Le 03/02/2023 à 21:25, Samuel Gougeon a écrit :
Le 03/02/2023 à 20:57, Samuel Gougeon a écrit :
Here is a draft proposal:
1) build the (let's say row) vector A = (dPHI/dt) of sampled data at
sampled values t
2) build the row vector B = f(-t) of sample data at t values
3) build the matrix C of (padded) A and the matrix D of (shiffted and
padded) D = B(T-t), with T as multiples of the dt step
   Each row of C and D corresponds to a T value.
4) compute E = (C .* D)*dt  when dt is the time step.
5) Set to zero all elements on (#) and above the diagonal of E.
   That's to cancel elements with t>T, to code the upper bound T of
the integral.
5) sum E along rows  (the actual integration. You can refine with a
trapeze method).

and that's it: the resulting column vector is your F(T).

As you know the formal expressions of f() and of phi(t), the other way is to 
use intg() in a for loop over T,
leading to something like this:

m = 2;         // Weibull modulus in mechanism  #1
k = 1E-7;      // corrosion rate(s-1) in mechanism  #1
function  r = pseudoconv(t, T)
   r = 3600*m*k*((k*t)^(m-1)) * exp(-((k*t)^m)) * (1 - exp((T+t)/3E5));
n = 500;
dT = 5;
F = zeros(1, n);
T = (0:(n-1))*dT;
for i = 1:n
   F(i) = intg(0, T(i), pseudoconv);


   F(i) = intg(0, T(i), list(pseudoconv, T(i)));

instead. That's the point preventing to use conv()!


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