I would like to solve the simple 1d ode and get the different solution vectors as long as the parameter p varies on a certain range
dx/dt=25.7+x*(0.00038*x-0.00000014*x^2)-x*(0.162+0.00000006*x^2)-p*x
Here is my code
function dxdt = lv(t,x,p)
dxdt = 25.7+x*(0.00038*x-0.00000014*x^2)-x*(0.162+0.00000006*x^2)-p*x;
end
and
clear all
% Write here the initial conditions
xstart = 1200.0;
tspan = 0:1000;
%parameters
% s = 25.7;
% a = 0.00038;
% b = -0.00000014;
% c = 0.162;
% d = 0.00000006;
%p = 0.01;
p = 0:0.001:0.1;
x_end=zeros(length(tspan),length(p));
for ii = 1:length(p)
[t,x] = ode45(#(t,x)lv(t,x,p(ii)),tspan,xstart);
end
I would like to collect the solution vectors obtained for any value of p.
Thanks a lot in advance for any help or suggestion
Related
I am trying to solve the 1D ADE
This is my code so far:
clc; clear; close all
%Input parameters
Ao = 1; %Initial value
L = 0.08; %Column length [m]
nx = 40; %spatial gridpoints
dx = L/nx; %Length step size [m]
T = 20/24; %End time [days]
nt = 100; %temporal gridpoints
dt = T/nt; %Time step size [days]
Vel = dx/dt; %Velocity in each cell [m/day]
alpha = 0.002; %Dispersivity [m]
De = alpha*Vel; % Dispersion coeff. [m2/day]
%Gridblocks
x = 0:dx:L;
t = 0:dt:T;
%Initial and boundary conditions
f = #(x) x; % initial cond.
% boundary conditions
g1 = #(t) Ao;
g2 = #(t) 0;
%Initialization
A = zeros(nx+1, nt+1);
A(:,1) = f(x);
A(1,:) = g1(t);
gamma = dt/(dx^2);
beta = dt/dx;
% Implementation of the explicit method
for j= 1:nt-1 % Time Loop
for i= 2:nx-1 % Space Loop
A(i,j+1) = (A(i-1,j))*(Vel*beta + De*gamma)...
+ A(i,j)*(1-2*De*gamma-Vel*beta) + A(i+1,j)*(De*gamma);
end
% Insert boundary conditions for i = 1 and i = N
A(2,j+1) = A(1,j)*(Vel*beta + De*gamma) + A(2,j)*(1-2*De*gamma-Vel*beta) + A(3,j)*(De*gamma);
A(nx,j+1) = A(nx-1,j)*(Vel*beta + 2*De*gamma) + A(nx,j)*(1-2*De*gamma-Vel*beta)+ (2*De*gamma*dx*g2(t));
end
figure
plot(t, A(end,:), 'r*', 'MarkerSize', 2)
title('A Vs time profile (Using FDM)')
xlabel('t'),ylabel('A')
Now, I have been able to solve the problem using MATLAB’s pdepe function (see plot), but I am trying to compare the result with the finite difference method (implemented in the code above). I am however getting negative values of the dependent variable, but I am not sure what exactly I could be doing wrong. I therefore will really appreciate if anyone can help me out here. Many thanks in anticipation.
PS: I can post the code I used for the pdepe if anyone would like to see it.
I have written a script to compute and solve a simple inverted pendalum system.Now suppose that I want to solve the nonlinear dynamic equation of the system with ODE45 function with different values of initial conditions.How could I use a for loop to solve for state vector of X for different values of initial conditions?I wrote a for loop to do that but I could not get the answer I wanted.Help me please.Here are my function and mfile as follows:
function xDot = of(x,g,L,u)
xDot = zeros(2,1);
xDot(1) = x(2);
xDot(2) = ((g./L)*sin(x(1)))+u;
end
And this is my main code:
clc;
clear;close all;
%% Solve The Nonlinear Equation
L = 1;
g = 9.81;
h = 0.25;
t = [0:h:5];
A = [0 1;(g/L) 0];
B =[0 1]';
Ics = [pi,0;pi/2 0;pi/5 0;0.001 0;pi 0.5;pi/2 0.5;pi/5 0.5;0.001 0.5];
[Poles,~] = eig(A); %Poles Of Closed LOop System
R = 0.01;
Q = eye(2);
K = lqr(A,B,Q,R);
u = #(x)-K*(x);
for i=1:size(Ics,1)
[~,X] = ode45(#(t,x)of(x,g,L,u(x)),t,Ics(i,:));
end
Also note that I want the first column of X vector which is the angular displacements of the pendulum in each iteration because the second column of X vector in ODE45 is always the Derivative of the main state vector.
