(See Figure 5.) Within the fabricated models on site, he used an Arduino board and three LDRs to measure the ambient light level on site at different times to determine the best orientation, material properties, and angle of louver openings.

Finally, the student fabricated a number of pavilion models with different scales, colors (to simulate materials), and louver openings. (See Figure 4.) Given that the models set up in Rhino can respond to the ambient light level read by the LDR, the solution space for the optimal design can be narrowed to those that match the real-time ambient light data. Third, the student calibrated the reading of the Arduino LDR with the lux reader. This task was also performed to calibrate the Arduino light sensor. This particular part of the study was essential, given that there could be cultural differences in how people respond to light levels in doing specific tasks as well as regulatory differences in lighting requirements. (See Figure 3.) After observing and analyzing these data, the student concluded that on average, Melbournians would find 580– 600 lux a comfortable light level for reading. Second, the student visited a number of popular reading locations around Melbourne carrying the ambient light informer, which consisted of an Arduino light sensor and a lux reader to measure the ambient light level in these spaces.

(See Figure 2.) The parametric variations in the Rhino model are scripted using RhinoScript. Changes on the ambient light level trigger fluctuations on LDR readings, which subsequently trigger updates on the parametric models set in Rhino. First, the student experimented with Rhino 3D models that are responsive to real- time ambient light data input from Arduino Light-Dependent-Resistors (LDR). Although there can be a number of parameters in designing a comfortable reading space, such as noise, temperature, wind, and light, given the short time frame of the project, light was chosen as a design constraint of the reading space for proof of concept.

inform the design, instead of using the traditional approach of using light simulation and a solar path diagram to predict the light level at different times of day, experiments were performed with a lux reader and ambient light informer, the DIY urban probe, made of a number of light sensors connected to an Arduino (See Figure 2) in the preliminary design processes.

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