Nonlinear material simulation in pdetool
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I tried to solve the similar problem explained here with a nonlinear material. And I wrote this code (with a simpler geometry): (And I defined the c coefficient with an interpolation function with respect to the magnitude of the gradient of the solution|grad(solution)|)
u0=4e-7*pi;
model = createpde(1);
gd = [ 3,4,+2.0,-2.0,-2.0,+2.0,1.5,1.5,-1.5,-1.5;...
3,4,+0.8,-0.8,-0.8,+0.8,0.8,0.8,-0.8,-0.8;...
3,4,+0.4,-0.4,-0.4,+0.4,0.4,0.4,-0.4,-0.4;...
3,4,+1.2,+0.8,+0.8,+1.2,0.4,0.4,-0.4,-0.4;...
3,4,-0.8,-1.2,-1.2,-0.8,0.4,0.4,-0.4,-0.4;]'; % geometry gd
ns = char('bound','c1','c2','c3','c4')';
sf = 'bound+c1+c2+c3+c4';
[ dl, bt] = decsg(gd,sf,ns);
model.SolverOptions.ReportStatistics = 'on';
geometryFromEdges(model,dl); % include geometry
generateMesh(model,'Hmax',0.04,'GeometricOrder','Linear');
applyBoundaryCondition(model,'dirichlet','Edge',[1,2,13,14],'u',0);
specifyCoefficients(model,'m',0,'d',0,'c',1,'a',0,'f',0,'Face',1);
cCoef = @(~,state) interp1([0 1-4 2e-4 2],[1/(1000) 1/(500) 1/(100) 1/(10)],sqrt((state.ux).^2 + (state.uy).^2));
specifyCoefficients(model,'m',0,'d',0,'c',cCoef,'a',0,'f',0,'Face',2);
specifyCoefficients(model,'m',0,'d',0,'c',1,'a',0,'f',u0,'Face',3);
specifyCoefficients(model,'m',0,'d',0,'c',1,'a',0,'f',-u0,'Face',4);
specifyCoefficients(model,'m',0,'d',0,'c',1,'a',0,'f',-u0,'Face',5);
results = solvepde(model);
B=sqrt((results.XGradients.^2+results.YGradients.^2)); %B=curl(A);
pdeplot(model.Mesh.Nodes,model.Mesh.Elements,'XYData',B,'Mesh','on');
figure; quiver(results.Mesh.Nodes(1,:),results.Mesh.Nodes(2,:),results.YGradients',-results.XGradients');
I supposed that the solver will handle the problem iteratively, however, I got this instead:
Iteration Residual Step size Jacobian: Full
0 1.0842e-19
And the solution is exactly the same as the time I replace the c coefficient with 1/1000. I expected to see some sort of saturation in the material.
3 Commenti
Alan Weiss
il 1 Nov 2018
I believe what occurs is that the residual is very small, so the solver thinks that it is done, that the initial solution is close enough to the correct one. But this comment is based only on viewing the reported iteration, not understanding the problem.
So my question for you is, did the solver find a good solution?
Alan Weiss
MATLAB mathematical toolbox documentation
Hamed Eskandari
il 4 Nov 2018
Viktor Szuhai
il 27 Set 2023
Nice job. But I believe I found a small typo: an "e" is missing in the cCoef definition. Correct:
cCoef = @(~,state) interp1([0 1e-4 2e-4 2],[1/(1000) 1/(500) 1/(100) 1/(10)],sqrt((state.ux).^2 + (state.uy).^2));
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