I have made a simple model of heat transfer between ambient and a silicon chip (module) from which I can read its internal temperature $T_m$. I do not need fancy equations and an approximate model will do just fine.
I have modelled the system using electrical equivalent model (RC), but determining parameters requires equilibrium point data.
Using the heat transfer slope I could potentially determine the model parameters, only that the data is noisy and even using Rick Chartrand, "Numerical differentiation of noisy, nonsmooth data" algorithm I still couldn't get a good estimate for the actual derivatives.
My solution now is to fit data and extract parameters from empirical data.
After I heat the module I let it cool down, but to measure the internal temp I need some part of the module to run, which should account for a residual power $P$.
The model is $\frac{\delta T_m}{\delta t} = c (T_a -T_m(t)) + P$
I can easily assume that the room will not change temperature because of a very small chip running, such that $T_a$ is constant.
I can determine estimates of $c$ from halftime and $P$ from solving $\frac{\delta T_m}{\delta t} = 0$.
A linear sweep of values around this estimates will give probably sufficiently good estimates.
My problem comes from the fact that if I solve the diferential equation I end up with this solution
$T_m(t) = T_m(0) e^{-ct} + (cT_a+P)t$
which makes no physical sense since it simply states temperature will rise infinetly.
What is the right solution for this diferential equation of heat transfer?