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Data and code for "Influence of geometrical levels of detail and inaccurate material optical properties on daylight simulation results"

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DataCite Commons2026-02-13 更新2026-03-28 收录
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https://data.4tu.nl/datasets/70d68de0-3c37-4b30-a7cd-f128faa2d098
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This repository contains the data and code for the paper <strong>"Influence of geometrical levels of detail and inaccurate material optical properties on daylight simulation results."</strong><strong>Abstract:</strong> Optimising the built environment via simulations of building models hinges on standardising data acquisition. In this research, we put forward distinct levels of detail for geometry and material inputs, specifically tailored for indoor daylight applications. We primarily focus on understanding the uncertainties arising from imprecise estimations of material optical properties and incomplete geometrical inputs in climate-based indoor daylight simulations. Employing a Monte Carlo approach, we analysed six office and teaching spaces, creating 20 variations for each by altering geometrical completeness and material accuracy. The technique of excluding non-permanent objects below certain sizes in four graduated steps was used to derive and test the impact of various geometrical levels of detail. Our findings reveal that different levels of geometrical completeness lead to errors ranging from 1.08% to 18.05%. Additionally, a twofold increase in simulation time was noted when geometrical detail was enhanced relative to the most basic model. Errors stemming from imprecise definitions of material optical properties followed a normal distribution. The uncertainty in simulation outcomes showed a linear rise with increasing input material uncertainty, lying between 10% and 30%, depending on space configurations. We observed heightened uncertainty near openings, attributed to window transmittance effects. The research underscores that daylight predictions are markedly more sensitive to transmittance uncertainties than to those in reflectance, regardless of the window-to-floor ratio. These insights may help guide a more efficient data acquisition process for indoor spaces used in daylight simulations.
提供机构:
4TU.ResearchData
创建时间:
2026-02-13
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