PhasePortrait_NonLinear.m

This document belongs to the easystab project, check the main page of the project to understand the general philosophy of the project.

The present program was specifically designed as a support for the course “Introductions to hydrodynamical instabilities” for the “DET” Master’s cursus in Toulouse. For the underlying theory please see Lecture notes for chapters 1-2

To use this program: click “raw page source” in the left column, copy-paste in the Matlab/Octave editor, and run.

Phase portrait of a NON-LINEAR dynamical system of dimension 2.

This program draws a phase portrait for a dynamical system written under the form

\frac{d}{dt} X(t) = F(X(t)).

With X = \left[ \begin{array}{c} x_1 \\ x_2 \end{array} \right]

and F is a function defined at the end of the program.

The drawing of the phase portrait is done using the same representation as for the linear case treated by the program PhasePortrait_Linear.m.

Example

The following figure was generated for a dynamical system corresponding to a simple pendulum. Use the program interactively to consider other cases, change parameters and plot more trajectories !

How to use this program :

Select your case by setting the variable ‘typeproblem’ at line 38.
Change the parameters in corresponding definition section, to be foundin subfunction F_PortraitdePhase defined at the end of this file (in the corresponding block of the “switch/case” selection)
Run the program with Octave or Matlab
Click on the figure to plot trajectories.

The program may be customized by adding cases with new values of ‘typeproblem’, just add the definition in a new switch/case statement at the end !

Main program:

function [] = PhasePortrait_NonLinear()	

% parametres :
global typeproblem xmin xmax ymin ymax Tmax Tmaxbak;
 
typeproblem = 'Pendulum';  

% Select the problem to consider among the following cases :
% 'Pendulum', 'RotatingPendulum', 'InvertedPendulum','SaddleNode', 'Transcritical', 
% 'Pitchfork', 'Brusselator', 'VanDerPol', 'LotkaVolterra', 'BuffaloWolf', 'Trefethen', 
% 'Exam2021'; 
% 'Custom' [ , ... ]
%typeproblem = 'LotkaVolterra';

%plotEnergy=0; % use 1 to plot the energy in figure 2  
 
% Dimensions of the figure. We give default values here. These can also be adjusted in the definition of the function. 
    close all;
    xmin = -2; xmax = 2;
    ymin = -2; ymax = 2;
    Z = F_PortraitdePhase([0.;0.]); % (trick) call to the function to get the values defined there if not using default..
    eps = 1e-6;
% Pour le trace de trajectoires en cliquant sur la figure : nombre de trajectoires, Tmax
    Ntraj = 10;
    Tmax = 50; % max time for integratation of trajectory
    Tmaxbak = 5; % max time for integratation of trajectory (backwrards)
    Tmaxplot = 5; % max time for plot in figure 2.
    ODEoptions = odeset('Refine',4,'RelTol',1e-7);
% Fin des parametres

Illustration of the flow with vectors

This is done again with the quiver command.

    xP = linspace(xmin,xmax,21);
    yP = linspace(ymin,ymax,21);
	[xG, yG] = meshgrid(xP,yP);
    	for i=1:length(xP)
        for j= 1:length(yP)
            F = F_PortraitdePhase([xP(i),yP(j)]);
            ux(j,i) = F(1);
            uy(j,i) = F(2);
        end
    end
    
   figure(1); 

   hold on
   quiver(xG, yG, ux, uy, 'Color', 'b');
    title({['Phase portrait for ',typeproblem ],'Left-click to draw a trajectory ; right-click to stop'},'FontSize',14);
    xlabel('x_1');ylabel('x_2');
    axis([xmin xmax ymin ymax]);
    hold on;
    plot([xmin xmax],[0 0],':k',[0,0],[ymin,ymax],':k');

%    if(plotEnergy==1)
%    figure(1);subplot(4,2,7:8);hold on;
    %title('Amplitude vs. time');
%    xlabel('t');ylabel('(x_1^2+x_2^2)^{1/2}');
%    end   
    
pause(1);

