand{\cross}{\times} \newcommand{\weak}[1]{\xrightharpoonup{#1}} $ %--Notations-- $ \newcommand{\Vol}{\mathrm{Vol}} \newcommand{\loc}{\mathrm{loc}} \renewcommand{\dim}{\mathrm{dim}} \newcommand{\Span}{\mathrm{Span}} \newcommand{\diam}{\mathrm{diam}} \newcommand{\Id}{\mathrm{Id}} \newcommand{\Lip}{\mathrm{Lip}} \newcommand{\dist}{\mathrm{dist}} \newcommand{\inv}{^{-1}} $ %--Algebra-- $ \newcommand{\hfsq}[1]{\frac{\abs{#1} ^2}{2}} $ %--Sets-- $ \newcommand{\R}{\mathbb{R}} \newcommand{\RR}[1]{\R ^{#1}} \newcommand{\Rd}{\RR d} \newcommand{\Rn}{\RR n} \renewcommand{\S}{\mathbb{S}} \renewcommand{\Z}{\mathbb{Z}} \newcommand{\T}{\mathbb{T}} \newcommand{\ssubset}{\subset \subset} \newcommand{\spt}{\operatorname{spt}} \newcommand{\supp}{\operatorname{supp}} \newcommand{\diam}{\mathrm{diam}} \newcommand{\diag}{\operatorname{diag}} \renewcommand{\dim}{\mathrm{dim}} \newcommand{\dimH}{\mathrm{dim} _\mathcal{H}} \newcommand{\Sing}{\mathrm{Sing}} \newcommand{\Vol}{\mathrm{Vol}} \newcommand{\inds}[1]{\mathbf1_{\set{#1}}} \newcommand{\ind}[1]{\mathbf1_{#1}} $ %--Summations-- $ \newcommand{\SUM}[3]{\sum \cnt{#1}{#2}{#3}} \newcommand{\PROD}[3]{\prod \cnt{#1}{#2}{#3}} \newcommand{\cnt}[3]{ _{#1 = #2} ^{#3} } \newcommand{\seq}[2]{\set{#1 _{#2}} _{#2}} \newcommand{\seqi}[2]{\set{#1 _{#2}} \cnt{#2}1i} $ %--Superscript and Subscript $ \newcommand{\pp}[1]{^{(#1)}} \newcommand{\bp}[1]{_{(#1)}} \newcommand{\pps}[2][1]{^{#2 + #1}} \newcommand{\bps}[2][1]{_{#2 + #1}} \newcommand{\pms}[2][1]{^{#2 - #1}} \newcommand{\bms}[2][1]{_{#2 - #1}} $ %--Notations-- $ \newcommand{\loc}{\mathrm{loc}} \newcommand{\Span}{\operatorname{Span}} \newcommand{\argmin}{\operatorname{argmin}} \newcommand{\argmax}{\operatorname{argmax}} \newcommand{\osc}{\operatorname{osc}} \newcommand{\Id}{\mathrm{Id}} \newcommand{\Lip}{\operatorname{Lip}} \newcommand{\Leb}{\operatorname{Leb}} \newcommand{\PV}{\mathrm{P.V.}} \newcommand{\dist}{\operatorname{dist}} \newcommand{\at}[1]{\bigr\rvert _{#1}} \newcommand{\At}[1]{\biggr\rvert _{#1}} \newcommand{\half}{\frac12} $ %--Text-- $ \newcommand{\inn}{\text{ in }} \newcommand{\onn}{\text{ on }} \renewcommand{\ae}{\text{ a.e. }} \newcommand{\st}{\text{ s.t. }} \newcommand{\forr}{\text{ for }} \newcommand{\as}{\text{ as }} $ %--Differential-- $ \newcommand{\d}{\mathop{\kern0pt\mathrm{d}}\!{}} \newcommand{\dt}{\d t} \newcommand{\dx}{\d x} \newcommand{\dy}{\d y} \newcommand{\ptil}{\partial} \newcommand{\pt}{\ptil _t} \newcommand{\pfr}[2]{\frac{\partial #1}{\partial #2}} \newcommand{\dfr}[2]{\frac{\mathrm{d} #1}{\mathrm{d} #2}} \newcommand{\ddt}{\dfr{}t} \newcommand{\pthf}[2]{\pth{\frac{#1}{#2}}} $ %--Integral-- $ \newcommand{\intR}{\int _0 ^\infty} \newcommand{\intRn}{\int _{\Rn}} \newcommand{\intRd}{\int _{\Rd}} \newcommand{\fint}{-\!\!\!\!\!\!\int} \newcommand{\intset}[1]{\int _{\set{#1}}} $ %--Greek-- $ \newcommand{\e}{\varepsilon} \newcommand{\vp}{\varphi} $ %--Norm-- $ \newcommand{\nmL}[2]{\nor{#2} _{L ^#1}} $

