Mr.Comfy 0.21 brings two new main features: custom thermal variable expressions and improvements in how the daylight mapping functionality can now be used more independently of the thermal mapping inputs, requiring only a set of Daysim *.pts and *.ill files to be provided. See the full list of changes here. The new metrics functionality is probably more exciting, so let’s see what that is all about:
One of the original development drivers behind Mr.Comfy has always been the insight that specific combinations of design intent, building typology and climate require custom analysis scenarios to achieve highest projected early design-stage building performance. Environmental design procedures should be uniquely tuned to marry process with data interpretation and generation scenarios, and link performance display to geometry. That’s the interstice in which the tool operates; the newest version goes a step further by allowing custom report variable generation from existing E+ report outputs.
Monthly performance map of “Zone Discomfort Excess Solar Gains” while space cooling active, custom thermal variable
Take the above example of the “Cupertino” sample building simulated in South Florida’s subtropical climate. A common design question in such a scenario is whether the shading strategy in combination with active cooling always delivers occupant comfort; the custom metric “Zone Discomfort Excess Solar Gains”, displayed above for each zone and month of the year, reveals that due to lower winter sun angles and still quite high outside air temperatures, several building spaces exhibit discomfort. A custom variable expression that only writes solar gains to a new data field if either the zone air temperature is above 26°C or Pierce TSENS thermal comfort index is higher than or equal to 1.8, which indicates heat discomfort, makes it possible to identify the exact location and point in time when this behaviour occurs- which can then e.g. be fixed through different shading strategies. Custom variables thus make it possible to design spatially visualized thermal metrics that are specifically tuned to the climate and building typology at hand. How they work in practice is documented here.
I hope that users will come up with their own metrics that you’re then willing to share here; I’d love to see some applied examples that are not as, well, constructed as the one above :)
The remaining updates for the 0.21 release concern improved thermal display processing speed, fixing the DIVA VIPER thermal parser to work with daylight co-display, and various little addons and improvements. As quite a few things changed under the hood, if you find any bugs, please just let me know.