Burgers' equation (Rev #8)

**Burger’s equation** is a partial differential equation that was originally proposed as a simplified model of turbulence as exhibited by the full-fledged Navier-Stokes equations. The turbulent behaviour especially of a stochastically forced Burgers’ equation is sometimes dubbed **Burgulence**.

It is a nonlinear equation for which exact solutions are known and is therefore important as a benchmark problem for numerical methods.

In one spatial dimension it is

$\frac{\partial u}{\partial t} + u \frac{\partial u}{\partial x} - \nu \frac{\partial^2 u}{\partial x^2} = 0$

in a spatial region $\Omega$, positive time $t \gt 0$ and with a real constant $\nu \gt 0$ (it is the kinematic viscosity), with appropriate boundary and initial conditions.

We write for the initial condition:

$u(x, o) = u_0(x)$

It is possible to write Burgers’s equation in a conservation form (with a flow that is conserved):

$\frac{\partial u}{\partial t} + \frac{\partial F(u)}{\partial x} = 0$

with

$F(u) := \frac{1}{2} u^2 - \nu \frac{\partial u}{\partial x}$

It is possible to transform Burgers’ equation to the heat equation via

$u = -2 \nu \frac{\phi_x}{\phi}$

so that the function $\phi$ satisfies

$\frac{\partial \phi}{\partial t} - \nu \frac{\partial^2 \phi}{\partial x^2} = 0$

iff the function $u$ satisfies Burgers’ equation.

We will use a spectral method for an approximate solution of Burgers’ equation on the domain $\Omega = (0, 2 \pi)$. “Fourier” means that we will use an approximation via a Fourier series, and “Galerkin” means that we will use the approximation functions also as test functions. As usual, we will use the spectral approximation for the spatial dimension only, not for the temporal. This results in an ansatz that is usually called “separation of variables”. One could indeed call “separation of variables” a special case of spectral methods.

Our ansatz for the approximate solution is:

$u_N (x, t) = \sum_{k = - \frac{N}{2}}^{k = \frac{N}{2} - 1} \hat{u}_n (t) e^{i k t}$

Since we choose as test functions our approximation functions, the conditions resulting from the requirement that $\langle \; M(f_{\alpha}), \; h_i \; \rangle = 0$ (see spectral methods for an explanation of the nomenclature) results in our case in the system of equations

$\int_0^{2 \pi} (\frac{\partial u_N}{\partial t} + u_N \frac{\partial u_N}{\partial x} - \nu \frac{\partial^2 u_N}{\partial x^2} ) \; e^{i k t} \; d x = 0$

for $k = - \frac{N}{2}...\frac{N}{2} - 1$ .

This results in a system of ordinary differential equations for the Fourier components $\hat{u}_n$:

$\frac{d \hat{u}_k}{d t} + \mathcal{F}(u_N \frac{\partial u_N}{\partial x})_k + k^2 \; \nu \; \hat{u}_k = 0$

with

$\mathcal{F}(u_N \frac{\partial u_N}{\partial x})_k := \frac{1}{2 \pi} \; \int_0^{2 \pi} \; u_N \frac{\partial u_N}{\partial x} \; e^{- i k x} d x$

The initial conditions are of course:

$\hat{u}_k (0) = \frac{1}{2 \pi} \int_0^{2 \pi} \; u(x, 0) \; e^{- i k x} d x$

and we have left the boundary conditions unspecified.

When we consider the Fourier transformation of a product of functions

$\mathcal{F} (u v) = \frac{1}{2 \pi} \int_0^{2 \pi} \; u v \; e^{- i k x} d x$

and assume that these functions are trigonometric polynomials, we get the k-th component in form of a convolution sum:

$\mathcal{F}_k (u v) = \sum_{i + j = k} \hat{u}_i \; \hat{v}_j$

Convolution sums can be efficiently evaluated by using a Fast Fourier Transform, for example.

The Burgers’ equation with stochastic forcing is, besides many other applications, also a sandbox problem to study turbulence. Due to the stochastic forcing term, the Burgers’ equation becomes a stochastic partial differential equation.

- Burgers equation, Wikipedia

A lot of information including the derivation of the solution on closed form can be found here:

- Lokenath Debnath:
*Nonlinear partial differential equations for scientists and engineers.*(ZMATH)

category: mathematical methods