MathLabs

Differential equations and dynamical systems

The Navier–Stokes equations

The equations governing fluid flow, whose smoothness in three dimensions is a Millennium Prize problem.

IntuitionIntuition: what makes fluid flow hard to predict?

Water swirling around a paddle, smoke curling upward, air rushing over a wing — all of these are described by one system of equations. The Navier–Stokes equations track a velocity field uu (how fast and in which direction the fluid moves at every point) and a pressure field pp, using the viscosity ν\nu (how 'sticky' the fluid is) to balance the forces that push, twist, and slow it down.

3D helicoid surface illustrating a twisting vortex line
A helicoid — a stylized vortex line twisting through space. In three dimensions a vortex tube can spin faster as it is stretched (vortex stretching), the mechanism believed to drive the extreme behavior that makes the 3D smoothness problem so hard.

UndergraduateThe equations

Definition: Incompressible Navier–Stokes equations

For an incompressible, viscous fluid filling a region, the velocity field u(x,t)u(x,t) and pressure p(x,t)p(x,t) (normalized by the constant density) satisfy a momentum balance and an incompressibility constraint. Together these express Newton's second law applied to a fluid element, plus the requirement that fluid volume is conserved as it moves.

∂tu+(u⋅∇)u=−∇p+νΔu\partial_t u+(u\cdot\nabla)u=-\nabla p+\nu\Delta u

Reading left to right: ∂tu\partial_t u is how the velocity changes over time at a fixed point; (u⋅∇)u(u\cdot\nabla)u is convection — fluid carrying its own momentum along with it, the nonlinear term responsible for most of the difficulty; −∇p-\nabla p is the force from pressure differences pushing fluid from high to low pressure; and νΔu\nu\Delta u is viscous diffusion, which smooths out sharp velocity gradients.

∇⋅u=0\nabla\cdot u=0

The incompressibility condition ∇⋅u=0\nabla\cdot u=0 says fluid does not pile up or vanish — volume is conserved as it moves. Whether the resulting flow looks smooth (laminar) or chaotic (turbulent) is largely governed by the Reynolds number Re=UL/ν\mathrm{Re}=UL/\nu, comparing a characteristic speed UU and length LL of the flow to the viscosity ν\nu: small Re\mathrm{Re} means viscosity dominates and damps out disturbances, large Re\mathrm{Re} means inertia dominates and small disturbances can grow into turbulence.

Flow regimes by Reynolds number
RegimeTypical ReBehavior
LaminarRe≲103\mathrm{Re}\lesssim 10^{3}Smooth parallel layers, no mixing between them
Transitional103≲Re≲10410^{3}\lesssim\mathrm{Re}\lesssim 10^{4}Intermittent bursts of disorder inside an otherwise smooth flow
TurbulentRe≳104\mathrm{Re}\gtrsim 10^{4}Chaotic eddies across many scales, mixing and drag increase sharply

UndergraduateKey theorems

For any divergence-free initial velocity u0u_0 with finite kinetic energy, the 3D Navier–Stokes equations on the whole space have a global-in-time weak solution uu satisfying the energy inequality.

Why is it true?

Even though the nonlinear term makes the equations too hard to solve exactly, viscosity keeps steadily draining kinetic energy; that single quantitative fact is enough to build an approximate solution and pass to a limit, even without knowing whether the limit is unique or smooth.

Proof

Approximate the equations by a Galerkin projection: pick the eigenfunctions of the Stokes operator as a basis, project the equations onto the span of the first nn of them, and solve the resulting finite-dimensional ODE system for an approximate solution unu_n; standard ODE theory gives existence on a short time interval.

Test the Galerkin equation against unu_n itself. The nonlinear and pressure terms drop out (they are orthogonal to unu_n once the divergence-free constraint is used), leaving the energy identity 12ddt∥un∥L22+ν∥∇un∥L22=0\frac{1}{2}\frac{d}{dt}\|u_n\|_{L^2}^{2}+\nu\|\nabla u_n\|_{L^2}^{2}=0. Integrating in time shows unu_n stays bounded in L∞(L2)∩L2(H1)L^{\infty}(L^2)\cap L^2(H^1) uniformly in nn, and in particular the short-time solution extends to all time.

