# Reaction-diffusion equations

We adapt a C code, brusselator.c, solving coupled reaction-diffusion equations, for 2 conpounds U and V : \displaystyle \partial_t U = \nabla^2 U + k(ka - (kb + 1)U + U^2 V) \displaystyle \partial_t V = DD \nabla^2 V + k(kb U - U^2 V)

clear all; clf


# Parameters

As the calculations are long, and the convergence depending on parameters, we recommend to keep a spatial step of 0.5 and time step around 0.1

We keep the same fluid parameters as in brusselator.c We only play on the value of µ.

% parameters
Nx=40; % number of gridpoints in x
Ny=40; % number of gridpoints in y
Lx=20; % domain length x
Ly=20; % domain length y
dt=0.1; % time step
tmax=270; % final time

mu=[7.8,4,3.3]; % values of main parameter µ
mut=length(mu);

kt=100./((mu-0.3).^4); % time factor correction to adapt to µ (low µ requires more calculation time)

% secondary parameters, not modified in this study
k=1;ka=4.5;DD=8;
nu=sqrt(1/DD);
kbcrit=sqrt(1+ka*nu);


# System matrices

We use periodical differentiation matrices, available in this package, so we won’t have to set boundary conditions.

\displaystyle \left( \begin{array}{cc} I & 0 \\ 0 & I \\ \end{array}\right) \left(\begin{array}{c} U_t \\ V_t \\ \end{array}\right)= \left( \begin{array}{cc} -k (kb+1) I+(Dxx+Dyy) & 0 \\ k.kb.I & DD (Dxx+Dyy)\\ \end{array}\right) \left(\begin{array}{c} U \\ V \\ \end{array}\right)+ \left(\begin{array}{c} k.ka\\ 0 \\ \end{array}\right)+ \left(\begin{array}{c} k U^2 V\\ -k U^2 V \\ \end{array}\right)

If we note \displaystyle q=\left( \begin{array}{c} U \\ V \\ \end{array}\right)

we have a system \displaystyle E q_t=A q + b + c(q)

% differentiation matrices
[d.x,d.xx,d.wx,x]=dif1D('fp',0,Lx,Nx,5);
[d.y,d.yy,d.wy,y]=dif1D('fp',0,Ly,Ny,5);
[D,l,X,Y,Z,I,NN]=dif2D(d,x,y);

II=blkdiag(I,I);
E=II;
u=1:NN;u=u(:);
v=NN+1:2*NN; v=v(:);

for mui=1:mut % Loop on the different values of µ

kb=kbcrit*(1+mu(mui)); % parameter depending on the value of µ

% system matrices

A=[-k*(kb+1)*I+(D.xx+D.yy) , Z  ;...
k*kb*I , DD*(D.xx+D.yy)];
b=[k*ka*ones(Nx*Ny,1);zeros(Nx*Ny,1)];


# Initial conditions

The balance state is for U=ka and V=kb/ka. We set the initial conditions to this state, with a random noise on V concentration.

% initial condition

uinit=ka*ones(Nx*Ny,1);
vinit=kb/ka+0.001*(rand(Nx*Ny,1)-0.5); %initial noise
q=[uinit(:);vinit(:)];


# March in time

We have to integrate \displaystyle \int_t^{t+dt} E q_t dt=\int_t^{t+dt} (Aq+b+c(q)) dt We use an implicit method, which tends to be more stable than Crank-Nicolson method in this case. Thus the integral becomes : \displaystyle E(q(t+dt)-q(t))\approx [A q(t+dt)+b+c(q(t))] dt \displaystyle q(t+dt)\approx (E-Adt)^{-1}[Eq(t)+b dt+c(q(t)) dt]

Please note that the computation of the 3 cases is quite long, especially for the last one, and may take about 3 min with an i7 on a 40x40 grid.

% march in time matrix
Mm=E-A*dt; % implicit method shows better convergence than crank nicolson

BB=Mm\(b*dt);

% marching loop
tvec=dt:dt:tmax;
Nt=length(tvec);

for ind=1:(Nt*kt(mui))

kuuv=k*q(u).*q(u).*q(v);
C=[kuuv;-kuuv]; % non linear term

q=BB+gmres(Mm,q+(C*dt),1000,1e-7); % one step forward, gmres is used to fasten computing.

%plotting
subplot(1,3,mui)
surf(X,Y,reshape(q(u),Ny,Nx));
view(2);
axis([0,Lx-Lx/Nx,0,Ly-Ly/Ny]);
drawnow

end
title(['U concentration with mu=' num2str(mu(mui))]);
xlabel('x'); ylabel('y');

end


# Results and validation

As in brusselator.c, we get some critical values of µ, for which the final concentration in U shows patterns : However, these critical values of µ are very different from the ones obtained in the C program. Moreover, there seems to be a dependance on the spatial and time step. The C program uses more complex and robust calculation method, as well as fastest. This could explain these differences, and the difficulties i encountered to make the calculation stable. Any contribution on this point is welcomed.

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