The Thermal Data
Moving on from the restriction test bench the G2 radiator was loaded into the thermal test chamber for a series of 9 tests – consisting of 3 flow rates, each having 3 different fan rpm rates tested. I felt the thermal test data was most important and which you as the reader would be most interested in.
Below is the final data results gathered from at least 2 data logging runs at each flow rate and fan rpm combination. The most stable 15 minute period from each logging run was used and then averaged with the other runs to obtain the data for the table below. A total of 16 temperature sensors were used in the thermal test chamber (8 air in, 2 air out, 3 water in, 3 water out) each take a reading every second and logged via a CrystalFontz unit. The data in the table below is the result of the logging runs which has then been used to create all the plots and tables there-after.
The performance metric of critical importance is the delta between the warm coolant temperature in and the cold ambient air temperature in to the radiator. Given that the system is well insulated and in equilibrium and we know the heat input to the system then we can also calculate a very important number – that is the amount of power required to raise the coolant temperature 1C (or 10C which is more useful reference point).
Like the vast majority of the other radiators tested, the Coolgate G2 cares little about flow rate, particularly above 1.0GPM. Here are some plots to show the variance:
For those who love the curves, I have plotted a chart and added a poly-line to extrapolate the data. Note that the extrapolation of the curve is much more sensitive to error than in between the tested range.
So the performance is not greatly affected by varying flow rate. However Delta T is not always helpful when thinking about how many radiators you would need to cool your system. Instead it’s more useful to know the delta/W, or more usefully, the inverse metric of W/delta C.
The metric plotted below tells us how many watts are dissipated by the radiator when the coolant rises 10C above ambient temperatures. (10 Delta T):
As expected increasing fan speed and therefore airflow through the radiator is the primary determinant in changing the radiators performance. This data can now be plotted as a pretty curve so that an end user can interpolate their own fan speed. Note again that the extrapolation of the curve is much more sensitive to error than in between the tested range.
This makes it easier to see that at higher fan speeds that a low flow starts to impact the cooling performance. This makes sense if you take it to the extreme and think about a very low flow rate where the coolant is already cooled 99% of the way to ambient with 10% of its journey through the radiator. In this example the radiator is not being efficiently used. 90% of the radiator surface area would then be wasted and you could have used a smaller radiator.
Having said all of this in this next plot all three flow rate results were averaged together to produce one curve. This works well because the radiator was so flow rate insensitive. Averaging reduces test error of course so this helps further to be sure of our data and is more useful therefore for comparing to other radiators.
Now let’s go back to the push data:
Here we can see that there is a reasonable difference between Push and Push/Pull. This is not a surprise given how thick the G2 is but is worth bearing in mind. This is a radiator that will do better at Push/Pull.
Now let’s analyze that data.