E: Paper Session_T7: Daylighting, Empirical Predictions and Environmental Factors
From Perception to Design: Daylight Glare Mitigation in Architectural Spaces
1University of Arizona; 2AIA, LEED AP
Daylight glare is one of the most intricate and dynamic phenomena to work with when designing architectural spaces. It has been researched across diverse fields including ophthalmology, photometry, architecture, environmental sciences, materials engineering, etc. Finding a comprehensive approach that interconnects these disciplines to inform real-world architectural practice, however, has proven elusive. Glare is defined as the excessive amount of light or high luminance ratios as perceived by the eye according to the Illuminating Engineering Society (IES 2018). Quantifying excessive light or luminance ratios, however, is challenging due to the eye’s adaptability and constantly changing outdoor illumination. Daylight discomfort glare can be assessed using different methods addressed in previous research studies such as: luminance contrast ratio, daylight glare probability (DGP), vertical eye illuminance, etc. In this study, we synthesize findings through systematic literature review from ophthalmology and photometry to understand the structure of the eye and glare occurrence to assist architects and researchers conducting glare simulations. We analyze glare in an indoor space by performing various daylighting computer simulations and demonstrate how to mitigate it via different design strategies. A computer model of the space is created in Rhino and simulated in DIVA to obtain horizontal illumination data, and DGP metrics. In this study, we use a mixed methodological approach which includes selecting an existing library space and analyzing it via: (1) in-person field visits and collecting horizontal illumination data using a luminance meter, (2) performing two-level baseline simulations: [a] horizontal illuminance at 30” above finish floor level, and [b] a DGP analysis, (3) validating computer-simulated horizontal illuminations with corresponding field-measured data discussed in step 1, (5) assessing and evaluating the baseline and its glare conditions (6) proposing glare-mitigation strategies based on published studies, and (7) presenting an improved design case simulation which incorporates glare control and mitigation with daylighting strategies.
Model Calibration for Circadian Daylighting in ALFA: Developing Empirical Circadian Predictions in Physical Scaled Model
Kent State University, United States of America
Daylight as an important element of sustainability, has a strong impact on human health and well-being. Many studies showed that with access to natural light in the space, the occupants’ mood and performance are improved. This is related to human responses to multi-spectral characteristics of daylight and referred as non-visual effects. These effects play an important role in adjustment of the circadian system, sleep quality and alertness levels. This study utilizes a computational tool called Adaptive Lighting for Alertness (ALFA), a plug-in for rhinoceros that can calculate both visual and non-visual effects within a 3D model to predict circadian potential of daylight. To reduce prediction errors, a physical scaled model was built and tested under overcast sky to calibrate simulation model for real conditions. The quantitative Daylight Factor (DF) results of the physical model dataset for a point-in-time measurement are presented in depth and compared with results of 3D model simulation. The conclusions substantially indicate that the ALFA simulation software predicts the levels of daylight in line with outcome of on-site measurement in physical model with 98.89 percent correlation. The prediction result of the software is slightly marginal under-predicts the levels of daylight with 1.0536 calibration coefficient due to some material mismatch in real-world on-site simulation conditions and software simulation settings.
Additionally, this paper examines if physical models can be used for daylight circadian potential predictions while in design stage. For this purpose, the concept of linear regression was adopted to predict the non-visual to visual effects ratio by using the basic information of field measurement such as daylight factor. The simulation results verified that the average absolute relative error is less than %3, which is acceptable in real-world application. In future studies, validation on other parameters can be performed, such as other sky conditions, various window configuration and orientation, to add this consideration in daylighting pre-design evaluation.
A Pilot Study on the Contextual and Environmental Factors Influencing Window Shading Preference
Baker Lighting Lab, University of Oregon, United States of America
The use of window shading devices can affect building energy use, supplemental lighting demands and occupant well-being related to performance, alertness, and satisfaction. Past studies that have explored the impact of window shading devices on building and occupant performance have been done primarily in the context of office buildings. With the aim of expanding this research to healthcare, hospitality, and educational spaces; this study investigates the influence of program type and sky condition on window shading preferences for a number of shading types. This paper introduces an online survey that recorded participant preferences for eight window shading conditions in the context of six spaces with varying program types. The selected program types represent spaces commonly found in education, hospitality, and healthcare with two levels of privacy; ‘high privacy’, with a typical maximum occupancy of two people and ‘low privacy,’ with furniture designed to accommodate a group of people. A questionnaire was circulated on social media platforms to recruit anonymous participants, who were given a brief description of the program types and then exposed to eight images of that space with varying window treatment conditions. Participants were asked to assign a preference rank to each of the eight window shading conditions for each of the six program types included in our study. This was done to determine whether building occupants prefer ‘closed’ window shading conditions in ‘high privacy’ and ‘open’ shading conditions in ‘low privacy’ spaces. As hypothesized, ‘half closed’ window shading settings were preferred for program types with ‘low privacy’ requirement and ‘full closed’ window shading conditions were preferred for spaces with ‘high privacy’ requirement. The results from this study showed that contextual factors such as program type and environmental factors such as sky condition impact a participant’s preference for window shading types and the degree of preferred occlusion.