These uniform bounds let us extract a subsequence of unu_n converging weakly-* in L∞(L2)L^{\infty}(L^2) and weakly in L2(H1)L^2(H^1) to some limit uu. The Aubin–Lions compactness lemma upgrades this to strong convergence in L2L^2 on bounded time-space regions, which is exactly what is needed to pass to the limit in the quadratic nonlinear term (un⋅∇)un→(u⋅∇)u(u_n\cdot\nabla)u_n\to(u\cdot\nabla)u.

Passing to the limit in the weak formulation of the Galerkin equations shows uu satisfies the Navier–Stokes equations in the sense of distributions, and the weak lower semicontinuity of the norm under the limit preserves the energy inequality. This uu is Leray's global weak solution; whether it is unique and smooth is exactly the open Millennium Prize question.

In two space dimensions, for every smooth divergence-free initial velocity with finite energy, the Navier–Stokes equations have a unique solution that stays smooth for all time — no blow-up ever occurs, in contrast with the open 3D case.

Why is it true?

Vorticity in 3D obeys ωt+u⋅∇ω=(ω⋅∇)u+νΔω\omega_t+u\cdot\nabla\omega=(\omega\cdot\nabla)u+\nu\Delta\omega, and the term (ω⋅∇)u(\omega\cdot\nabla)u can amplify vorticity through stretching. In 2D vorticity is a scalar perpendicular to the plane of motion, so that stretching term vanishes identically, leaving only transport and diffusion — nothing left to amplify it without bound.

Proof

Write the vorticity (a scalar in 2D) as ω=∂xu2−∂yu1\omega=\partial_x u_2-\partial_y u_1 and substitute the momentum equation into ∂tω+u⋅∇ω\partial_t\omega+u\cdot\nabla\omega. Because uu is divergence-free and two-dimensional, direct computation shows every term coming from (u⋅∇)u(u\cdot\nabla)u that would stretch ω\omega cancels, leaving the transport-diffusion equation ωt+u⋅∇ω=νΔω\omega_t+u\cdot\nabla\omega=\nu\Delta\omega with no source term.

A transport-diffusion equation with no source obeys a maximum principle: along the trajectory X˙(t)=u(X(t),t)\dot X(t)=u(X(t),t) of a fluid particle, ddtω(X(t),t)=νΔω(X(t),t)\frac{d}{dt}\omega(X(t),t)=\nu\Delta\omega(X(t),t), and diffusion cannot increase a maximum or decrease a minimum. Hence ∥ω(⋅,t)∥L∞≤∥ω0∥L∞\|\omega(\cdot,t)\|_{L^\infty}\le\|\omega_0\|_{L^\infty} for all t≥0t\ge0: the vorticity never blows up.

Once vorticity is bounded uniformly in time, elliptic regularity applied to the stream function (recovering uu from ω\omega) bounds all spatial derivatives of uu in terms of ∥ω∥L∞\|\omega\|_{L^\infty}, so uu stays in every Sobolev space it started in.

Bootstrapping this argument — bounded vorticity gives bounded velocity gradients, which control higher derivatives through the equation itself — shows the solution remains smooth for all t>0t>0, with no finite-time blow-up possible; this is exactly the statement that fails to be known in three dimensions, where the vortex-stretching term (ω⋅∇)u(\omega\cdot\nabla)u is present.

UndergraduateReal-World Applications and Worked Examples

The Navier–Stokes equations are the workhorse of computational fluid dynamics: aircraft and ship hulls are shaped by simulating airflow and water flow around candidate designs before anything is built; weather and climate models integrate a version of these equations over the whole atmosphere and ocean; cardiovascular researchers simulate blood flow through arteries to plan surgery; and astrophysicists use compressible variants to model gas flows in accretion disks and stellar interiors.

Example: Velocity profile between two plates (plane Poiseuille flow)

Steady, incompressible flow driven by a constant pressure gradient runs between two infinite parallel plates at y=−hy=-h and y=hy=h, with no-slip at both walls. Writing G=−1ρdpdx>0G=-\frac{1}{\rho}\frac{dp}{dx}>0 for the (constant) driving term and ν\nu for the kinematic viscosity, find the velocity profile u(y)u(y).

