# Numerical solution of PDE:s, Part 10: The thin-film equation

Earlier, I showed how to solve the 1D and 2D versions of the complex Ginzburg-Landau equation, which is an example of a nonlinear partial differential equation, and which had to be linearized for solution with implicit differencing, meaning that the matrix in the linear system was different on each timestep.

Another nonlinear PDE is the so-called thin film equation, which in 2D form reads Here the function $h(x,y,t)$ describes the local thickness of a film of viscous liquid located on top of a solid surface described by the xy-plane. The parameter $\gamma$ is the surface tension of the liquid-gas interface and $\mu$ is the viscosity of the liquid.

An unusual thing about this equation is that it’s fourth order in the spatial coordinates, while most equations in physics are second order DE:s. Some equations of continuum mechanics describing elastic bending of cylinders and plates are also fourth order, so this is not the only example.

In many cases, a corresponding equation with only one spatial coordinate is enough for describing thin film physics, and then the graph of the solution can be though of as depicting an intersection of the film at a single value of y-coordinate. When discretizing this equation, we must note that the factor $h^3$ has to be treated explicitly to get a linear system of equations, just like what had to be done with the $|A|^2$ in the CGLE. Also, we will set $\gamma/(3\mu ) = 1$ to make the equation dimensionless. One correct way to discretize this equation leads to the system where the two index object $\alpha_{j}^{i}$ is Note that now the linear system is not tridiagonal, but heptadiagonal due to the higher order derivatives. Solution of the equation with this differencing scheme and a Gaussian initial condition $h(x,0)$ is done with the following R language code.

library(graphics) #load the graphics library needed for plotting

lx <- 10 #length of the computational domain
lt <- 10 #length of the simulation time interval
nx <- 150 #number of discrete lattice points
nt <- 150 #number of timesteps
dx <- lx/nx #length of one discrete lattice cell
dt <- lt/nt #length of timestep

psi = c(1:nx) #array for the function h values
sol = c(1:nx)

kappa = dt/(4*dx*dx*dx*dx)

for(j in c(1:nx)) {
psi[j] = exp(-(j*dx-5)*(j*dx-5))
sol[j] = psi[j]
}

xaxis <- c(1:nx)*dx #the x values corresponding to the discrete lattice points
alpha <- c(1:nx)

A = matrix(nrow=nx,ncol=nx) #matrix for forward time evolution

for (m in c(1:nt)) { #main time stepping loop

for(j in c(1:nx)) {
alpha[j] = kappa*psi[j]*psi[j]*psi[j]
}

for(j in c(1:nx)) {
for(k in c(1:nx)) {
A[j,k]=0
if(j==k) {
if(j!=nx && j!=1) {
A[j,k] = 1 + 2*alpha[j+1] + 2*alpha[j-1] #diagonal elements
}
if(j==1) {
A[j,k] = 1 + 2*alpha[j+1]
}
if(j==nx) {
A[j,k] = 1 + 2*alpha[j-1]
}
}

if(j==k+1 && j!=1) {
A[j,k] = -alpha[j-1] #off-diagonal elements
}

if(j==k-1 && j!=nx) {
A[j,k] = -alpha[j+1]
}

if(j==k+2 && j<nx) {
A[j,k] = -2*alpha[j+1]
}

if(j==k-2 && j>1) {
A[j,k] = -2*alpha[j-1]
}

if(j==k+3 && j<nx) {
A[j,k] = alpha[j+1]
}

if(j==k-3 && j>1) {
A[j,k] = alpha[j-1]
}

}
}

for(l in c(1:nx)) {
psi[l] = sol[l]
}
sol <- solve(A,psi) #solve the system of equations

jpeg(file = paste("plot_",m,".jpg",sep=""))
plot(xaxis,sol,xlab = "position (x)",ylab="h(x,t)",ylim=c(0,1),pch='.')
title(paste("h(x,t) at t = ",round(m*dt,digits=2)))
lines(xaxis,sol)
dev.off()

}

dev.off()

An animation of the solution looks like this.

The solutions of this equation have the property that the graph settles into the shape of a downward opening parabola when time proceeds. A problem with this is that the contact angle, in which the liquid surface approaches the solid surface (x-axis) at the final equilibrium, can be anything between 0 and 90 degrees depending on the relative width and height of the initial Gaussian. In a real liquid-solid system, the final contact angle depends on the surface tensions of both the liquid-solid and the liquid-gas interfaces, as described by the Young equation.