Drawing of a few trajectories selected by clicking on the figure :

 INTERACTIVE = isempty(getenv('OCTAVE_AUTORUN'));
   % to detect if the code is running on the server or interactively
   if INTERACTIVE
     % this part is for using the program in interactive mode, 
     % several trajectories can be drawn by clicking on the figure
   figure(1); 
   [xp,yp,button] = ginput(1)
   while(button==1);
    disp('click on figure to launch a trajectory...')
    xinit = [xp ; yp];
    dFdx1 = (F_PortraitdePhase([xp+eps,yp])-F_PortraitdePhase([xp-eps,yp]))/(2*eps);
    dFdx2 = (F_PortraitdePhase([xp,yp+eps])-F_PortraitdePhase([xp,yp-eps]))/(2*eps);
    disp([' Drawing trajectory starting from [x1,x2] = [ ',num2str(xp), ' , ' num2str(yp), ' ] ']);
    disp([' Gradient matrix at starting point :   [ [ ' ,num2str(dFdx1(1)), ' , ',num2str(dFdx2(1)), ' ] ;']);
    disp(['                                     [ ' ,num2str(dFdx1(2)), ' , ',num2str(dFdx2(2)), ' ] ]']);
 
    
   [t,xtraj] = ode45(@(t,X)F_PortraitdePhase(X),[0, Tmax],xinit,ODEoptions);
   [tbak,xtrajback] = ode45(@(t,X)F_PortraitdePhase(X),[0,-Tmaxbak],xinit,ODEoptions);
    plot(xtraj(:,1),xtraj(:,2),'r',xtraj(1,1),xtraj(1,2),'ro');
    hold on;
    plot(xtrajback(:,1),xtrajback(:,2),'m:');
    axis([xmin xmax ymin ymax]);
    xp=xtraj(end,1); yp=xtraj(end,2);
    dFdx1 = (F_PortraitdePhase([xp+eps,yp])-F_PortraitdePhase([xp-eps,yp]))/(2*eps);
    dFdx2 = (F_PortraitdePhase([xp,yp+eps])-F_PortraitdePhase([xp,yp-eps]))/(2*eps);
    disp([' This trajectory ends at point [x1,x2] = [ ',num2str(xp), ' , ' num2str(yp), ' ] ']);
    disp([' Gradient matrix at this point :         [ [ ' ,num2str(dFdx1(1)), ' , ',num2str(dFdx2(1)), ' ] ;']);
    disp(['                                         [ ' ,num2str(dFdx1(2)), ' , ',num2str(dFdx2(2)), ' ] ]']);
    disp(' ');
 %    if(plotEnergy==1)
       %figure(2);
 %      subplot(4,2,7:8);
 %      plot(t,sqrt(xtraj(:,1).^2+xtraj(:,2).^2));
       %figure(1);
%     end
    [xp,yp,button] = ginput(1);
   end   
    figure(1);
    xlabel('x_1');  ylabel('x_2');title('Phase portrait of a nonlinear 2x2 system');
   
  else
    % this part is for using the program in non-interactive mode, 
    % it is execcuted when running automatically on the server.
    % a single trajectory will be drawn and a figure will be generated for the website
    xinit = [0;2];
    [t,xtraj] = ode45(@(t,X)F_PortraitdePhase(X),[0, Tmax],xinit,ODEoptions);
    [tbak,xtrajback] = ode45(@(t,X)F_PortraitdePhase(X),[0,-Tmaxbak],xinit,ODEoptions);
    plot(xtraj(:,1),xtraj(:,2),'r',xtrajback(:,1),xtrajback(:,2),'m',xtraj(1,1),xtraj(1,2),'ro'); 
    xinit = [0;.2];
    [t,xtraj] = ode45(@(t,X)F_PortraitdePhase(X),[0, Tmax],xinit,ODEoptions);
    [tbak,xtrajback] = ode45(@(t,X)F_PortraitdePhase(X),[0,-Tmaxbak],xinit,ODEoptions);
    plot(xtraj(:,1),xtraj(:,2),'r',xtrajback(:,1),xtrajback(:,2),'m',xtraj(1,1),xtraj(1,2),'ro'); 
  end
  set(gcf,'paperpositionmode','auto');
%  print('-dsvg','-r50','PhasePortraitNonLinear.svg');
saveas(gcf,'PhasePortraitNonLinear','svg');
  
