Using Dragonfly in an iterative HB urban energy workflow

Hi all

I am new here so first of all congrats to Mostapha, Chris and all the contributors for this wonderful work!

I am running an energy parametric evaluation workflow with HB for different urban block configurations., and would like to integrate Dragonfly to account for the UHI effect.

In this iterative workflow I have several dynamic building and urban scale parameters and would be grateful to hear your opinion, according to the design of the Dragonfly tool, which should be considered as relevant for UHI evaluation.

The list of parameters:

• Building use (different energy input parameters but also floor height)
• Glazing ratio (20,40,60,80)
• Glazing properties (2 different SHGC and Tv)
• Floor Area Ratio (2,4,6,8)
• Distance between buildings (10,20,30)
• Typology (different contours – high rise, slab EW, slab NS, scatter, courtyard)
• Urban grid rotation (0, 45)

I guess I can generate 1920 EPWs (total num. of iterations), but figured some parameters might be redundant… What do you think?

I will also probably do a fast sensitivity analysis and compare results between few iterations

Thank you !

Jonathan

If you are investigating geometrical aspects I think you should keep the energy input parameters constant and as simple as possible. I also think EnergyPlus is a bit to detailed for a study like this, mostly due to calculation time.
/Max

I would suggest that you take a read through Aiko Nanoko’s Thesis on the UWG (see pdf at bottom of the page) before setting your sensitivity study to run. Aiko has done a sensitivity study of a few different parameters to see which ones had the largest effect on cooling energy use. It may help you narrow down your list since some of your parameters won’t have a significant effect on UHI. You have also left out two parameters that have a significant effect (the sensible heat from traffic and the fraction of building cooling energy that is rejected to the urban canyon).

Also, rotating the urban grid won’t have any impact on the Urban Weather Generator’s calculation since the UWG tries to approximate the average change in air temperature over a district’s urban canyons. It does this by computing the energy balance of an omni-directional infinite urban canyon.

Rotating the canyons may affect the radiant temperature differences that you will get in different parts of urban area but it will not affect this average urban canyon temperature.

Thanks MAX,

These are the parameters I use for my energy evaluation and want to use (the same) for the UHI.
BTW in my scale of exploration, Energyplus handled it reasonably well (see https://doi.org/10.1016/j.egypro.2018.09.133)

Dear Chris,

Thank you, I will go over Aiko’s work. The question is whether her sensitivity analysis for Boston applies for my Mediterranean context (TLV). I think it’ll be wise to run my own check with my parameters (which i previously used for the energy and daylight modules of my study).

@JonathanNatanian
Coupling UWG and E+ for a parametric urban block study is doable (i’ve recently experimented with similar simulations) but it will make sense, only under the assumption that a neighborhood of a sufficient size is built using the exact same block typology. Don’t be surprised if you find UWG a bit inelastic towards the parameters you examine.
I suggest you first run a seperate parametric study to generate EPWs and then import them into the E+ workflow. Generating unique EPW names does the trick. Several of your E+ cases may use the same EPW to reduce sim loads. Another simpler approach is to have a specific case study city and generate one EPW for each Local Climate Zone. This will also allow you to validate the results against real world measurements if such measurements exist.
Also, you might want to consider local shading in your analysis instead of glazing type, since the former is more related to urban block design than the latter. See here for a similar study: https://www.sciencedirect.com/science/article/pii/S2210670716303572

Dear Aris,

Thank you so much for these great insights!