Daylight simulation workflows incorporating measured bidirectional scattering distribution functions
[Display omitted] •Considerable R&D has improved the accuracy and speed of Radiance ray-tracing tools.•Data-driven BSDFs and matrix algebraic simulation methods have been validated.•Examples of daylight analysis workflows using these new tools are provided.•An assessment of what’s needed to achi...
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Veröffentlicht in: | Energy and buildings 2022-03, Vol.259, p.111890, Article 111890 |
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Sprache: | eng |
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Zusammenfassung: | [Display omitted]
•Considerable R&D has improved the accuracy and speed of Radiance ray-tracing tools.•Data-driven BSDFs and matrix algebraic simulation methods have been validated.•Examples of daylight analysis workflows using these new tools are provided.•An assessment of what’s needed to achieve broad use of the tools is provided.
Daylight predictions of architectural spaces depend on good estimates of light transfer through skylights, windows and other fenestration systems. For clear glazing and painted surfaces, parametric transmission and reflection models have proven adequate, but there are many cases where light-scattering, semi-specular shading and daylighting materials defy simple characterization. Something as commonplace as fabric roller shades and venetian blinds may turn daylight prediction into guesswork, and numerous advanced systems on the market tuned specifically to enhance daylight are not sufficiently characterized to distinguish their performance. In this paper, we describe new tools available to handle novel and specialized fabrics, materials, and devices using data-driven modelling of bi-directional scattering distribution functions (BSDFs). These representations are usually tabulated at constant or adjustable angular resolution for efficient point-in-time and annual daylight simulations. We describe a variety of BSDF simulation workflows, including some of the tools and methods that make advanced analysis possible, and highlight some of the current challenges. We conclude with a discussion of future work and how such data might be created and shared worldwide. |
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ISSN: | 0378-7788 1872-6178 |
DOI: | 10.1016/j.enbuild.2022.111890 |