What is the impact of glazing on energy and daylighting?

PROJECT INFORMATION

Graphic Name: What's the impact of glazing specifications on peak loads and energy consumption? 

Submitted by: RJ Hartman

Firm Name: Clark Nexsen

Other contributors or acknowledgements (optional) Ryan Johnson, Architect. Ryan was the primary contributor/modeler of the graphic

What tools did you use to create the graphic?

  • Grasshopper Diva

  • Grasshopper Human UI

What kind of graphic is this? 

Primary Inputs: Room massing, interior furniture, exterior shading, window geometry, and SHGC.

Primary Outputs: sDA, ASE, Lighting Energy, Peak Energy, and Chilled Beam Length

GRAPHIC INFORMATION

What are we looking at?

The graphic shows a dashboard like interface that shows a plan of the space with the sDA/ASE. Below the plan is an elevation of the exterior wall showing the configuration of the glazing and the amount of solar energy hitting the glass. Alongside the plan/elevation, there are several large metrics highlighting the impact of the glazing on the daylight and energy. There are indicators to the right of each metric that shows what is established as an industry standard for acceptable performance, or project specific goals for what is considered good/acceptable/poor performance.

How did you make the graphic?

Elements were first modeled as individual spaces/rooms in Rhino then brought into Grasshopper and analyzed using Diva energy and daylight analysis. Since the Diva output data is saved locally to the users' workstation, the Grasshopper script was set up to be able to run several analyses at once. After the analysis, the Diva data could be read off the workstation by Diva and processed with Grasshopper into the desired metrics. The graphic interface of Human UI, a plugin for Grasshopper, allows users to choose 2 specific options which are read by Grasshopper, processed, and graphically displayed via Human UI..

What specific investigation questions led to the production of this graphic?

  • How do we balance energy and daylighting via manipulating the amount of glass and shading?

  • How much glass is too much glass?

  • What "Window to Wall Ratio" do we want?

  • Does this external shade reduce glare?

  • What glass type should we choose?

  • What is the impact of frit on the glazing performance?

  • What is good daylighting?

  • What is bad glare?

  • What is good lighting energy?

  • What is our target energy use for the space?

  • How many chilled beams are required in this space?

How does this graphic fit into the larger design investigations and what did you learn from producing the graphic?

The architects were trying to design the façade of the building and wanted to know how much glass they could have in a space that provides a well daylit space while managing glare and solar gain. Using traditional outputs from daylighting and energy model tools, the team was having difficulty quickly determining what it meant to have good daylighting and what it meant to have good energy use. This is led us asking “How do we present our data/findings so that different team members (architects, electrical engineers, and mechanical engineers) can understand this data and can make design decisions from it as well.” The way that the Grasshopper + Human UI script worked, it saved an image to the server of each graphic automatically once it was displayed. These graphics were used in meetings to pull up multiple images of options side by side on the same screen like cards. This allowed for a quick literal side by side comparison of options. Ultimately these goals and being able to compare options helped decisions be made about shading, glazing configuration, and glass types for the final design of the building.

What was successful and/or unique about the graphic in how it communicates information?

Our graphic was successful in that we were able to convey a significant amount of information to our team, while maintaining a clean and easy to follow organization. It eliminated a lot of confusion and questions by the team and sped up the decision making process of the designers.

What would you have done differently with the graphic if you had more time/fee?

The plan with our graphic is to continuously update based on the needs of whatever project we are using it on. With this being said, we would have liked to have a clearer naming directory for the various options we were analyzing. In our submitted graphic, the description of the option is all the information we have about the option, which resulted in a cryptic naming convention not easily understood by the team. We would have preferred to include a description of the option within the graphic but could not come up with a good solution to this problem since all the data is being read from Diva. We potentially could try to read and write data to another place, (eg: excel) to be able to use that as a place to store data that is not stored in the Diva results. Examples of data that could be stored are a written description of the option, information about shading, orientation data, or anything that the team/project deems useful for the project. We also think that the bar chart at the bottom could be cleaner, and more useful with labeling/colors/etc but used the stock Human UI chart component.

What is the impact of automated shading systems on energy efficiency, availability of natural light and view, visual comfort, and occupants’ productivity?

PROJECT INFORMATION

Graphic Name: What is the impact of automated shading systems on energy efficiency, availability of natural light and view, visual comfort, and occupants’ productivity?

