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The heat equation: where it comes from
Conservation of energy plus Fourier's law, diffusion as a random walk, and the diffusion length.
Let \( u(x,t) \) be the temperature in a thin rod with density \( \rho \), specific heat \( c \) and conductivity \( K \). The heat in a segment \( [a, b] \) is \( \int_a^b c\rho\,u\,dx \), and it changes only through the flux \( \phi \) at the ends: \( \frac{d}{dt}\int_a^b c\rho\,u\,dx = \phi(a,t) - \phi(b,t) = -\int_a^b \phi_x\,dx \). Since the segment is arbitrary, \( c\rho\,u_t = -\phi_x \). Fourier's law says heat flows down the gradient, \( \phi = -K u_x \), and together they give \[ u_t = k\,u_{xx}, \qquad k = \frac{K}{c\rho}. \] In a region of space the same argument with the divergence theorem gives \( u_t = k\,\Delta u \).
The equation has a second derivation that explains its universality. A particle hops a distance \( h \) left or right every \( \tau \) seconds. The probability \( u \) of finding it at \( x \) obeys \( u(x, t + \tau) = \tfrac12 u(x - h, t) + \tfrac12 u(x + h, t) \); expanding both sides in Taylor series gives \( \tau u_t \approx \tfrac12 h^2 u_{xx} \), the heat equation with \( k = h^2/(2\tau) \). Diffusion of ink, of heat and of probability are one equation.
The equation contains its own length scale. It is unchanged by \( x \mapsto \lambda x,\ t \mapsto \lambda^2 t \), so the only length that can be built from \( k \) and a time \( t \) is \( \sqrt{kt} \): in four times as long, heat spreads twice as far. The example checks that \( u = e^{-t}\sin x \) solves \( u_t = u_{xx} \): the sine shape stays put while its amplitude decays.
Picture it: \( u_{xx} \) measures bending. Where the temperature graph is concave down (a local hot spot), \( u_{xx} < 0 \) and the temperature falls; where it is concave up, the point warms. Every bump flattens.
Think it: for a rod with fixed ends, \( \frac{d}{dt}\int \tfrac12 u_x^2\,dx = \int u_x u_{xt}\,dx = -\int u_{xx}u_t\,dx = -\int u_t^2\,dx \le 0 \). The heat equation runs downhill on the Dirichlet energy as fast as possible, a gradient flow. The viscous term \( \nu\Delta u \) in Navier-Stokes comes from the same structure: a conservation law (momentum) closed by a constitutive law (stress proportional to velocity gradient).
Apstrādāts piemērs · does u = exp(-t)*sin(x) satisfy u_t = u_xx
Does u = exp(-t)*sin(x) satisfy u_t = u_xx
Solis pēc kārtas
- u = e^{- t} \sin{\left(x \right)},\qquad u_{t} = u_{xx}
To check a solution, compute every derivative the equation uses, substitute, and see whether both sides agree.
- u_{t} = - e^{- t} \sin{\left(x \right)}
Differentiate with respect to t, holding the other variables constant.
- u_{xx} = - e^{- t} \sin{\left(x \right)}
Differentiate 2 times with respect to x.
- \text{LHS} = - e^{- t} \sin{\left(x \right)},\quad \text{RHS} = - e^{- t} \sin{\left(x \right)}
Substitute the derivatives into both sides.
- \text{LHS} - \text{RHS} = 0
Subtract and simplify.
- \text{LHS} - \text{RHS} = 0\ \checkmark
The two sides agree for every value of the variables, so it is a solution.
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Kā lietot: The heat equation: where it comes from
- Write the conservation law for an arbitrary segment: rate of change of content equals net inward flux.
- Turn the flux difference into an integral of the flux derivative and let the segment shrink.
- Close the equation with a constitutive law (Fourier or Fick): flux proportional to minus the gradient.
- Read off the diffusivity k and the diffusion length sqrt(k t).
Jautājumi, ko cilvēki vaicā
Why does the heat equation have a first time derivative but a second space derivative?
Conservation contributes one derivative of the flux, and the constitutive law makes the flux itself a derivative of u. Time enters only through the rate of change of content.
Does heat really spread infinitely fast?
In the equation, yes: starting from a temperature that is non-negative and not identically zero, for t > 0 the solution on the whole line is positive everywhere. The effect far away is exponentially small, which is why the model is excellent in practice.
What should I know before starting?
Partial derivatives, the divergence theorem and multiple integrals from multivariable calculus; linear second-order ODEs; eigenvalues from linear algebra; and enough analysis to be comfortable with uniform convergence and integrals over infinite intervals.
Why are there so few formulas for solutions?
Explicit solutions exist for linear equations with constant coefficients on simple domains. Almost everything else (curved domains, variable coefficients, nonlinear terms) has none, so the modern subject proves that a solution exists and estimates its size and smoothness without ever writing it down.
How does this course lead to the Navier-Stokes problem?
The Navier-Stokes equations are a heat equation for the velocity with a transport term and a pressure that is found from a Poisson equation. Reading the Millennium problem needs energy estimates, weak solutions, Sobolev spaces, local existence with a blow-up criterion, and the scaling argument that explains why three dimensions is hard. The last six lessons build those.
What do elliptic, parabolic and hyperbolic mean?
Elliptic equations (Laplace) describe equilibrium and smooth everything; parabolic equations (heat) describe diffusion forward in time; hyperbolic equations (waves) carry signals at finite speed and keep their sharp edges. The type decides which data make a sensible problem.
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