An integrated climate-based daylight performance evaluation framework for indoor arenas' roof system

Abstract

Daylight environment plays a crucial role in indoor arenas' comfort and energy performance, characterized by their large spans and large spaces. Design parameters of roof systems in these arenas are essential for enhancing the daylight within competition halls. However, due to the complexity of spatial archetypes in these halls and the unique characteristics of freeform surfaces, more research is needed on the interplay between roof system design parameters and daylight performance. Rapid evaluation of daylight performance in competition halls during the design phase remains a challenge. This study proposes a climate-based evaluation framework for assessing daylight performance in indoor arenas and integrates an efficient practical and research workflow using Rhino + Grasshopper in conjunction with Python.Further, based on this framework and workflow, controlled variable simulation experiments were conducted to explore the relationships between external design conditions, such as climate (represented by three typical climatic zones) and seating capacity, and roof system design parameters, including roof form, redundant height, orientation, skylight form, distribution, and skylight-to-floor ratio (SFR), with time-series, climate-based daylight performance indicators. To integrate the critical factors affecting indoor lighting environments in arenas - illuminance potential, glare evaluation, and uniformity assessment - this study selected Continuous Daylight Autonomy (cDA), Useful Daylight Illuminance (UDI), and Glare Autonomy (GA) as performance indicators, supplemented by two time-series climate indicators specifically for evaluating illuminance uniformity in indoor arenas: “Uniformity Autonomy (UA) “and “Uniformity Variance Autonomy (UVA) ". Statistical analysis of five performance indicators across 860 experimental models in seven groups shows that different design conditions and parameters affect the optimal range of SFR. The significance of the impact of various design parameters varies across different performance indicators. There is a strong correlation between cDA, UDI, and GA, while UA and UVA effectively supplement the evaluation of the first three indicators.