> with(linalg):with(DEtools):with(plots):#treiberg/M6412eg1.pdf · Brusselator Equation Phase...
Transcript of > with(linalg):with(DEtools):with(plots):#treiberg/M6412eg1.pdf · Brusselator Equation Phase...
![Page 1: > with(linalg):with(DEtools):with(plots):#treiberg/M6412eg1.pdf · Brusselator Equation Phase Portrait Predator Prey models The simplest model is the Lotka-Volterra for the (nondimensionalized)](https://reader033.fdocuments.us/reader033/viewer/2022060210/5f048f867e708231d40e9392/html5/thumbnails/1.jpg)
MATH 6410 - 1 MAPLE PLOTS OF SYSTEM TRAJECTORIES
> with(linalg):with(DEtools):with(plots):# Warning, the protected names norm and trace have been redefined and unprotected
Warning, the previous binding of the name adjoint has been removed and it now has an assigned value
Warning, the name changecoords has been redefined
> restart:
> with(plots):with(linalg):with(DEtools):Warning, the name changecoords has been redefined
Warning, the protected names norm and trace have been redefined and unprotected
Warning, the previous binding of the name adjoint has been removed and it now has an assigned value
> F:= (xx,yy)-> xx-yy-xx^2+xx*yy;G:=(xx,yy)->-yy-xx^2;
F := xx, yy( ) ® xx - yy - xx 2 + yy xx
G := xx, yy( ) ® -yy - xx 2
>
This time, enter a nonlinear system of equations and a list of initial data.
> deqtn:={diff(x(t),t)=F(x(t),y(t)),
> diff(y(t),t)=G(x(t),y(t))};
deqtn := d dt
x t( ) = x t( ) - y t( ) - x t( )2 + y t( ) x t( ), d dt
y t( ) = -y t( ) - x t( )2ìíî
üýþ
> ICs:=[[x(0)=-1,y(0)=3],[x(0)=0,y(0)=3],[x(0)=.9,y(0)=3],[x(0)=.6,y(0)=3],[x(0)=.8,y(0)=3], [x(0)=1,y(0)=3],[x(0)=2,y(0)=3],[x(0)=3,y(0)=3],[x(0)=3,y(0)=0],[x(0)=3,y(0)=-1.5], [x(0)=0.05,y(0)=0], [x(0)=-.05,y(0)=0],[x(0)=0,y(0)=-.05], [x(0)=0,y(0)=.05], [x(0)=1.5,y(0)=-3], [x(0)=1,y(0)=-3], [x(0)=0,y(0)=-3],[x(0)=-1,y(0)=-3],[x(0)=-2,y(0)=-3],[x(0)=-1.1,y(0)=-1], [x(0)=-1,y(0)=-.9], [x(0)=-.9,y(0)=-1],[x(0)=-1,y(0)=-1.1],[x(0)=-1,y(0)=-.95], [x(0)=-1,y(0)=-1.05]]:
>
> DEplot(deqtn,[x(t),y(t)],t=0..15,ICs,x=-3..3,y=-3..3,arrows=small,
> stepsize=.1,color=navy,linecolor=red,title=`Nonlinear System`);
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y
x
3
3
2
1
20
-1
1
-2
-3
0-1-2-3
Nonlinear System
>
To find the critical points, enter the right side. then find the vanishing points of the vector field.
> solve({F(u,v)=0,G(u,v)=0},{u,v});
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u = 0, v = 0{ }, v = -1, u = 1{ }, v = -1, u = -1{ }
> x0:=-1;y0:=-1;x0 := -1
y0 := -1
Plot of solutions of the nonlinear equation near the critical point (-1,-1)
> IC2:=[seq([x(0)=-1.+0.01*k,y(0)=-1.+0.01*k],k=0..10)]:
> DEplot(deqtn,[x(t),y(t)],t=0..15,IC2,x=-1.1..-0.9,y=-1.1..-0.9,arrows=small,
> stepsize=.1,color=navy,linecolor=red,scaling=constrained,title=`Nonlinear System near (-1,-1)`);
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y
x
-0.9-0.9
-0.95
-1
-0.95
-1.05
-1.1
-1-1.05-1.1
Nonlinear System near (-1,-1)
Look near the equilibrium point (-1,-1). First, near that point, by the 2-dimensional Taylor's Theorem, the system is approximated by the linear system whose matrix is [F(-1+u,-1+v),G(-1+u,-1+v)]=[0 +diff(F(x,y),x)*u +diff(F(x,y),y)*v, 0+ diff(G(x,y),x)*u+diff(G(x,y),y)*v].
