CLIMATE-BASED DAYLIGHT MODELLING Parametric design tools such as Grasshopper and environmental analysis plugins Ladybug and Honeybee (http://www.grasshopper3d. com/group/ladybug) have opened up a huge potential in time-series analysis. These tools allow the user to adapt and amend the design to achieve desired target daylight levels by running multiple design iterations on geometry and material properties.
When considering our simple test building in Birmingham, UK we are able to amend the glass transmissivity values, introduce shading arrangements, amend the window to wall ratio (WWR) and return values in key daylight metrics including, but not limited to, Daylight Autonomy, Useful DayIight Illuminance, and Spatial Daylight Autonomy.
Daylight Autonomy (DA): A temporal metric that describes the annual daylight accessibility. It is defined as the percentage of total occupied hours that exceeds a specified illuminance level (generally 300 lux is required for an office) Useful Daylight Illuminance (UDI): A temporal metric that describes the potential for daylight comfort. This metric is calculated as same way as Daylight Autonomy, but with difference minimum threshold 100 lux, and a maximum threshold 2000 lux.
As we move into this parametric environment, it is quickly evident that there are potentially thousands of iterations. A relatively simple sounding analysis can quickly build up into numerous studies and more significantly numerous computational hours. For example, if you have 100 nodes in a 1m x 1m grid and 8,760 hours, you now have 876,000 data points. If you run three room depths, three room heights, eight orientations, three WWR’s, and three shade designs, you now have 648 possible combinations, or 567,648,000 data points. If you are looking at DA, UDI,
www.daylightingmag.co.uk
and cDA, you now have over 1 billion pieces of data. As mentioned, one issue is the amount of time needed to run multiple simulations for intensive ray-tracing calculations, but secondly there is the issue in presenting data when there are thousands of iterations. A tool that is able to present this information when exploring multidimensional parametric studies is the Thornton Tomasetti-developed DesignExplorer http://core. thorntontomasetti.com/designexplorer-announcement/. This tool allows the user to navigate through
Spatial Daylight Autonomy (sDA): A spatial metric that describes the percentage of the studied floor area that receives daylight over the target level.’ This metric is calculated based on the results of the Daylight Autonomy study and measures the percentage of an analysis area that exceeds a specified illuminance level (300 lux) for at least 50% of 3,650 annual hours (8am-6pm).
May/Jun 2017
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