Submitted by: Amir Nezamdoost

Firm Name: Energy Studies in Buildings Laboratory - University of Oregon

Other contributors or acknowledgements (optional) 

What tools did you use to create the graphic?

  • Adobe Photoshop

  • R

  • OpenStudio + EnergyPlus

  • Radiance + Custom Scripting

What kind of graphic is this? 

Primary Inputs: (1) Blind schedule, including blind position and blind movement; (2) Time; (3) Sun penetration depth and vertical interior illuminance behind the window.

Primary Outputs: Hourly and annual percent of blind occlusion (per window group), hourly and annual number of blind movement (per window group), Hourly daylighting performance, Lighting schedule, hourly electric lighting analysis

GRAPHIC INFORMATION

What are we looking at?

This set of images shows the individual blinds conditions of 691 window groups for 9:00am, 12:00pm, and 3:00pm during January, March, May, July, September, and November. The circular wind-rose summarizes the blinds occlusion for each facade while the 3D shows the three-dimensional plot of all windows and their condition for that point in time. The camera angle rotates around the window surfaces depending on the time of year to give a sense of depth while still exposing all window surfaces. The background color palette differentiates the time of the year being represented in each column set.

How did you make the graphic?

The individual point-in-time 3D plot were visualized using the R statistical analysis language by plotting the window geometry of the daylighting model. The results of the blinds operation simulation were also processed with R and applied to each respective window surface until the whole building's blinds positions are plotted. And finally, the specific sun direction is calculated, plotted, and tethered to a central north arrow for orientation. Separately, the wind rose blinds occlusion graphic is also produced in R by plotting four wedges and controlling their lateral fill based on the percent of window area that is being occluded by blinds on each of the four facades. These two pieces are then put together using either Adobe Photoshop or Adobe AfterEffects, depending on the desired output, along with the additional information like time, date, and percent occlusion values.

What specific investigation questions led to the production of this graphic?

1- How does the automated shading system work on different facades of the case study project?

2- What is the frequency of blind movement in the automated shading system?

3- What would be the position of automated blinds/shades in specific points-in time?

4- How much electric light consumption can be saved by using automated shading systems?

5- How much time can be saved by not operating blinds manually?

6- How much the balanced position of automated blinds can impact on aesthetics of building in comparison to haphazard position of manual blinds?

How does this graphic fit into the larger design investigations and what did you learn from producing the graphic?

This investigation allowed us to make relative performance comparisons among a variety of blinds operation algorithms to further understand the accuracy and reliability of specific simulation methods of complicated human behavior. This ultimately resulted in some of the more interesting visualizations showing the "haphazard" manual blinds algorithms contrasted against the uniform automated blinds algorithms. And on that note, we were also able to develop some interesting ways to process and visualize daylighting, energy, and blinds operation data that we can also utilize in future investigations. Lessons learned: - Different ways of making simulation data spatial and easier to understand and communicate. - Better understand blinds as they change over time, and the algorithms used to simulate their behavior as part of the design process.

What was successful and/or unique about the graphic in how it communicates information?

For blinds visualization we have annual results for 691 windows of the building, and needed a way to better understand and troubleshoot this data. R's plotting functions allowed us to visualize individual window surfaces and their blinds positions in 3D space. This method proved as effective and efficient throughout the simulation phase. Consequently, it also became an effective way to communicate the data and our findings to the clients by showing it in a similar manner as it is observed on actual building facades.

What would you have done differently with the graphic if you had more time/fee?

If we had more time, we definitely added more detailed geometry and landscape to this graphic. We also would include average percent of blind occlusion to each of the analyses.

What is the impact of different top lighting strategies / screening strategies on daylight?

PROJECT INFORMATION

Graphic Name: What is the impact of different top lighting strategies / screening strategies on daylight?

Submitted by: Morgan Maiolie

Firm Name: NA

Other contributors or acknowledgements (optional) Integrated Design Lab

What tools did you use to create the graphic?

  • Adobe Illustrator

  • Adobe InDesign

  • Physical model + heliodon

What kind of graphic is this?

Primary Inputs: Material surface reflectance, screen perforations, skylight translucency

Primary Outputs: daylight factor across space

GRAPHIC INFORMATION

What are we looking at?

The graphic shows how daylight factor across a room is affected by different top lighting strategies with both clear and perforated screened glass along one wall.

How did you make the graphic?