end

Definition of the function F :

function F = F_PortraitdePhase(X)

% Fonction pour tracer le portrait de phase d'un systeme 2-2 de la forme 
% d X /d T = F(X). 
%
global typeproblem xmin xmax ymin ymax;
x1 = X(1); x2 = X(2);



switch (typeproblem)

Case “Pendulum” :

Equation for the motion of a damped pendulum.

m L^2 \ddot \theta = - \mu_f \dot \theta - m g \sin \theta

Considering this problem as a dynamical system of order 2 for X = [x_1 ; x_2] = [\theta, \dot \theta] leads to :

\frac{ d}{dt} [x_1 ; x_2] = [ x_2 ; - \mu x_2 - \omega_0^2 \sin(x_1) ]

with \omega_0^2 = \sqrt{\frac{g}{L}} ; \mu = \frac{\mu_f}{M L^2}.

Exercice : Observe the structure of the phase portrait for various values of the damping parameter mu. What do we observe for mu=0 ?

    case('Pendulum')
        
        mu = .1;
        omega0 = 1; % omega_0 = sqrt(g/L)
        F = [x2;-omega0^2*sin(x1)-mu*x2];
        % adjust the figure axes 
        xmin = -3*pi ; xmax = 3*pi; ymin = -2*pi; ymax = 2*pi;

Case “RotatingPendulum” :

Equation for the motion of a pendulum with imposed precession

m L^2 \ddot \theta = - \mu_f \dot \theta - m g L \sin \theta + m L^2 \Omega^2 \sin \theta \cos \theta

Considering this problem as a dynamical system of order 2 for X = [x_1 ; x_2] = [\theta, \dot \theta] leads to :

\frac{ d}{dt} [x_1 ; x_2] = [ x_2 ; - \mu x_2 - \omega_0^2 \sin x_1 (1 - R \cos x_1 ) ]

with \omega_0^2 = \sqrt{\frac{g}{L}} ; \mu = \frac{\mu_f}{M L^2} ; R = \frac{\Omega^2}{\omega^2}.

Exercice : Observe the structure of the phase portrait for various values of the rotation parameter R. Draw the corresponding bifurcation diagram.

    case('RotatingPendulum')
        
        omega0 = 1; mu = .5;
        R = 1.1;
        F = [x2;-omega0^2*sin(x1)*(1-R*cos(x1))-mu*x2];
        % adjust the figure axes 
        xmin = -pi/2 ; xmax = pi/2; ymin = -.5; ymax =.5;

Case “InvertedPendulum” :

Equation for the motion of an inverted pendulum with a spring (offset angle alpha)

m L^2 \ddot \theta = - \mu_f \dot \theta + m g L \sin \theta - K (\theta-\alpha)

Considering this problem as a dynamical system of order 2 for X = [x_1 ; x_2] = [\theta, \dot \theta] leads to :

\frac{ d}{dt} [x_1 ; x_2] = [ x_2 ; - \mu x_2 + \omega_0^2 \sin x_1 - k ( x_1- \alpha) ]

with \omega_0^2 = \sqrt{\frac{g}{L}} ; \mu = \frac{\mu_f}{m L^2} ; k = \frac{K}{m L^2}.