> A:=subs({x=x0,y=y0},matrix([ [diff(F(x,y),x),diff(F(x,y),y)],[diff(G(x,y),x),diff(G(x,y),y)]]));
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A := 2 -2
2 -1
éêêë
ùúúû
> eigenvects(A);
12
+ 12
I 7 , 1, 34
+ 14
I 7 , 1éêë
ùúû
ìíî
üýþ
éêêë
ùúúû
, 12
- 12
I 7 , 1, 34
- 14
I 7 , 1éêë
ùúû
ìíî
üýþ
éêêë
ùúúû
Define a linear system using the matrix.
> ODE1:={diff(x(t),t)=(A[1,1]*x(t)+A[1,2]*y(t)),diff(y(t),t)=A[2,1]*x(t)
> +A[2,2]*y(t)};
ODE1 := d dt
y t( ) = 2 x t( ) - y t( ), d dt
x t( ) = 2 x t( ) - 2 y t( )ìíî
üýþ
> ICL2:=[seq([x(0)=0.1*k,y(0)=0.1*k],k=0..10)]:
> phaseportrait(ODE1,[x(t),y(t)],0.0..11.0,ICL2, x=-1.1..1.1,
> y=-1.1..1.1, scaling=constrained,stepsize=.05,title=`Linearization at
> (-1,-1)`,linecolor=black);
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-1
0-0.5-1
y
x
1
1
0.5
00.5
-0.5
Linearization at(-1,-1)
The real parts are positive, so the local picture is that of a spiral source.
Plot of solutions of the nonlinear equation near the critical point (-1,-1)
> x0:=1;y0:=-1;
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x0 := 1
y0 := -1
> IC2:=[seq([x(0)=1.0+0.1*cos(k*Pi/6),y(0)=-1.0+0.1*sin(k*Pi/6)],k=0..11)]:
> DEplot(deqtn,[x(t),y(t)],t=0..3,IC2,x=0.9..1.1,y=-1.1..-0.9,arrows=small,
> stepsize=.03,color=navy,linecolor=red,scaling=constrained,title=`Nonli
> near System near (1,-1)`);
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x
y
1.1-0.9
-0.95
1.05
-1
-1.05
1
-1.1
0.950.9
Nonlinear System near (1,-1)
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Find the linearization near (1,-1) as before and plot the solutions of the linearized equations.
> A:=subs({x=x0,y=y0},matrix([ [diff(F(x,y),x),diff(F(x,y),y)],
> [diff(G(x,y),x),diff(G(x,y),y)]]));
A := -2 0
-2 -1
éêêë
ùúúû
> eigenvects(A);-1, 1, 0, 1[ ]{ }[ ], -2, 1, 1, 2[ ]{ }[ ]
> ODE2:={diff(x(t),t)=(A[1,1]*x(t)+A[1,2]*y(t)),diff(y(t),t)=A[2,1]*x(t)+A[2,2]*y(t)};
ODE2 := d dt
y t( ) = -2 x t( ) - y t( ), d dt
x t( ) = -2 x t( )ìíî
üýþ
> ICl2:=[seq([x(0)=cos(k*Pi/6),y(0)=sin(k*Pi/6)],k=0..11)]:
> phaseportrait(ODE2,[x(t),y(t)],0.0..4.0,ICl2, x=-1.1..1.1,
> y=-1.1..1.1, scaling=constrained,stepsize=.05,title=`Linearization at
> (1,-1)`,linecolor=black);
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-1
0-0.5-1
y
x
1
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0.5
00.5
-0.5
Linearization at(1,-1)
3. STABLE LIMIT CYCLES
Rayleigh's Clarinet Equation
Finally, we illustrate by way of examples, that a planar system may have a stable limit cycle, a closed periodic trajectory that attracts nearby trajectories. Consider the equation proposed by Lord Rayleigh for the vibration of a clarinet reed. mx'' = -kx + ax' -b(x')^3 which is stiffer than linear drag but soggier for small displacements. As in the text, we convert to a first order system with a=b=m=k=1.
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> ODE3:={diff(x(t),t)=y(t),diff(y(t),t)=-x(t)+y(t)-y(t)^3};
ODE3 := d dt
x t( ) = y t( ), d dt
y t( ) = -x t( ) + y t( ) - y t( )3ìíî
üýþ
> IC3:=[[x(0)=-2,y(0)=3],[x(0)=0,y(0)=3],[x(0)=2,y(0)=3],
> [x(0)=-2,y(0)=-3],[x(0)=0,y(0)=-3],[x(0)=2,y(0)=-3],
> [x(0)=-.2,y(0)=.3],[x(0)=0,y(0)=.3],[x(0)=.2,y(0)=.3],
> [x(0)=-.2,y(0)=-.3],[x(0)=0,y(0)=-.3],[x(0)=.2,y(0)=-.3]]:
> DEplot(ODE3,[x(t),y(t)],t=0..10,IC3,x=-3..3,y=-3..3,arrows=small,
> stepsize=.1,color=black,linecolor=t,title=`Clarinet Equation Phase
> Portrait`);
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-1
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0-1-2-3
Clarinet Equation PhasePortrait
The Brusselator
A model for a hypothetical chemical interaction of two species, which was proposed by researchers from Brussels, is given by the following equation
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> a:= 1.0; b:= 3.0;a := 1.0
b := 3.0
> ODE4:={diff(x(t),t)=a-(b+1)*x(t)+x(t)^2*y(t),diff(y(t),t)=b*x(t)-x(t)^
> 2*y(t)};
ODE4 := d dt
y t( ) = 3.0 x t( ) - x t( )2 y t( ), d dt
x t( ) = 1.0 - 4.0 x t( ) + x t( )2 y t( )ìíî
üýþ
> IC4:=[[x(0)=0.2,y(0)=0.2],[x(0)=2.2,y(0)=0.2],[x(0)=4.2,y(0)=0.2],[x(0
> )=0.2,y(0)=3.2],[x(0)=0.2,y(0)=5.2],[x(0)=2.2,y(0)=6.2],[x(0)=2,y(0)=3
> ],[x(0)=1,y(0)=4],[x(0)=1,y(0)=2.6]]:
> DEplot(ODE4,[x(t),y(t)],t=0..7,IC4,x=0..4,y=0..6,arrows=small,
> stepsize=.0475,color=black,linecolor=[red,coral,yellow,green,cyan,blue
> ,navy,maroon,magenta],title=`Brusselator Equation Phase Portrait`);
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x43210
y
6
5
4
3
2
1
0
Brusselator Equation Phase Portrait
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Predator Prey models
The simplest model is the Lotka-Volterra for the (nondimensionalized) prey x(t) and the predator y(t)
> a:= 0.7;a := 0.7
> ODE5:={diff(x(t),t)=x(t)*(1-y(t)),diff(y(t),t)=a*y(t)*(x(t)-1)};
ODE5 := d dt
y t( ) = 0.7 y t( ) x t( ) - 1( ), d dt
x t( ) = x t( ) 1 - y t( )( )ìíî
üýþ
> IC4:=[[x(0)=0.2,y(0)=0.2],[x(0)=2.2,y(0)=0.2],[x(0)=4.2,y(0)=0.2],[x(0
> )=0.2,y(0)=3.2],[x(0)=0.2,y(0)=5.2],[x(0)=2.2,y(0)=6.2],[x(0)=2,y(0)=3
> ],[x(0)=1,y(0)=4],[x(0)=1,y(0)=2.6]]:
> DEplot(ODE5,[x(t),y(t)],t=0..15,IC4,x=0..6,y=0..6,arrows=small,
> stepsize=.0475,color=black,linecolor=[red,coral,yellow,green,cyan,blue
> ,navy,maroon,magenta],title=`Brusselator Equation Phase Portrait`);
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y
x
6
6
5
4
5
3
2
4
1
03210
Brusselator Equation Phase Portrait
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More realistically, the prey growth is not unbounded but logistic, predator carrying capacity is proportional to prey density
> a:= 0.75; b:= 0.2;c:=0.05;a := 0.75
b := 0.2
c := 0.05
> b-(a-sqrt((1.0-a-c)^2+4.0*c))*(1.0+a+c-sqrt((1.0-a-c)^2+4.0*c));-0.1407602310
> ODE6:={diff(x(t),t)=x(t)*(1-x(t)-a*y(t)/(c+x(t))),diff(y(t),t)=b*y(t)*(1-y(t)/x(t))};
ODE6 := d dt
x t( ) = x t( ) 1 - x t( ) - 0.75 y t( )0.05 + x t( )
æçè
ö÷ø
, d dt
y t( ) = 0.2 y t( ) 1 - y t( )x t( )
æçè
ö÷ø
ìíî
üýþ
> IC5:=[[x(0)=0.2,y(0)=0.2],[x(0)=0.4,y(0)=0.2],[x(0)=0.5,y(0)=0.2],[x(0
> )=0.6,y(0)=0.2],[x(0)=0.2,y(0)=0.4],[x(0)=0.2,y(0)=0.5],[x(0)=0.4,y(0)=1.0
> ],[x(0)=1,y(0)=0.5],[x(0)=0.4,y(0)=0.4],[x(0)=0.39,y(0)=0.39]]:
> DEplot(ODE6,[x(t),y(t)],t=0..60.0,IC5,x=0..0.9,y=0..0.5,arrows=small,
> stepsize=.0475,color=black,linecolor=[red,coral,yellow,green,cyan,blue
> ,navy,maroon,magenta,black],title=`Brusselator Equation Phase Portrait`);
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0.40.20
y
0.5
0.4
0.3
x
0.2
0.1
0.80
0.6
Brusselator Equation Phase Portrait
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