I put daylight sensors in a physical model to detect changes in the skylight / screens. The data from the sensors was input into an excel sheet and used to generate charts that show daylight changes across the interior of the space. I used illustrator to edit the line work, colors, and add icons to denote each iteration of the model's analysis.

What specific investigation questions led to the production of this graphic?

What is the difference in illumination between a center aligned square skylight and a strip? How do different types of screen perforations affect each of these skylight geometries?

How does this graphic fit into the larger design investigations and what did you learn from producing the graphic?

This graphic was paired with photographs of the model to understand both performance and aesthetics of the screen / skylight changes. Together, they taught me that a strip skylight created more even lighting and larger perforations in a screen could provide more dynamic light bouncing around the space and illuminating the center more brightly. The copper facade studies showed that daylight was decreased considerably, but was spread more evenly.

What was successful and/or unique about the graphic in how it communicates information?

Keying colors and icons proved an effective way to quickly communicate different design decisions + performance implications.

What would you have done differently with the graphic if you had more time/fee?

Made the Y axis on the two graphs match! Can't believe I didn't do that.

What is the impact of context shadows on community park's solar access and new landscape design?

PROJECT INFORMATION

Graphic Name: What is the impact of context shadows on community park's solar access and new landscape design?

Submitted by: Amam Singhvi

Firm Name: AECOM

Other contributors or acknowledgements (optional) Xiaofei Shen, Jason Vollen

What tools did you use to create the graphic?

  • Adobe Illustrator

  • Adobe Photoshop

  • Grasshopper Image Processing

  • Revit

What kind of graphic is this?

Primary Inputs: Location, Solar Path, Context 3D massing, Site Boundary, Time Step

Primary Outputs: Shadows cast during design days, separated into morning(AM) and evening(PM). Percentage area of the community park in shade (Through image analysis in grasshopper).

GRAPHIC INFORMATION

What are we looking at?

The graphic is showing shadows cast by surrounding context buildings on September 21 from sunrise to sunset, in 1 hour time step. The aim was to study shadow patterns in comparison to the two different occupancy patterns of the site (i.e. AM and PM). To this end, the shadows are color-coded into red for sunrise till noon and blue for noon till sunset. This is one of four boards showing shadow analysis through Spring, Summer, Fall and Winter.The aim was to represent these as simply as possible while creating an engaging visual for the non-design community as well. Interestingly, the shadow overlap colors thus generated were eventually used in the overall graphics theme of the final package.

How did you make the graphic?

After the location and orientation were cross-checked, shadows for each hour were exported as image files. The images were separated based on time and color-coded using Grasshopper, Photoshop and Illustrator which generated darker color for each overlap. To get accurate area of the site boundary which is not shaded, each image file is passed through a Grasshopper image processing script. The hourly values were used in the detailed version whereas for the purpose of this graphic the output was averaged for AM and PM.

What specific investigation questions led to the production of this graphic?

What is the impact of context shadows on site's solar access during different seasons? How can the regions in shade as well as their overlap frequency (darker colors) inform the landscape design strategy, specifically the planting pattern for different seasons? Where can we place mechanical equipment or other site equipment without reducing a solar access?

How does this graphic fit into the larger design investigations and what did you learn from producing the graphic?

The graphic was originally intended to study shadow patterns in comparison to the two dominant occupancy patterns and usage of the site i.e. before noon and after 2 pm. However, many more design questions were answered using this study in the larger project investigations such as placement of various programmatic elements such as parking so as to cause minimum disruption to solar access, placement of buildings on site and their heights, the landscape planting strategy based on High, Medium and Low shaded areas during each season, potential future shadow impacts etc. The most important thing I learnt was that sometimes minimum interventions can transform something basic into a something more intuitive and informative to a larger audience. Analytical graphics does not have to be complicated.

What was successful and/or unique about the graphic in how it communicates information?

The most successful aspect of this graphic is that people want to look at it! This automatically leads to interesting questions and useful insights from a room full of people. On one hand it is just a shadow study while on the other hand, there are a lot of useful information one can derive from it. The remaining three images for March, June and December combined with this one together gives a holistic picture of solar access and shade on the site.

What would you have done differently with the graphic if you had more time/fee?

I would have used the color coding and graphic's layout into an interactive dashboard and created animation of the shadows as per user inputs of time of day and season. The four graphics of different seasons played one after the sufficed in this case.