sandbox/easystab/stab2014/Fernando_Lugard

    Our contribution to EasyStab

    Advection difusion equation f_t+Uf_x=\mu f_{xx}

    This is our code: advection-diffusion.m

    We used the code vibrating_string.m
    To do that, we replace the big matrice A and E with our new equation, so we have E=I and A=nudx+Udxx. U is the advection factor and nu is the diffusion factor

    To validate our code, we find a theoretical solution of diffusion equation for a simple systeme which is the diffusion of an heating source. we modified our initial condition to check the initial condition of the analytical equation, and we compared the numerical and analytical solution.

    visualisation of the advected diffused solution

    visualisation of the advected diffused solution

    error between the numerical and the theoritical solution

    error between the numerical and the theoritical solution

    Here we can see that the initial condition is advected and diffused. And also we see that the error between the numerical solution and the analytical solution is low and shade to 2.10e-3

    Vibrating String Dissipation

    This is our code: Vibrating-string_dissipation.m

    We used the code vibrating_string.m
    To do that, we add a new term Alpha, which represent the dissipation factor, to our equation. ? To validate our code, we find a theoretical solution of dissipation equation, and we compared the motion of a point on the numerical and analytical solution. ?

    evolution of the vibrating string with dissipation

    evolution of the vibrating string with dissipation

    evolution of the center point

    evolution of the center point

    Here we can see that the motion of a point on the numerical solution is like the motion of the same point on the analytical solution.

    Test other function for diffmat code

    This is our code: diffmat_other_function_test.m We used the code diffmat.m and we just change the initial function.

    first test of the computation Here is the first figure that is produced by the code

    Comparison of the numerical and exact derivative for the first and second derivative of a polynomial function

    Comparison of the numerical and exact derivative for the first and second derivative of a polynomial function

    We can notice that for this kind of function, the differentiation matrix is very effective.

    second test of the computation

    Comparison of the numerical and exact derivative for the first and second derivative of a exponentialfunction

    Comparison of the numerical and exact derivative for the first and second derivative of a exponentialfunction

    DD and D*D comparison on a trigonometric function

    In order to check the differentiation matrices performence for the second derivative we compare DD and D*D on a cosinus in this program compare.m

    Comparison of the differentiation matrices DD and D times D of a cosinus

    Comparison of the differentiation matrices DD and D times D of a cosinus

    Reaction diffusion

    We take the diffusion equation and we add a no linear term which characterizes the combustion. So we have adapted the code diffusion_eigenmodes.m in order to make combustion.m

    combustion, snapshot march in time

    combustion, snapshot march in time

    Notes

    De la part de Jérôme

    domaine	        valeur	note
    connectivité 	2	2
    recyclage	2	2
    graphiques	2	1
    théories	4	2
    Originalité	4	1
    note /14	14	8

    Ce que vous avez fait de plus original c’est l’étude du système de reaction-diffusion. Mais votre exposé ne nous apprend pas grand chose: quand-est-ce que le système est stable? Quand est-il instable? Comment cela dépend il des longueurs d’ondes? Vous avez pourtant tous les outils théorique (et même le corrigé…) pour calculer queld est-ce que ce sera stable ou instable? Et quelles sont les différences entre les deux états stationnaires?

    Je crois que vous avez un peu trop vite baissé les bras. Dommage.

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