Completed PhD Thesis at LIPID

To understand how daylight gives shape and life to architectural spaces, whether existing or imagined, requires quantifying its dynamism and energy. Maintaining these details presents a challenge to simulation and analysis methods which flatten data into discrete images from a point and time or obscure detail behind a virtual integrating sphere. This thesis presents a new method for sampling and evaluating simulated daylight. It is intended as a bridge between image-based and sensor-based methods, one that can produce image-like high accuracy directional distribution data with simulation times much closer to sensor-based methods.
Instead of producing a fixed grid of points, pixels, and sun directions, an iteratively guided sampling approach structured by the discrete wavelet transformation captures the distribution of light incident on a viewpoint with a variable density. By storing the direction vector and effective solid-angle of each sampled ray, this data can be directly evaluated for any luminance based quantity and view direction. Coupled with daylight coefficients, where the contribution from regions of the sky-dome are recorded rather than the single value for a single sky distribution, these methods reduce simulation time at three stages by reducing the number of samples: the renderer needs to solve for, that need to be multiplied by each evaluated sky vector, and that need to be evaluated as potential glare sources. This enables a more complete characterization of daylight and visual comfort across an occupied zone, one that is less biased by proxy measurements, representative point selection, or assumptions about glare-causing pathways.
Using these methods, we can more reliably understand how daylight will respond to proposed interventions, which should be useful for guiding design, regulatory standards, and performance optimization.

Accepted without reservation, the thesis was presented to the public on January 16th, 2023