view +time/Cdiff.m @ 1037:2d7ba44340d0 feature/burgers1d

Pass scheme specific parameters as cell array. This will enabale constructDiffOps to be more general. In addition, allow for schemes returning function handles as diffOps, which is currently how non-linear schemes such as Burgers1d are implemented.
author Vidar Stiernström <vidar.stiernstrom@it.uu.se>
date Fri, 18 Jan 2019 09:02:02 +0100
parents 151ab2b5a686
children 8894e9c49e40 c9009d5a3101
line wrap: on
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classdef Cdiff < time.Timestepper
    properties
        D
        E
        S
        k
        t
        v
        v_prev
        n
    end


    methods
        % Solves u_tt = Du + Eu_t + S
        % D, E, S can either all be constants or all be function handles,
        % They can also be omitted by setting them equal to the empty matrix.
        % Cdiff(D, E, S, k, t0, n0, v, v_prev)
        function obj = Cdiff(D, E, S, k, t0, n0, v, v_prev)
            m = length(v);
            default_arg('E',sparse(m,m));
            default_arg('S',sparse(m,1));

            obj.D = D;
            obj.E = E;
            obj.S = S;


            obj.k = k;
            obj.t = t0;
            obj.n = n0;
            obj.v = v;
            obj.v_prev = v_prev;
        end

        function [v,t] = getV(obj)
            v = obj.v;
            t = obj.t;
        end

        function [vt,t] = getVt(obj)
            vt = (obj.v-obj.v_prev)/obj.k; % Could be improved using u_tt = f(u))
            t = obj.t;
        end

        function obj = step(obj)
            [obj.v, obj.v_prev] = time.cdiff.cdiff(obj.v, obj.v_prev, obj.k, obj.D, obj.E, obj.S);
            obj.t = obj.t + obj.k;
            obj.n = obj.n + 1;
        end
    end
end