1. Documentation (ReadMe)

Catchy Name:

My tool package is called “Net-Stadion” since this is a simple generative design tool that helps developers with net-zero energy planning with solar panels and gives them a general idea of total construction cost.

Brief Overview:

If you are a developer or designer working on a stadium project who wants to have a basic glimpse of the rough cost and solar energy generation before getting deep into it, please do not hesitate to try Net-Stadion design tool first!

Net-Stadion will give you a rough estimation of how much money you can save if you install the solar system and try to give you the best solution according to your needs.

With the tradeoff relations inside the tool, users can have an idea if they can really pay back after installing solar panels since the initial and maintenance costs of the solar system are not cheap.

In this way, Net-Stadium can help save your money and time wasted on other suboptimal solutions and give you a general idea of how to design for the next step.

Teaser Image:

For the inputs, you don’t need to do extra calculations or data transformation. Just put the ideal Floor Area, Height, Expected Audience Capacity, and Expected PV System Life depending on the brands/ types of solar panels that developers will choose.

Net-Stadion will give you outputs including Base Floor Area, Rooftop Solar Panels Area, Solar Potential, Cost Saving by Using Solar Energy, and a Total Cost Estimation.

Here is a teaser image for guidance:

On the right hand side we can see the detailed variables and outputs values of each design case. The first case is the optimized situation. In addition, the grey line on the chart below shows the detailed situations on each parameter.

Video Demo:

Attached is the video demo of how to use the Net-Stadion package tool.

Link: https://clipchamp.com/watch/ObDwWVK0b4R

2. Detailed Nodes Explanation in Dynamo

Here is an overview of Dynamo nodes, where the long green column on left is input list and orange column on right is output list. The light green part of the nodes is some value settings and the blue one is the basic structure of the stadium. Then we move on to the purple nodes, where we calculate the evaluations we need for outputs. The gray parts are solar analysis part and related evaluations.

These are inputs:

Firstly, according to the base floor area and the height of the stadium data that users provided, we can write the nodes/flows that can do generative design for different shapes of the stadium.

The geometry of the stadium is basically formed of four circles, one fore bottom, one on the medium, one on the top, and one circle to serve as a opening hole in the shell structure.

Through linking different circles, we can get the surfaces of wall, rooftop, and the whole building surfaces. We can use these data for floor area and solar panel area calculations.

Then in the middle part of the nodes, I tried to write nodes regenerating wall panels and solar panels on wall and roof separately. To better distinguish the difference, here I adopt “Rect_Seamless Panel-4 pt” for wall panels and “Rect_Panel with Resizable Opening: Glazing” for rootop.

I also want to add a logic that can arrange the seats of audience and using risers to model the seats inside the stadium. I use “Riser” family type to simulate the seat and I set the angle as an input so users can adjust the seats surrounding according to their needs.

Then, the rooftop part of the stadium will be used for solar panel installation. So the rooftop area will be calculated and the approximate number of panels used will be estimated and counted into the total costs (because solar panels are still quite expensive so far).

The solar energy generated will be estimated based on solar potential and solar directness. Then the energy saving will be transformed into cost saving based on the prices in real electricity markets.

These are our outputs:

Finally we can save this and open it in generative study.

By controlling inputs, we can settle down the fixed known information and see how the unknown inputs can be changed to make an optimization option.

For example, one input example can be:

The primary optimization results look like this: