Autocatalytic process: Difference between revisions
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<math> \frac{d y}{d t} = \alpha\cdot y \cdot (A-y) </math>, i.e. <math> y' = \alpha\cdot y \cdot (A-y) </math>.  | <math> \frac{d y}{d t} = \alpha\cdot y \cdot (A-y) </math>, i.e. <math> y' = \alpha\cdot y \cdot (A-y) </math>.  | ||
The initial population is <math>y(0)= s</math>.  | The initial population is <math>y(0)= s</math>, <math>A := 5</math>.  | ||
The blue line is the simulation with <math>\Delta t = 0.1</math>.  | The blue line is the simulation with <math>\Delta t = 0.1</math>.  | ||
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   var y = alpha.X()*t.pos[1]*(A-t.pos[1]); // Autocatalytic process  |    var y = alpha.X()*t.pos[1]*(A-t.pos[1]); // Autocatalytic process  | ||
   //var y = (alpha.X()*t.pos[1]-tau*t.pos[1]*t.pos[1]); // Logistic process  |    //var y = (alpha.X()*t.pos[1]-tau*t.pos[1]*t.pos[1]); // Logistic process  | ||
   t.  |    t.lookTo([1.0+t.pos[0],y+t.pos[1]]);  | ||
  t.fd(delta*Math.sqrt(1+y*y));  | |||
   x += delta;  |    x += delta;  | ||
   if (x<10.0) {  |    if (x<10.0) {  | ||
      setTimeout(loop,  |       setTimeout(loop,10);  | ||
   }  |    }  | ||
}  | }  | ||
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function loop() {  | function loop() {  | ||
   var y = alpha.X()*t.pos[1]*(A-t.pos[1]); // Autocatalytic process  |    var y = alpha.X()*t.pos[1]*(A-t.pos[1]); // Autocatalytic process  | ||
   t.  |    t.lookTo([1.0+t.pos[0],y+t.pos[1]]);  | ||
  t.fd(delta*Math.sqrt(1+y*y));  | |||
   x += delta;  |    x += delta;  | ||
   if (x<10.0) {  |    if (x<10.0) {  | ||
      setTimeout(loop,  |       setTimeout(loop,10);  | ||
   }  |    }  | ||
}  | }  | ||
Revision as of 12:59, 23 April 2009
Autocatalytic population growth model
In time [math]\displaystyle{ \Delta t }[/math] the population grows by [math]\displaystyle{ \alpha\cdot y \cdot(A-y) }[/math] elements: [math]\displaystyle{ \Delta y = \alpha\cdot y\cdot \Delta t \cdot(A-y) }[/math], that is [math]\displaystyle{ \frac{\Delta y}{\Delta t} = \alpha\cdot y \cdot(A-y) }[/math].
With [math]\displaystyle{ \Delta \to 0 }[/math] we get [math]\displaystyle{ \frac{d y}{d t} = \alpha\cdot y \cdot (A-y) }[/math], i.e. [math]\displaystyle{ y' = \alpha\cdot y \cdot (A-y) }[/math].
The initial population is [math]\displaystyle{ y(0)= s }[/math], [math]\displaystyle{ A := 5 }[/math].
The blue line is the simulation with [math]\displaystyle{ \Delta t = 0.1 }[/math].
The JavaScript code
<jsxgraph height="500" width="600" board="board"  box="box1">
brd = JXG.JSXGraph.initBoard('box1', {originX: 10, originY: 250, unitX: 40, unitY: 20, axis:true});
var t = brd.createElement('turtle',[4,3,70]);
            
var s = brd.createElement('slider', [[0,-5], [10,-5],[-5,0.5,5]], {name:'s'});
var alpha = brd.createElement('slider', [[0,-6], [10,-6],[-1,0.2,2]], {name:'α'});
var e = brd.createElement('functiongraph', [function(x){return s.X()*Math.exp(alpha.X()*x);}],{strokeColor:'red'});
t.hideTurtle();
A = 5;            
function clearturtle() {
  t.cs();
  t.ht();
}
            
function run() {
  t.setPos(0,s.X());
  t.setPenSize(4);
  delta = 0.1; // global
  x = 0.0;  // global
  loop();
}
             
function loop() {
  var y = alpha.X()*t.pos[1]*(A-t.pos[1]); // Autocatalytic process
  t.lookTo([1.0+t.pos[0],y+t.pos[1]]);
  t.fd(delta*Math.sqrt(1+y*y));
  x += delta;
  if (x<10.0) {
     setTimeout(loop,10);
  }
}
</jsxgraph>