sandbox/easystab/domain_derivative_2D.m

    Testing the domain geometry derivative

    This is like domain_derivative_1D.m but here in 2D. The only change is that instead of imposing h_{xx}+2=0 in the domain, we impose h_{xx}+h_{yy}+2=0 and we impose as a Dirichlet boundary condition on the left and right sides of the domain, the value of the theoretical solution of the problem h=y(1-y)

    disp('%%%%%%%')
    clear all; clf;
    
    % parameters
    Nx=20;           % number of grid points in x
    Ny=20;            % number of grid points in y
    p=-1;         % desired slope at top boundary
    L=0.9;      % the length in y of the computational domain
    
    % differentiation
    [d.x,d.xx,d.wx,x]=dif1D('cheb',0,1,Nx);
    [d.y,d.yy,d.wy,y]=dif1D('cheb',0,L,Ny);
    [D,l,X,Y,Z,I,NN]=dif2D(d,x,y);
    
    ZZ=blkdiag(Z,zeros(Nx,Nx));
    II=blkdiag(I,eye(Nx,Nx));
    
    l.h=(1:NN)';
    l.eta=NN+[1:Nx]';
    l.top=[l.ctl; l.top; l.ctr];
    l.bot=[l.cbl; l.bot; l.cbr];
    
    %initial guess
    eta0=zeros(Nx,1);
    sol0=[Y(:).*(1-Y(:));eta0];
    sol=sol0;
    
    % Newton iterations
    quit=0;count=0;
    while ~quit
    
        % the present solution and its derivatives
        h=sol(l.h); hy=D.y*h;  hxx=D.xx*h; hyy=D.yy*h; 
        eta=sol(l.eta);
        
        % nonlinear function
        f=[hxx+hyy+2; hy(l.top)+eta.*hyy(l.top)-p*ones(Nx,1)]; 
    
        % analytical jacobian
        A=[D.xx+D.yy, ZZ(l.h,l.eta); ...
           D.y(l.top,:)+diag(eta)*D.yy(l.top,:), diag(hyy(l.top))];
    
        % Boundary conditions
        loc=[l.left; l.right; l.bot; l.top];
        C=II([l.bot;l.right;l.left],:);
        
        f(loc)=[C*(sol-sol0); ... % the linear boundary conditions
                h(l.top)+diag(eta)*hy(l.top)]; % the nonlinear boundary conditions
        A(loc,:)=[C; ...
                  II(l.top,:)+diag(eta)*D.y(l.top,:)*II(l.h,:)+diag(hy(l.top))*II(l.eta,:)];

    Linear extrapolation

    I do here the linear extrapolation in one single line of code by using array operations. This is a little tricky if you are not used to it. If you prefer, you can replace this by a loop of x, and at each loop do the linear extrapolation like we did in 1D in domain_derivative_1D.m.

        % Show present solution
        ne=10;
        [XX,YY]=meshgrid(x,linspace(L,L+max(eta),ne)); % grid for linear extrapolation
        hh=(h(l.top)*ones(1,ne))'+(hy(l.top)*ones(1,ne))'.*(YY-L); % linear extrapolation
        mesh(X,Y,reshape(h,Ny,Nx),'edgecolor','b'); hold on
        mesh(XX,YY,hh,'edgecolor','r'); 
        plot3(x,L+eta,x*0,'k','linewidth',2); hold off % draw boundary
        legend('h','linear extrapolation','estimated boundary position','location','north')
        view([-101,16])
        drawnow;
        
        % convergence test
        res=norm(f,inf);
        disp([num2str(count) '  ' num2str(res)]);
        if count>50|res>1e5; disp('no convergence'); break; end
        if res<1e-9; quit=1; disp('converged'); continue; end
    
        % Newton step
        sol=sol-A\f;
        count=count+1;
    end
    
    set(gcf,'paperpositionmode','auto')
    print('-dpng','-r80','domain_derivative_2D.png')
    

    The figure

    Exercices/Contributions

    • Please
    • Please

    %}