Suppose we have a simple house like the one shown in Figure 1 and we are in the Boston area. Energy3D supports students to try a design choice, run a simulation, collect the data, analyze the result, and evaluate the solution -- all in real time as is shown in the video in this post. Energy3D's powerful simulation and analysis tools provide instantaneous feedback to students so that their design processes can be guided and informed by the scientific and engineering principles built in the software. Let's use the investigation of south-facing windows described above as an example.
|Figure 2 (Excel graph)|
|Figure 3 (Energy3D graph)|
The trend in Figure 2 suggests that, with the enlargement of windows on the south side, the cooling cost continues to rise while the heating cost levels off. A monthly breakdown in Figure 3 reveals this trend more clearly. As shown by the golden dashed line in Figure 2, the solar heating through the windows increases rapidly when their total area gets enlarged.
|Figure 4 (Energy3D graph)|
|Figure 5 (Energy3D graph)|
She concludes that, with the trees planted to the south of the house, the net energy cost over a year can be decreased to lower than the case of no window at all, providing an acceptable solution that takes care of view, lighting, and landscaping.
The Energy3D graphs in this blog post show that students can keep the results of previous runs (the curve of each run is labeled by a number) and superimpose new data on top of them. As the data view can get quite complex, Energy3D provides options to turn on/off data types and runs. The embedded video shows how those features work for visualizing and analyzing the simulation results.
PS: Some readers may notice that our calculations predict higher AC cost in September than in August or July. This is because when those calculations were done, the house had no window on the east or west side. Adding windows to those sides, the AC cost will peak around July or August -- even when the trees are not present.