Exercice : Observe the structure of the phase portrait for various values of the offset angle alpha. Draw the corresponding bifurcation diagram.

    case('InvertedPendulum')
        
        omega0 = 1; mu = .5;k = .9;
        alpha = 0.04;
        
        F = [x2;+omega0^2*sin(x1)-k*(x1-alpha)-mu*x2];
        % adjust the figure axes 
        xmin = -pi/2 ; xmax = pi/2; ymin = -.5; ymax =.5;

Case ‘VanDerPol’ :

The VanDerPol oscillator is a well-known model of self-sustained oscillator. It is defined as follows :

m \ddot x +\omega_0^2 x = (r - \delta x^2) \dot x

Exercice : Observe the structure of the phase portrait for various choices of the parameters. Reconstruct the corresponding bifurcation diagram in terms of the bifurcation parameter r.

    case('VanDerPol');    
        omega0 = 1;
        r = 0.1;
        delta = 1;
        F = [x2 ; -omega0^2*x1+(r-delta*x1^2)*x2];
        % adjust the figure axes 
        xmin = -3 ; xmax = 3; ymin = -3; ymax = 3;

Case ‘Brusselator’ :

The ‘Brusselator’ (more precisely the homogeneous brusselator) is a simple model for a chemical reaction displaying unsteadiness. It is defined as follows :

\dot x_1 = -(\beta + 1) x_1 + x_1^2 x_2 + \alpha ; \dot x_2 = \beta x_1 - x_1^2 x_2 ;

Exercice : Observe the structure of the phase portrait for various choices of the parameters. Construct the bifurcation diagram as function of the bifurcation parameter beta.

   case('Brusselator')
       
       alpha = 1; 
       beta = 3; 
       F = [-(beta+1)*x1+x1^2*x2+alpha ; beta*x1-x1^2*x2] ;
        
        
        % adjust the figure axes 
        xmin = 0 ; xmax = 6; ymin = 0; ymax = 6;

Case ‘LotkaVolterra’ :

The Lotka-Volterra is a simple system representing the competition of two animal species (for instance hares and lynxes). It is defined as follows :

\dot x_1 = x_1( \alpha - \beta x_2) ; \dot x_2 = x_2( -\gamma + \delta x_1) ;

Exercice : Observe the structure of the phase portrait for various choices of the parameters. Is this problem conservative or not ?

   case('LotkaVolterra')
       % we take the same values as wikipedia...
       alpha = 2/3; 
       beta = 4/3; 
       gamma = 1; 
       delta = 1;
       F = [(alpha-beta*x2)*x1 ; (delta*x1-gamma)*x2];
        % adjust the figure axes 
        xmin = 0 ; xmax = 2.5; ymin = 0; ymax = 2.5;

Case ‘BuffaloWolf’ :

This problem is a

\dot x_1 = r x_1 - A x_1 (x_2+ E x_2^2) - B x_1^2; \dot x_2 = -C x_2 + D x_1 (x_2+ E x_2^2) ) ;

Exercice : Observe the structure of the phase portrait for various choices of the parameters. Reconstruct the corresponding bifurcation diagram as the parameter r is varied

    case('BuffaloWolf')
    r = 2.3;
    A = .3;
    B = 0.1;
    C = 1;
    D = .2;
    E = 0.15;
    F = [(r*x1 -A*x1*(x2+E*x2^2) - B*x1^2) ; (-C*x2 + D*x1*(x2+E*x2^2)) ];

    % adjust the figure axes 
 xmin = -1 ; xmax = 20; ymin = -1; ymax = 10;

Case ‘Trefethen’ :

This is a simple model which is asymptotically stable but may lead to sucritical transition with very small initial conditions thanks to transient growth.

\dot x_1 = -(1/R) x_1 + x_2 -\sqrt{x_1^2+x_2^2} x_2; \dot x_2 = -(2/R) x_2 + \sqrt{x_1^2+x_2^2} x_1;

Exercice : observe the behaviour of the system for very small values of the initial condition (try with various values of R?)

    case('Trefethen')
    R = 10;
    NL = 1;% select 0 or 1
    normX = sqrt(x1^2+x2^2)  ; 
    F1 = -(1/R)*x1 + x2 - NL*normX*x2; 
    F2 = -(2/R)*x2 + NL*normX*x1;
    F = [F1;F2];
 % adjust the figure axes 
 xmin = -.05 ; xmax = .05; ymin = -.05; ymax = .05;

Case ‘SH_5.1’ :

This case is a variant of Trefethen’s original model, and is taken from Schmid & Henningson (Exercice 5.1, p. 525)

    case('SH_5.1')
    R = 10;
    NL = 0;% select 0 or 1
    normX = sqrt(x1^2+x2^2)  ; 
    F1 = -(1/R)*x1 + NL*normX*x2; 
    F2 = -(2/R)*x2 + x1 - NL*normX*x1;
    F = [F1;F2];
 % adjust the figure axes 
 xmin = -1 ; xmax = 1; ymin = -1; ymax = 1;

Case ‘SaddleNode’ :

We investigate a saddle-node bifurcation in dimension 2 by considering the following model:

m \ddot x + \mu \dot x = r - x^2

If the friction is dominant over the inertia this equation reduces to the classical one-dimensional saddle-node bifurcation.

Exercice : Observe the structure of the phase portrait for various choices of the parameters. Reconstruct the corresponding bifurcation diagram.

   case('SaddleNode')
         mass = .5; friction = 1; gravity = 1; 
         r=  -.1; 
         F = [x2 ; -friction*x2/mass + (r-x1^2)*gravity];
        % adjust the figure axes 
        xmin = -3 ; xmax = 3; ymin = -1; ymax = 1;

Case ‘Pitchfork’ :

We investigate a transcritical bifurcation in dimension 2 by considering the following model:

m \ddot x + \mu \dot x = r x - \delta x^3

If the friction is dominant over the inertia this equation reduces to the classical one-dimensional pitchfork bifurcation.

Exercice : Observe the structure of the phase portrait for various choices of the parameters. Reconstruct the corresponding bifurcation diagram.

   case('Pitchfork')
         mass = .5; friction = 2; 
         r= .5; delta = 1;
         F = [x2 ; -friction*x2/mass+(r*x1-delta*x1^3)/mass];
         % adjust the figure axes 
         xmin = -1 ; xmax = 1; ymin = -1; ymax = 1;

Case ‘TransCritical’ :

We investigate a transcritical bifurcation in dimension 2 by considering the following model:

m \ddot x + \mu \dot x = r x - x^2

If the friction is dominant over the inertia this equation reduces to the classical one-dimensional transcritical bifurcation.

Exercice : Observe the structure of the phase portrait for various choices of the parameters. Reconstruct the corresponding bifurcation diagram.

   case('TransCritical')
         mass = .5; friction = 1; 
         r= .5; 
         F = [x2 ; -friction*x2/mass+(r*x1-x1^2)/mass];
         % adjust the figure axes 
         xmin = -1 ; xmax = 1; ymin = -.5; ymax = .5;

Case ‘Exo3.1’ :

   case('Exo3.1')
         F = [x1*(1-x1) ; x2*(2-4*x1)];
         % adjust the figure axes 
         xmin = -.5 ; xmax = 1.5; ymin = -.5; ymax = 1.5;

Case ‘Exam2021’ :

   case('Exam2021')
         r = -.9; A = 100; B = 1;
         F = [x1*(r+1-(x1-1)^2)+A*x2 ; -B*x2];
         % adjust the figure axes 
         xmin = -.5 ; xmax = 1.6; ymin = -.01; ymax = .02;         


   case('Custom')
        % add your own case here !
  
   otherwise
       error(['Error in PhasePortrait_NonLinear : unknown type ', typeproblem]);
 
end%swith

end%function