Solution

For flow that is steady and depends only on yy, with u=(u(y),0)u=(u(y),0), the nonlinear term (u⋅∇)u(u\cdot\nabla)u vanishes because uu does not vary in the flow direction xx. The momentum equation reduces to the balance 0=−1ρdpdx+νu′′(y)0=-\frac{1}{\rho}\frac{dp}{dx}+\nu u''(y), i.e. νu′′(y)=−G\nu u''(y)=-G.

Integrating twice in yy gives u(y)=−G2νy2+C1y+C2u(y)=-\frac{G}{2\nu}y^{2}+C_1y+C_2 for constants C1,C2C_1,C_2 fixed by the boundary conditions.

The no-slip condition requires u(h)=u(−h)=0u(h)=u(-h)=0. Plugging in both points and subtracting eliminates C2C_2 and forces C1=0C_1=0 (by symmetry, since the two equations differ only by the sign of the linear term); adding them back gives C2=Gh22νC_2=\frac{Gh^{2}}{2\nu}.

Substituting back, the velocity profile is u(y)=G2ν(h2−y2)u(y)=\frac{G}{2\nu}\left(h^{2}-y^{2}\right): a parabola that is zero at the walls and maximal at the centerline y=0y=0, exactly the classic Poiseuille profile.

Example: Reynolds number: a swimming bacterium versus a car on the highway

Estimate the Reynolds number Re=UL/ν\mathrm{Re}=UL/\nu for (a) a bacterium of length L=2×10−6 mL=2\times10^{-6}\text{ m} swimming at U=2×10−5 m/sU=2\times10^{-5}\text{ m/s} in water (ν≈10−6 m2/s\nu\approx10^{-6}\text{ m}^2/\text{s}), and (b) a car of length L=4 mL=4\text{ m} moving at U=30 m/sU=30\text{ m/s} in air (ν≈1.5×10−5 m2/s\nu\approx1.5\times10^{-5}\text{ m}^2/\text{s}). What does each value say about which term of the Navier–Stokes equations dominates?

Solution

For the bacterium: Re=(2×10−5)(2×10−6)10−6=4×10−5\mathrm{Re}=\dfrac{(2\times10^{-5})(2\times10^{-6})}{10^{-6}}=4\times10^{-5}, far below 1.

For the car: Re=(30)(4)1.5×10−5=8×106\mathrm{Re}=\dfrac{(30)(4)}{1.5\times10^{-5}}=8\times10^{6}, far above 1.

A tiny Re≪1\mathrm{Re}\ll1 means the viscous term νΔu\nu\Delta u dominates the nonlinear term (u⋅∇)u(u\cdot\nabla)u: the bacterium experiences water as thick as honey, inertia is irrelevant, and it must use corkscrew-like strokes to move at all, since simply flapping back and forth produces no net motion at such low Re\mathrm{Re}.

A huge Re≫1\mathrm{Re}\gg1 means the nonlinear convective term dominates viscosity almost everywhere except in a thin boundary layer near the car's surface: the flow readily separates and becomes turbulent in the wake, which is exactly why aerodynamic drag and shape design matter so much at highway speeds.

What does the incompressibility condition ∇⋅u=0\nabla\cdot u=0 mean physically?

A pipe flow has characteristic speed U=0.5 m/sU=0.5\text{ m/s}, characteristic length L=0.02 mL=0.02\text{ m}, and kinematic viscosity ν=1×10−6 m2/s\nu=1\times10^{-6}\text{ m}^2/\text{s}. Compute Re=UL/ν\mathrm{Re}=UL/\nu and say whether the flow is likely laminar or turbulent.

Why is simulating airflow over an aircraft wing at cruise speed computationally expensive compared to slow, honey-like flows?

Exactly what does the Navier–Stokes Millennium Prize problem ask for?

References

  1. Charles L. Fefferman (2000). Existence and Smoothness of the Navier-Stokes Equation (Millennium Prize Problem)
  2. Terence Tao (2016). Finite time blowup for an averaged three-dimensional Navier-Stokes equation · arXiv:1402.0290
  3. L. Caffarelli, R. Kohn, L. Nirenberg (1982). Partial regularity of suitable weak solutions of the Navier-Stokes equations