Matlab solving a system of equations
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So I have a set of equations listed below. The varying number is the value of x which goes between -0.5 and 0.5 in increments of 0.1. This is then added to 0.153 to give me a vector of values for b. These values need to be then inserted into my equations and solved.
a = 1.4;
x = -0.5:0.1:0.5;
b = 0.153 + x.^2;
eqn1= Px == dx.*75451.26;
eqn2= dx == (0.183.*578.8.*0.403)/b;
eqn3= Px == dx.*287.*Tx;
eqn4= Tx == 300/(1+0.2.*Mx.^2);
eqn5= Vx == Mx.*(401.8.*Tx).^0.5;
sol = vpasolve(eqn1,eqn2,eqn3,eqn4,eqn5);
Here are my 5 equations
Réponses (1)
Alan Stevens
le 16 Mar 2021
Parameters, dx, Px, Tx, Mx and Vx can be evaluated simply as follows:
a = 1.4;
x = -0.5:0.1:0.5;
b = 0.153 + x.^2;
dx = (0.183.*578.8.*0.403)./b;
Px = dx.*75451.26;
% Tx = Px./(dx.*287); But this means Tx, Mx and Vx just have constant values:
Tx = 75451.26/287;
Mx = ((300./Tx - 1)*5).^0.5;
Vx = Mx.*(401.8.*Tx).^0.5;
disp(['Tx = ',num2str(Tx)])
disp(['Mx = ',num2str(Mx)])
disp(['Vx = ',num2str(Vx)])
plot(x,Px,'--o'),grid
xlabel('x'),ylabel('Px')
6 commentaires
Cizzxr
le 16 Mar 2021
Cizzxr
le 16 Mar 2021
Cizzxr
le 16 Mar 2021
Alan Stevens
le 16 Mar 2021
With these revised equations the values of Tx, Mx and Vx are no longer constant, but the same general approach can be taken:
a = 1.4;
x = -0.5:0.1:0.5;
b = 0.153 + x.^2;
dx = 0.183.*578.8.*0.403./b;
Px = 75451.26*dx.^a;
Tx = Px./(dx.*287);
Mx = ((300./Tx - 1)*5).^2;
Vx = Mx.*(401.8.*Tx).^0.5;
plot(x,Mx,'--o'),grid
xlabel('x'),ylabel('Mx')
Cizzxr
le 16 Mar 2021
Alan Stevens
le 16 Mar 2021
With your latest model you can no longer separate the equations - you need an iterative solution. The folowing uses fminsearch. Only you can decide if the resulting values are sensible:
a = 1.4;
x = -0.5:0.05:0.5;
b0 = 0.153;
Te = 300./(120000./7000).^(0.4./1.4);
density = 7000./(287.*Te) ;
M = ((((120000./7000).^(0.4./1.4))-1)./0.2).^0.5 ;
Ve = M.*((a.*287.*Te).^0.5);
dx0 = density.*Ve.*(0.403./b0)./ Ve;
Px0 = (7000.*(dx0.^a))./(density.^a);
Tx0 = Px0./(287.*dx0);
Mx0 = (((300./Tx0)-1)/0.2).^0.5;
Vx0 = Mx0.*((287.*1.4.*Tx0).^0.5);
K0 = [dx0; Px0; Tx0; Mx0; Vx0]; % Initial guesses
K = zeros(5,numel(x));
for i = 1:numel(x)
K(:,i) = fminsearch(@(K)fn(K,x(i)),K0);
end
% Extract variables
dx = K(1,:);
Px = K(2,:);
Tx = K(3,:);
Mx = K(4,:);
Vx = K(5,:);
plot(x,Mx,'--o'),grid
xlabel('x'),ylabel('Mx')
axis([min(x) max(x) 0 3])
function F = fn(K,x)
a = 1.4;
b = 0.153 + x.^2;
Te = 300./(120000./7000).^(0.4./1.4);
density = 7000./(287.*Te) ;
M = ((((120000./7000).^(0.4./1.4))-1)./0.2).^0.5 ;
Ve = M.*((a.*287.*Te).^0.5);
dx = K(1);
Px = K(2);
Tx = K(3);
Mx = K(4);
Vx = K(5);
dxn = density.*Ve.*(0.403./b)./ Vx;
Pxn = (7000.*(dxn.^a))./(density.^a);
Txn = Pxn./(287.*dxn);
Mxn = (((300./Txn)-1)/0.2).^0.5;
Vxn = Mxn.*((287.*1.4.*Txn).^0.5);
F = norm(dxn-dx)+norm(Pxn-Px)+norm(Txn-Tx)+norm(Mxn-Mx)+norm(Vxn-Vx);
end
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