Keywords:BSDF, BRDF, BTDF, Geometric optics, Scattering, Complex Fenestration Systems
The transmission and distribution of light through Complex Fenestration Systems (CFS) impacts visual comfort, solar gains and the overall energy performance of buildings. For most fenestration, scattering of light can be approximated as the optical property of a thin surface, the Bidirectional Scattering Distribution Function (BSDF). It is modelled in simulation software to replicate the optical behaviour of materials and surface finishes. Data-driven BSDF models are a generic means to model the irregular scattering by CFS employing measured or computed data-sets. While measurements are preferred by researchers aiming at realism, they are constraint by the measurement geometries of the employed instrumentation. Particularly for large samples prevailing in the field of building sciences, measurements of the BSDF for directions close to grazing are impacted by shadowing and edge effects. Reliable extrapolation techniques are not available due to the irregularity of the BSDF. Computational simulation is not limited by such constraints at the cost of lower realism. A hybrid approach is therefore proposed. The BSDF of a CFS is measured for incident elevation angles from 0° to 60°. For incident elevation angles from 0° to 85°, the BSDF of the sample is computed. The BSDF acquired by both techniques in the overlapping range of directions between 0° to 60° is compared and reveals good qualitative accordance. The variance of the direct-hemispherical reflection and transmission based on the two techniques is between 3% and 28%. A hybrid data-set is then generated, utilizing measurements where possible and simulations where instrumentation cannot provide reliable data. A data-driven model based on this data-set is implemented in simulation software. This hybrid model is tested by comparison with the geometrical model of the sample. The hybrid approach to BSDF modelling shall support the utilization of BSDF models based on measured data by selectively overcoming the lack of reliable measured or extrapolated data.