[bobo5195]

Ah Les, surely a man of your talents could work out a solution to the fourier equation for an IHS + heat die. Might want to try STRAND7, its an FEA (doesn’t do fluids) package the demo goes up to 100 elements which might just do for your tasks

There is no true (as in perfectly true) calibration factor for cpus as there is two sets of data that need to be know. The absolute heat flux and the distribution of this heat flux. Give that the dim sims mentioned on this board are based on constant surface temperature / heat flux distribution there is going to be some difference that may or may not be significant. The distribution of the heat energy “flow” is a unknown for cpus and will probably remain that way. One solution might be to ignore it and say that heat distribution to the WB or a at least a short distance inside it becomes constant. To model the increased resistance because of the longer heat path some kind of constant can be used.

Generating this constant other than clicking the random number generator; guesstimation in your head might be a little tricky. The proper solution would be to look at statistical distributions in real cpus and pick an appropriate value. This might help with heat distributions as well if you could include them. Further more with knowledge about cpu heat output distribution you could include that as well. I have a feeling that intels formula is empirical using stats from a large number of cpus. Get some chips test the output, put a resistance in. From these results find a conservative value for what the factor should be(or indeed the form of the factor. Could say that thermal paste thickness is increased by X or ideal resistance multiplied by some number).

Obviously DIYers can’t do this. A statistical model using FEA could easily be developed with perfect knowledge about the manufacturing (FEA all possible paths and get a result). We don’t have a perfect data but we can guess.
The unknowns are:
CPU total heat output – Guess able or non dimensionalise.
CPU heat distribution – hard. Constant heat flux might be appropriate. I think being a little smart and saying the cache is cooler than the processing units might be valid.
Distribution of thermal compound – This again is hard. We’re going to need some BC’s here to help.
Geometry of HIS – this is obviously some manufacturing variations, indeed are we even going to say the same about the core (given the process for making a core I would say that saying its perfectly flat and level is a suitable assumption).
Material properties – some silicon to model the core, thermal paste is doable, I H S is doable as well. Interface properties if we chose to include them might be hard.
Block properties – Do we include the interface there, I assume just use Bills data, but the distribution might prove troublesome.

My suggestion would be:
Assume we’ve got material properties and say the I h s conforms exactly to available data. Data on heat output from cpus is around. The temperature distribution can be made up by some guy with a beard (preferably long, with pizza in it :P), even have it as a changing distribution(ie manufacturing difference between cpus, could do further test to show influence on OCing etc. Thermal paste distribution is then the troublesome bit. We can however say what the limiting value is as we can guess when the core can shut down due to heat with the bare minimum craptastic heat sink. We can also guess what perfectly applied thermal paste would look like. Easy option then is run FEA on this millions of times to show what a random distribution of the variables would look like. This then gives you an approximate model of heat distribution on the surface of the I h s (so you can tell if its approximately constant or not) and a good GUESS at thermal resistance of the rig. This kind of procedure of empirical FEA is carried out by a few companies to check that parts as a group won’t fail etc. It will take awhile however on computers. Its not SETI but a good evenings work and computer time.