To create an equation that can model equilibrium contact angles appropriately, we add a disjoining pressure term $\Pi (h)$ in the equation, as here: One form of the $\Pi$-term that works is where the $h_*$ is a precursor film thickness. The idea behind the precursor film is that even when we have a liquid drop or puddle surrounded by apparently dry solid surface, there is actually a very thin adsorbed layer of liquid molecules of that dry area (the molecules get there by evaporating from the liquid surface and reattaching on the solid). So, in a simulation where we include the disjoining pressure, we need to use an initial condition that is a Gaussian with an added constant equal to the precursor thickness: $h(x,0) = \exp \left[-b(x-x_0 )^2 \right] + h_*$

In here we will set the values n = 5 and m = 2 in the disjoining pressure term, and set the precursor film thickness into the value 0.01. The disjoining pressure term can be treated explicitly at the same time as we use implicit differencing for the rest of the equation – we subtract, on the RHS of the discretized equation a term $D_{j}^i$, defined by where and $\Pi_{j}^i$ is the disjoining pressure evaluated at the discrete points. Note the use of a central finite difference instead of a one-sided difference when calculating the derivatives – doing otherwise is likely to make the simulation crash. The term $D_{j}^{i}$ only affects the right-hand side vector of the linear system $\mathbf{Ax} = \mathbf{b}$ that we solve on each timestep. A code that solves the new equation for the pre-factor value $B=0.1$ is shown next.

library(graphics) #load the graphics library needed for plotting

lx <- 10 #length of the computational domain
lt <- 5 #length of the simulation time interval
nx <- 150 #number of discrete lattice points
nt <- 5000 #number of timesteps
dx <- lx/nx #length of one discrete lattice cell
dt <- lt/nt #length of timestep

prec = 0.01 #precursor film thickness

psi = c(1:nx) #array for the function h values
sol = c(1:nx)

disjoin1 = c(1:nx)
disjoin2 = c(1:nx)
disjoin3 = c(1:nx)

kappa = dt/(4*dx*dx*dx*dx)

for(j in c(1:nx)) {
psi[j] = exp(-(j*dx-5)*(j*dx-5))+prec
sol[j] = psi[j]
}

xaxis <- c(1:nx)*dx #the x values corresponding to the discrete lattice points
alpha <- c(1:nx)

A = matrix(nrow=nx,ncol=nx) #matrix for forward time evolution

for (m in c(1:nt)) { #main time stepping loop

disjoin1 = 0
disjoin1[nx] = 0
for(j in c(1:nx)) {
disjoin1[j] = 0.1*((prec/psi[j])^5 - (prec/psi[j])^2)
}

for(j in c(2:(nx-2))) {
disjoin2[j] = psi[j]*psi[j]*psi[j]*(disjoin1[j+1] - disjoin1[j-1])/(2*dx)
}

for(j in c(2:(nx-2))) {
disjoin3[j] = (disjoin2[j+1]-disjoin2[j-1])/(2*dx)
}

disjoin3 = 0
disjoin3 = 0
disjoin3[nx-1] = 0
disjoin3[nx] = 0

for(j in c(1:nx)) {
alpha[j] = kappa*psi[j]*psi[j]*psi[j]
}

for(j in c(1:nx)) {
for(k in c(1:nx)) {
A[j,k]=0
if(j==k) {
if(j!=nx && j!=1) {
A[j,k] = 1 + 2*alpha[j+1] + 2*alpha[j-1] #diagonal elements
}
if(j==1) {
A[j,k] = 1 + 2*alpha[j+1]
}
if(j==nx) {
A[j,k] = 1 + 2*alpha[j-1]
}
}

if(j==k+1 && j!=1) {
A[j,k] = -alpha[j-1] #off-diagonal elements
}

if(j==k-1 && j!=nx) {
A[j,k] = -alpha[j+1]
}

if(j==k+2 && j<nx) {
A[j,k] = -2*alpha[j+1]
}

if(j==k-2 && j>1) {
A[j,k] = -2*alpha[j-1]
}

if(j==k+3 && j<nx) {
A[j,k] = alpha[j+1]
}

if(j==k-3 && j>1) {
A[j,k] = alpha[j-1]
}

}
}

for(l in c(1:nx)) {
psi[l] = sol[l]
}
sol <- solve(A,psi-disjoin3) #solve the system of equations
for(l in c((nx-20):nx)) { # remove the risk of boundary effects on the right end of the domain
psi[l]=prec
sol[l]=prec
}

if(m%%10 == 0) {
jpeg(file = paste("plot_",m,".jpg",sep=""))
plot(xaxis,sol,xlab = "position (x)",ylab="h(x,t)",ylim=c(0,1),pch='.')
title(paste("h(x,t) at t = ",round(m*dt,digits=2)))
lines(xaxis,sol)
dev.off()
}

}

In the next animation, the solution curves for pre-factor values $B=0.1$ (red curve) and $B=0.01$ (black curve) are shown in the same graph.

In the animation, it is apparent that a larger value of $B$ leads to a larger contact angle at equilibrium. Actually, it can be shown that the contact angle $\theta$ depends on the values of the parameters as More information about the thin film equation and its solutions can be found on the Wiki page here, and on the NJIT department of applied mathematics homepage. When the effects of gravitation or surface tension gradients are added in the TFE, many kinds of interesting pattern formation effects can happen just like in our previous example of a nonlinear PDE, the Ginzburg-Landau equation. If the liquid film consists of a mixture of many, possibly volatile liquids, complicated multiphysics problems involving fluid dynamics, evaporation, heat transfer and chemical kinetics, all at the same time, are obtained.