For this module, I knew that cooling would be a big issue due to the purpose of my space. Creating a rock climbing center, or any fitness center for that matter really needs to take people into consideration when sizing the HVAC system. Because of the purpose of the space, there will be a lot of radiation coming from people and adding to the overall load. When I was first adjusting the space types, I noticed the large number of people that the space was accounting for. Instead of changing it, I wanted to see how these large numbers would affect the overall load. Resultingly, the people accounted for over 81% of internal gains and overall contributed the most to the cooling load. Additionally, 82% of the negative heating load was from the zone ventilation. The windows and wall envelope features also contributed to the negative load, accounting for roughly 17%.

The general characteristics of my construction of my building can be seen in the screenshot above. Below, the analytical spaces can be shown that were run through the energy simulation.

My building had a total of 16 spaces, each assigned to a specific space type based on occupancy, activity, and other metrics.

For my overall heating strategy, I knew that the large amount of windows would account for a lot f direct gain and heat loss. When the energy simulation was run, it showed that the conduction through glass envelope and through the walls were large contributors. However, the largest contributor was the zone ventilation. The overall heating load after occupancy adjustments was found to be roughly -436890 BTU/hr. While this is a high number, the building itself is very large. Additionally, exercise spaces need a lot of proper ventilation and therefore cannot be avoided in the overall load. As a result, I was satisfied with my heating load because my strategy was to have natural light in the climbing spaces, have proper ventilation in the zones, and to allow for some direct gain through the envelope. If anything, I would adjust/increase the wall R-values to keep some of the heat out of the spaces while still allowing VLT to continue.

Going into the design of the building, I knew that the cooling load was going to be quite high. The location of the building gets a lot of direct sunlight and combined with rigorous physical activity in an enclosed space, it resulted in a large amount of cooling required to maintain the given setpoints. I knew that the overall cooling load that resulted from the energy simulation was going to be slightly off for several reasons. First, the large spaces of the buildings can account for many people, but are very dependent on the intended uses of the space. Second, a huge portion of the square footage of the largest spaces in my building like the climbing area and both bouldering areas, are actually unwalkable for occupants. This is because the structural framing for the climbing walls takes up a large amount of that given space. The simulation did not account for this and as a result, overestimated the amount of people that would be in those spaces. To combat this issue, I then adjusted the occupancy levels for each space to better represent what I thought the spaces could hold well, and as a result brought the cooling load down by 20%.

While the cooling load still remains quite high, the overall building location and purpose ties into this high load result. Therefore, it may be high but it is at least somewhat accurate as a representation.

The graphics above depict the preliminary loads before occupancy levels were adjusted. The pdf includes the full results and breakdowns of these loads.

The graphics above depict the loads after occupancy levels were adjusted. The pdf includes the full results and breakdowns of these loads.

The main challenge that I saw coming very early on was finding the right amount of occupancy level for the space. A huge difficultly is that the largest area in the building is also largely made up of structural beams and columns for the climbing walls. This is the same with the bouldering areas as well. Because of this, I knew that the numbers for these space occupancies were going to be too high.

Another main challenge that I came across was finding the CFM for each space. When trying to implement it into my schedules, the option would be missing. As a result, I designed the amount of CFM per space based on the square footage as a rough estimate.

Lastly, I did notice that in my analytical surfaces, there were two holes identified. This would have had a major impact in my cooling and heat loads shown in the energy simulations above. However, I had trouble identifying their exact locations, and will be working to find them. Once I find them, I will be able to increase the offset values to ensure proper closure for each of my spaces. The holes can be identified in the screenshot below as the “air” opening type.