A comparison of two light-redirecting fenestration systems using a modified modeling technique for Radiance 3-phase method simulations

Abstract

This paper compares two different macroscopic light-redirecting fenestration systems (LRFS) using Radiance’s three-phase method. The goal is to assess the potential of simplified daylight metrics that are less computationally expensive, such as Daylight Factor (DF), in the optimization of LRFS. This work compares a highly specular LRFS optimized for DF (Lasy_S) with a validated commercial LRFS (LightLouver) using a Double Clear Glazing window as the control case. The comparison uses two different locations representative of two different annual sky conditions: London, UK - overcast; Phoenix, AZ, USA – clear. Useful Daylight Illuminance (UDI) and annual Daylight Glare Probability (DGP) are the metrics used to evaluate daylight availability and visual comfort. To facilitate the efficient modeling of customized macroscopic LRFS in the three-phase method workflow, this work extends Radiance’s genblinds routine, making it able to generate complex venetian blinds systems based on multiple-curved sections. With the modified genblinds, the Lasy_S system is properly remodeled for the three-phase method for an accurate comparison of the different systems. The analysis shows that Lasy_S is a light-weighted and low-maintenance LRFS that outperforms LightLouver in terms of useful annual illuminance levels in both locations, being more effective in cloudier skies due to the metric used in the optimization. Nevertheless, albeit the system mitigates glare, it is not as successful as the commercial LRFS. This indicates that DF and annual horizontal illuminance metrics are unable to properly inform an optimization process on glare performance, thus being more appropriate for initial exploratory optimizations. Hence, to fully address glare, daylight optimization procedures based on DF should be complemented with more detailed glare simulations that do not require unreasonable computational resources.