Research Blog

Couette flow

2022, August 16

$ \newcommand{\cN}{\mathcal N} \newcommand{\cA}{\mathcal A} $

Couette Frame

Let $(x, y) \in \T \times \R$. Couette flow refers to the shear flow $u _c (t, x, y) = y e _1$. If $u$ solves a transport-diffusion equation

\[\pt u + b \cdot \grad u = \nu \La u + f,\]

that is, $u$ is transported by some flow $b$ and diffuse at viscosity $\nu$, then we can make a change of variable $z = x - t y$, and

\[\tilde u (t, z, y) = u (t, x, y), \tilde b (t, z, y) = b (t, x, y) - u _c (t, x, y), \tilde f (t, z, y) = f (t, x, y).\]

Then

\[\begin{align*} \pt u (t, x, y) &= \pfr{}t \tilde u (t, x - t y, y) = \pt \tilde u - y \ptil _z \tilde u, \\ \ptil _x u (t, x, y) &= \pfr{}x \tilde u (t, x - t y, y) = \ptil _z \tilde u, \\ \ptil _y u (t, x, y) &= \pfr{}y \tilde u (t, x - t y, y) = -t \ptil _z \tilde u + \ptil _y \tilde u, \end{align*}\]

therefore

\[\begin{align*} (\pt + u _c \cdot \grad) u (t, x, y) &= \pt \tilde u (t, z, y), \\ (\ptil _y + t \ptil _x) u (t, x, y) &= \ptil _y \tilde u (t, z, y). \end{align*}\]

Moreover, denote $\grad _L = (\ptil _z, \ptil _y - t \ptil _z)$ and $\La _L = \grad _L \cdot \grad _L = \ptil ^2 _z + (\ptil _y - t \ptil _z) ^2$, then $\grad u = \grad _L \tilde u$, $\La u = \La _L \tilde u$. Hence, $\tilde u$ solves the system

\[\begin{align*} \pt \tilde u + \tilde b \cdot \grad _L \tilde u = \nu \La _L \tilde u + \tilde f. \end{align*}\]

Fourier transform

From now on, we drop the tildes. Suppose we adopt the Fourier transform $(z, y) \to (k, \eta)$, then

\[\hat \grad _L = \begin{pmatrix} i k \\ i \eta - i t k \end{pmatrix} = i k \begin{pmatrix} 1 \\ \hat \eta - t \end{pmatrix}, \qquad \hat \La _L = - k ^2 (1 + (\hat \eta - t) ^2) = - k ^2 \ang{t - \hat \eta} ^2.\]

where $\hat \eta := \eta / k$ (we are interested in the non-zero mode $k \neq 0$).

Inviscid Damping

Inviscid damping refers to the phenomenon that some sort of decay exists in the Couette frame albeit without viscosity. Consider the following inviscid equation

\[\pt u = -\ptil _z ^2 (- \La _L) \inv u + f.\]

At the first sight, one may think the right hand side seems to be a positive zeroth order Riesz transform of $u$, hence there should be some exponential growth. However, let $\cN$ be a Fourier multiplier with symbol $\hat \cN = \ang{t - \hat \eta}$, then

\[\pt u = -\ptil _z ^2 (- \La _L) \inv u + f = \cN ^{-2} u + f.\]

Let $\cA$ be a time-dependent Fourier multiplier defined as $\cA \at{t = 0} = \Id$, $\dot \cA = [\pt, \cA] = - 2 \cA \cN ^{-2}$, then

\[\pt \cA u = \dot \cA u + \cA \pt u = - 2 \cA \cN ^{-2} u + \cA \cN ^{-2} u + \cA f = - \cA \cN ^{-2} u + \cA f.\]

This means

\[\ddt \frac{\nor{\cA u} _{H ^s} ^2}2 + \nor{\cN ^{-1} \cA u} _{H ^s} ^2 \le (\cA f, \cA u) _{H ^s} \le \half \nor{\cN ^{-1} \cA u} _{H ^s} ^2 + \half \nor{\cN \cA f} _{H ^s} ^2.\]

Moreover, note that

\[\begin{cases} \pt \hat \cA = - 2 \ang{t - \hat \eta} ^{-2} \hat \cA \\ \hat \cA (0) = 1 \end{cases}\]

which has a solution that is uniformly bounded in $\hat \eta$, so $\cA$ is a bounded multiplier from both above and below, hence

\[\ddt {\nor{\cA u} _{H ^s} ^2} + \nor{\cN ^{-1} u} _{H ^s} ^2 \le C \nor{\cN f} _{H ^s} ^2.\]

Hence

\[\nor{u} _{H ^s} ^2 (t) + \int _0 ^t \nor{\cN \inv u} _{H ^s} ^2 (\tau) \d \tau \le C \nor{u _{in}} _{H ^s} ^2 + C \int _0 ^t \nor{\cN f} _{H ^s} ^2 (\tau) \d \tau.\]

Note that $\hat \cN = \ang{t - \hat \eta} \le \ang{\hat \eta} \ang{t}$, so

\[\nor{u} _{H ^s} ^2 (t) + \int _0 ^t \ang \tau ^{-2} \nor{u} _{H ^{s - 1}} ^2 (\tau) \d \tau \le C \nor{u _{in}} _{H ^s} ^2 + C \int _0 ^t \ang{\tau} ^2 \nor{f} _{H ^{s + 1}} ^2 (\tau) \d \tau.\]

Here we used that for $\alpha \in \mathbb R$,

\[\ang t ^\alpha \nor{u} _{H ^{s - |\alpha|}} \le \nor{\cN ^\alpha u} _{H ^s} \le \ang t ^\alpha \nor{u} _{H ^{s + |\alpha|}}.\]

Enhanced Dissipation

Consider the following equation

\[\pt u = \nu \La _L u + f.\]

The naive energy estimates shows the following estimate on the energy dissipation,

\[\ddt \frac{\nor u _{H ^s} ^2}2 + \nu \nor{\grad _L u} _{H ^s} ^2 = (u, f) _{H ^s}.\]

Now, denote $\hat \nu = k ^2 \nu$, then $-\nu \La _L = \hat \nu \cN ^2$, thus (with abuse of notation we regard $\hat \nu$ as a multiplier as well)

\[\pt u + \hat \nu \cN ^2 u = f.\]

To control the enhanced dissipation, we separate the dissipation by

\[\pt u + \max \{\hat \nu \cN ^2, \hat \nu ^\frac13\} u = (\hat \nu ^\frac13 - \hat \nu \cN ^2) _+ u + f.\]

Let $\cA$ be a time-dependent Fourier multiplier defined as $\cA \at{t = 0} = \Id$, and similar as before let $\dot \cA = [\pt, \cA] = - \cA (\hat \nu ^\frac13 - \hat \nu \cN ^2) _+$, then

\[\pt \cA u + \max \{\hat \nu \cN ^2, \hat \nu ^\frac13\} \cA u = \cA f.\]

So

\[\ddt \frac{\nor{\cA u} _{H ^s} ^2}2 + \nor{\hat \nu ^{\frac16} \cA u} _{H ^s} ^2 \le (\cA f, \cA u) _{H ^s} \le \half \nor{\hat \nu ^{\frac16} \cA u} _{H ^s} ^2 + \half \nor{\hat \nu ^{-\frac16} \cA f} _{H ^s} ^2\]

Again, note that $(\hat \nu ^\frac13 - \hat \nu \cN ^2) _+$ is supported near $\abs{t - \hat \eta} < \hat \nu ^{-\frac13}$, which again makes $\cA$ uniformly bounded from both sides. Therefore,

\[\ddt {\nor{\cA u} _{H ^s} ^2} + \nu ^\frac13 \nor{|\ptil _z| ^\frac13 u} _{H ^s} ^2 \le (\cA f, \cA u) _{H ^s} \le \nu ^{-\frac13} \nor{|\ptil _z| ^{-\frac13} f} _{H ^s} ^2\]

Now $\nor{\abs{\ptil _z} ^\frac13 u} _{H ^s} ^2 \ge c \nor{\cA u} _{H ^s}$, so

\[\nor{u} _{H ^s} ^2 (t) \le C \nor{u _{in}} _{H ^s} ^2 e ^{-c \nu ^\frac13 t} \int _0 ^t e ^{c \nu ^\frac13 \tau} \nu ^{-\frac13} \nor{f} _{H ^s} ^2 (\tau) \d \tau.\]

Reference: C. Zhai and W. Zhao, “Stability Threshold of the Couette Flow for Navier–Stokes Boussinesq System with Large Richardson Number $\boldsymbol{\gamma}^{\boldsymbol{2}} \boldsymbol{\gt} \frac{\boldsymbol 1}{\boldsymbol 4}$,” SIAM J. Math. Anal., vol. 55, no. 2, pp. 1284–1318, Apr. 2023, doi: 10.1137/22M1495160. arXiv:2204.09662

Recent Posts

22 Jan 2021

De Giorgi

14 Apr 2020

Lorentz Space