Ethan Sun

Link to Design Journal
Journal Entry For
Module 6 - Evaluate Your Alternatives
Created By
Related to 120C/220C Students - Spring 2022 (1) (Related to Design Journal Entries | Winter 2022 (Linked Student))

Number of Units: 2

For this assignment, I used the parametric tower form “Hourglass Tower” that I created for module 5. The two input parameters that can be varied are the lower atrium height and the top side length, which are the dimensions in red as shown below. The upper atrium height is fixed to be 100ft above the lower atrium height. The side cut length is fixed at 70ft. The tower height is fixed at 700ft.


Metric 1: Perimeter Area Ratio

I wanted the first metric to evaluate the relative amount of area on each floor that has access to the windows of the building. This would be a good indicator of how suitable the floor can be used for residential uses, because unlike office space, residential units should not have too much interior space that has no natural light.


A simple way to measure this is to get the ratio of perimeter to area of each floor. To achieve this, I modified the MassFloors node to make it also return the perimeter of the Mass Floor. Then, for every floor I divide the perimeter by the area, such that a higher value means greater suitability for residential uses. Then the average value of all the floors is computed and multiplied by 100. For the Hourglass Tower, this metric has a value of around 2.9.

The above logic is encapsulated in a custom node called PerimeterAreaRatio.

Metric 2: Ground Insolation Indicator

As many examples in class investigated the effects on insolation of the building by surrounding buildings as well as the building itself, I wanted to look at the shading effects that the building has on the environment.


To measure this, I had to create another very thin box mass at the ground level to act as the “ground surface”. As shown in the screenshot above, the surface that I created is 2000ft by 2000ft. The solar setting is a One Year Solar Study at the designated location of the tower.


The custom node GroundInsolationValue that I created for this metric is shown above. The first screenshot shows the selection of the tower surfaces as the shading surfaces and the ground surface as the analysis surface, which is generally the same as the examples in class.

The second screenshot shows the logic for evaluating the insolation values on the ground surface. The user is able to input a desired spacing to achieve a balance between computation efficiency and accuracy.

When SolarAnalysis.Analysis outputs the cumulative insolation values for each point, I added in logic such that the value is adjusted by the distance from the computation point to the tower. In my idealization, a point further away from the tower would likely be less “annoyed” by the shading of the tower, so a higher weight is given to the points closer to the tower. Currently, a linear relationship is set between the distance and the weight (the closest points have a weight of 10 while the furthest points have a weight of 2), but this relationship can be further improved.

Then, the average insolation value of all the points is returned. For the Hourglass Tower, this metric has a value of around 2190. A higher value means less negative impact on the insolation of the surrounding environment.


Finally, I incorporated the two new custom evaluation nodes into my testing node from module 5.

Shown below is the testing node, which outputs the three default metrics and the two custom ones that I’ve created.


The final Dynamo graph that calls the testing node is shown below.



The list of values resulting from the new testing node is shown below. As expected, as the Lower Atrium Height and the Top Side Length increases, the Ground Insolation Indicator value decreases because the surfaces at the upper portion of the tower gets larger, which creates more shading.

The Perimeter Area Ratio also decreases as the Lower Atrium Height increases, but the optimal value of Top Side Length is actually 150ft (for the cases tested), which is an intermediate value.

Thus, this analysis shows how powerful such custom evaluation nodes can be, and the insight they create.