You can store all the integration outputs for the different initial conditions in a 3D array.
The number of rows of Xout will equal the number of time steps at which you want to evaluate your solution, so numel(t). The number of columns is the number of states, and then the third dimension will be the number of initial conditions you want to test.
Xout = zeros(numel(t), size(Ics, 2), size(Ics, 1)); % initialize the 3D array
for k = 1:size(Ics, 1)
[~, Xout(:, :, k)] = ode45(#(t, x)of(x, g, L, u(x)), t, Ics(k, :));
end
The documentation online for this particular function is poor, and I was looking for an example on how to use it. It seems to take in 9+ arguments, but I'm not entirely sure what they even are. I am working on implementing function using the trapezoidal rule, however, I am missing the functions that compute the partial derivatives with respect to y and t. I believe that calculating one for t in MATLAB is another case, but here, I'm just trying to use MATLAB to compute it for y.
I'm using the following input initially:
func = #(t,y)(y.^2+y+t);
interval = [0 1];
y0 = 0;
[steps, derivY, derivJ, W, inverseW] = getTrapezoidalODEValues(func, interval, y0)
I set up the function like this:
function [h,J,T,W,iW] = getTrapezoidalODEValues(odefun,tspan,y0,options)
if (nargin==3)
options = odeset();
end
h = NaN; J(0) = NaN; T(0) = NaN; W(0) = NaN; iW = NaN;
[t,yy] = ode(odefun,tspan,y0,options);
[nstep,ndim] = size(yy);
d = 1/(2+sqrt(2));
for j=2:nsteps
h = t(j)-t(j-1);
I = eye(???,???); % identity matrix
quadpoly = ???; % the quadratic polynomial
J(j-1) = numjac(???); % <--partial derivative of odefun with respect to y
T(j-1) = ???; % partial derivative of odefun with respect to t
W(j-1) = I - h .* d .* J;
iW(j-1) = inv(W(j-1));
end
end
I would like to use Matlab to compute two finite difference loops in such a manner that if we have two equations, let's say (1) and (2), it completes one step of (1) then solves (2) for one step then (1) for the next step and then (2) and so on and so forth.
To this end, I provide the parameters of my code below:
%% Parameters
L = 5; % size of domain
T = 5; % measurement time
dx = 1e-2; % spatial step
dt = 1e-3; % time step
x0 = 0;
c = 1;
%%
t = 0:dt:T; % time vector
x = (0:dx:L)'; % spatial vector
nt = length(t);
nx = length(x);
Lx = (1/dx^2)*spdiags(ones(nx,1)*[1 -2 1],-1:1,nx,nx); % discrete Laplace operator
mu = dt/dx;
I = eye(nx,nx); % identity matrix
A = spdiags(ones(nx,1)*[-1 1 0],-1:1,nx,nx); % finite difference matrix
Then the first loop is given by
%% Finite Difference Equation (1)
% preallocate memory
u = zeros(nx,nt);
v = zeros(nx,nt);
% initial condition in time
u(:,1) = sinc((x-x0)/dx);
v(:,1) = sinc((x-x0)/dx);
for i = 1:nx-1
u(:,i+1) = ((1/(c*dt))*I+(1/dx)*A)\((1/(c*dt))*u(:,i)+v(:,i));
end
and the second equation (2) is given by
%% Finite Difference Equation (2)
% preallocate memory
u = zeros(nx,nt);
v = zeros(nx,nt);
% final condition in time
u(:,nt) = sinc((x-x0)/dt);
% initial condition in space
for j = nt:-1:2
v(:,j-1) = ((1/dx)*A+(1/(c*dt))*I)\((1/(c*dt))*v(:,j)
end
In the current format, Matlab will run the first loop i = 1:nx-1 and then the second loop j = nt:-1:2.
But I want to run the two loops as follows: i = 1, then j = nt, then i = 2, then j = nt-1 and so on and so forth. How should I code this?
You can composite two loops like the following:
% define other variables and preallocations
j = nt;
for i = 1:nx-1
u(:,i+1) = ((1/(c*dt))*I+(1/dx)*A)\((1/(c*dt))*u(:,i)+v(:,i));
v(:,j-1) = ((1/dx)*A+(1/(c*dt))*I)\((1/(c*dt))*v(:,j)
j = j - 1;
end
for i = 1:nx-1
u(:,i+1) = ((1/(c*dt))*I+(1/dx)*A)\((1/(c*dt))*u(:,i)+v(:,i));
%This if will be true once each 10 iterations
if(mod((nt-i),10)==0)
j=((nt-i)/10)+1;
v(:,j-1) = ((1/dx)*A+(1/(c*dt))*I)\((1/(c*dt))*v(:,j);
end
end
Don't really know if this will work, but making it more usable as you are trying my idea.
