view +time/Rungekutta4SecondOrder.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 c6fcee3fcf1b
children
line wrap: on
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classdef Rungekutta4SecondOrder < time.Timestepper
    properties
        F
        k
        t
        w
        m
        D
        E
        S
        M
        C
        n
    end


    methods
        % Solves u_tt = Du + Eu_t + S by
        % Rewriting on first order form:
        %   w_t = M*w + C(t)
        % where
        %   M = [
        %      0, I;
        %      D, E;
        %   ]
        % and
        %   C(t) = [
        %      0;
        %      S(t)
        %   ]
        % 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.
        function obj = Rungekutta4SecondOrder(D, E, S, k, t0, v0, v0t)
            obj.D = D;
            obj.E = E;
            obj.S = S;
            obj.m = length(v0);
            obj.n = 0;


            if isa(D, 'function_handle') || isa(E, 'function_handle') || isa(S, 'function_handle')
                default_arg('D', @(t)sparse(obj.m, obj.m));
                default_arg('E', @(t)sparse(obj.m, obj.m));
                default_arg('S', @(t)sparse(obj.m, 1)    );

                if ~isa(D, 'function_handle')
                    D = @(t)D;
                end
                if ~isa(E, 'function_handle')
                    E = @(t)E;
                end
                if ~isa(S, 'function_handle')
                    S = @(t)S;
                end

                obj.k = k;
                obj.t = t0;
                obj.w = [v0; v0t];

                % Avoid matrix formulation because it is VERY slow
                obj.F = @(w,t)[
                    w(obj.m+1:end);
                    D(t)*w(1:obj.m) + E(t)*w(obj.m+1:end) + S(t);
                ];
            else

                default_arg('D', sparse(obj.m, obj.m));
                default_arg('E', sparse(obj.m, obj.m));
                default_arg('S', sparse(obj.m, 1)    );

                I = speye(obj.m);
                O = sparse(obj.m,obj.m);

                obj.M = [
                    O, I;
                    D, E;
                ];
                obj.C = [
                    zeros(obj.m,1);
                                 S;
                ];

                obj.k = k;
                obj.t = t0;
                obj.w = [v0; v0t];

                obj.F = @(w,t)(obj.M*w + obj.C);
            end
        end

        function [v,t] = getV(obj)
            v = obj.w(1:end/2);
            t = obj.t;
        end

        function [vt,t] = getVt(obj)
            vt = obj.w(end/2+1:end);
            t = obj.t;
        end

        function obj = step(obj)
            obj.w = time.rk.rungekutta_4(obj.w, obj.t, obj.k, obj.F);
            obj.t = obj.t + obj.k;
            obj.n = obj.n + 1;
        end
    end


    methods (Static)
        function k = getTimeStep(lambda)
            k = rk4.get_rk4_time_step(lambda);
        end
    end

end