I'm new to Matlab and am really struggling even to get to grips with the basics.
I've got a function, myspring, that solves position and velocity of a mass/spring system with damping and a driving force. I can specify values for the spring stiffness (k), damping coefficient (c), and mass (m), in the command window prior to running ode45. What I am unable to do is to define a forcing function (even something simple like g = sin(t)) and use that as the forcing function, rather than having it written into the myspring function.
Can anyone help? Here's my function:
function pdot = myspring(t,p,c,k,m)
w = sqrt(k/m);
g = sin(t); % This is the forcing function
pdot = zeros(size(p));
pdot(1) = p(2);
pdot(2) = g - c*p(2) - (w^2)*p(1);
end
and how I'm using it in the command window:
>> k = 2; c = 2; m = 4;
>> tspan = linspace(0,10,100);
>> x0 = [1 0];
>> [t,x] = ode45(#(t,p)myspring(t,p,c,k,m),tspan,x0);
That works, but what I want should look something like this (I imagine):
function pdot = myspring(t,p,c,k,m,g)
w = sqrt(k/m);
pdot = zeros(size(p));
pdot(1) = p(2);
pdot(2) = g - c*p(2) - (w^2)*p(1);
end
Then
g = sin(t);
[t,x] = ode45(#(t,p)myspring(t,p,c,k,m,g),tspan,x0);
But what I get is this
In an assignment A(:) = B, the number of elements in A and B must be the same.
Error in myspring (line 7)
pdot(2) = g - c*p(2) - (w^2)*p(1);
Error in #(t,p)myspring(t,p,c,k,m,g)
Error in odearguments (line 87)
f0 = feval(ode,t0,y0,args{:}); % ODE15I sets args{1} to yp0.
Error in ode45 (line 115)
odearguments(FcnHandlesUsed, solver_name, ode, tspan, y0, options, varargin);
Horchler, thank you for the reply. I can do as you suggested and it works. I am now faced with another problem that I hope you could advise me on.
I have a script that calculates the force on a structure due to wave interaction using Morison's equation. I gave it an arbitrary time span to begin with. I would like to use the force F that script calculates as the input driving force to myspring. Here is my Morison script:
H = 3.69; % Wave height (m)
A = H/2; % Wave amplitude (m)
Tw = 9.87; % Wave period (s)
omega = (2*pi)/Tw; % Angular frequency (rad/s)
lambda = 128.02; % Wavelength (m)
k = (2*pi)/lambda; % Wavenumber (1/m)
dw = 25; % Water depth (m)
Cm = 2; % Mass coefficient (-)
Cd = 0.7; % Drag coefficient (-)
rho = 1025; % Density of water (kg/m^3)
D = 3; % Diameter of structure (m)
x = 0; % Fix the position at x = 0 for simplicity
t = linspace(0,6*pi,n); % Define the vector t with n time steps
eta = A*cos(k*x - omega*t); % Create the surface elevation vector with size equal to t
F_M = zeros(1,n); % Initiate an inertia force vector with same dimension as t
F_D = zeros(1,n); % Initiate a drag force vector with same dimension as t
F = zeros(1,n); % Initiate a total force vector with same dimension as t
fun_inertia = #(z)cosh(k*(z+dw)); % Define the inertia function to be integrated
fun_drag = #(z)(cosh(k*(z+dw)).*abs(cosh(k*(z+dw)))); % Define the drag function to be integrated
for i = 1:n
F_D(i) = abs(((H*pi)/Tw) * (1/sinh(k*dw)) * cos(k*x - omega*t(i))) * ((H*pi)/Tw) * (1/sinh(k*dw)) * cos(k*x - omega*t(i)) * integral(fun_drag,-dw,eta(i));
F_M(i) = (Cm*rho*pi*(D^2)/4) * ((2*H*pi^2)/(Tw^2)) * (1/(sin(k*dw))) * sin(k*x - omega*t(i)) * integral(fun_inertia,-dw,eta(i));
F(i) = F_D(i) + F_M(i);
end
Any further advice would be much appreciated.
You can't pre-calculate your forcing function. It depends on time, which ode45 determines. You need to define g as a function and pass a handle to it into your integration function:
...
g = #(t)sin(t);
[t,x] = ode45(#(t,p)myspring(t,p,c,k,m,g),tspan,x0);
And then call it I n your integration function, passing in the current time:
...
pdot(2) = g(t) - c*p(2) - (w^2)*p(1);
...