What is the Impact of Glazing Area based on Habitable Area on Annual Daylight?

PROJECT INFORMATION

Graphic Name: What is the Impact of Glazing Area based on Habitable Area on Annual Daylight?

Submitted by: Marty Brennan

Firm Name: ZGF

Other contributors or acknowledgements (optional) Heidi Bullinga

What tools did you use to create the graphic?

  • Grasshopper Honeybee

  • Adobe InDesign

  • Python

What kind of graphic is this? 

Primary Inputs: building type, apartment layout, floor to ceiling height, glazing area (8%,10%,12%)

Primary Outputs: UDI 2000 (proxy for direct sunshine), DA 300(daylight distribution), sDA 300/50% 55% (LEED threshold without ASE)

GRAPHIC INFORMATION

What are we looking at?

The graphic is intended to show various annual daylight metrics for residential apartments based on different parameters. There is an overall axon to show urban context and then three axonometric visualizations showing UDI 2000, DA 300 and SDA 300. Parameters like building type, level, window area and ceiling height are shown in the upper left corner.

How did you make the graphic?

A colleague fed me CAD plans of apartment layouts that had building envelope, apartment demising walls, habitable interior walls and non-habitable cores arranged by layers. A grasshopper/python script read these layers along with several parameter inputs mentioned above to build a parametric apartment building floor complete with exterior windows. This was fed thru a Honeybee daylighting analysis. Annual data expressed as a percentage (UDI 200) was grouped into four colored bins to simplify the typical gradient visualization. This grid data was exported as a raster image and layered over a wireframe vector of the geometry in indesign.

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

It was important to visually communicate the impact of building depth, increased ceiling height and glazing area on annual daylight access for a diversity of building types. The graphic had to be compelling, get to the point but be backed with data to drive policy change.

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

The investigation aimed to show increased "livability" for a range of one, two and three bedroom urban apartments. Axonometric drawings efficiently communicate 3d space. in this case, showing the apartment plan layouts, window apertures and daylighting data was really important. Adding the urban context gives an understanding of the impact of orientation and adjacent geometry. It can be difficult to make annual daylighting metrics meaningful to policymakers - borrowing LEED metrics for other building types allowed a "pass/fail" visualization that could be unpacked with the additional visualization.

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

Grouping data in bins simplifies what you are looking at and allows one to talk about highs, lows and in-betweens. Combining a pass/fail visualization with backup data gives the audience something to work from, and dig in deeper if they choose. Using UDI2000 as a good thing instead of bad is unique - here it is celebrated as access to sunlight that every cat in a window appreciates.

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

The various parametric outputs are perfect for Design Explorer. One could more easily navigate parameter impacts and quickly come to the conclusion that window area and ceiling height matter to livability. Showing electric energy savings from daylighting would be a useful addition.

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's the impact of different skylight geometries on daylight autonomy?

PROJECT INFORMATION

Graphic Name: What's the impact of different skylight geometries on daylight autonomy?

Submitted by: Andrew Corney, Product Director

Firm Name: Sefaira

What tools did you use to create the graphic? Sefaira

What kind of graphic is this? Graphic Matrix

GRAPHIC INFORMATION

What are we looking at?

This is a visual showing 4 roof light options for a medical office building project. It compares different strategies - applying simple roof lights, clerestory glazing, both or neither. It aims to use colour to communicate which parts of the floor plate benefit from the different options.

How did you make the graphic?

I took 4 screen captures from 4 analysis runs in the Sefaira plugin and combined them using powerpoint.

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

How well do these daylight options improve the amount of daylight entering the top floor of the building?

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

This graphic was developed for a competition.

One goal was to introduce as much daylight as possible into the building and communicate how the design solution was able to do that. We needed to identify the problem first (deep floor plate with partitions) then a solution that could solve it.

Four different roof light and energy options were being considered:

  • No Skylights - this was the base case and is the top right image. The analysis confirms that there is low daylight on the top floor. Including this image in the graphic helps baseline performance for the reader.
  • Clerestory windows
  • Flat Skylights
  • Both Clerestory windows and flat skylights

The graphic helped to identify how well the options compared in terms of achieving the goal of more daylight. It did this through:

  • Effective side-by-side comparison of results
  • Glanceable detail from a distance with extra detail provided close up

Looking at the graphic, it becomes clear that for most of the floor plate, the skylights were far more effective at bringing in natural light than the clerestory windows. The diagram helped the team hone in on a solution that combined skylights and a subset of clerestory windows to achieve this part of the project goal. It was presented in tandem with energy results.

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

This graphic tool very little time to generate. It's a standard Sefaira output that I was able to make in about 10 minutes with the SketchUp model as a starting Point. It's great because for something that's very quick to generate, it clearly quantifies visually the differences between the options for a client who is not interested in numbers.

The graphic primarily uses colour to communicate the difference between options. This makes the 4 options glance-able for a lay person or for a person who is not close up to the diagram (eg in a presentation).

There is a scale provided on the image as well and importantly all images are shown at the same lighting scale to ensure a fair comparison between each.

I believe this graphic is successful because it does most of the important work for the reader:

  • Consistent scale and viewpoint
  • Intuitive use of colour to communicate performance
  • Key focus on a single piece of information (There is not a whole lot of other information to confuse the reader.)
  • Compares options fairly.

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

The graphic is great as is and we included more in the original study to communicate the impact on energy use as well.  Showing the analysis in tandem with external views of the model is always a good way to reinforce the bigger story and overall picture for the 4 different solutions.

What is the impact of different blind control strategies on illuminance over the course of an average day?

PROJECT INFORMATION

Graphic Name: What is the impact of different blind control strategies on illuminance over the course of an average day?

Submitted by: Jacob Dunn

Firm Name: ZGF

Other contributors or acknowledgements (optional) Chris Chatto - ZGF

What tools did you use to create the graphic? Grasshopper / Indesign

What kind of graphic is this? Graphic Matrix

Primary Inputs: building geometry, glazing specifications, blind controls (automated vs. manual)

Primary Outputs: illuminance (lux)

GRAPHIC INFORMATION

What are we looking at?

This compilation graphic compares the amount of illuminance in a single space for a typical day in October for two different automated shading strategies: external blinds and internal roller shades.  A baseline case is also shown without any type of shading for the windows.  Each illuminance map is reported for a single hour throughout the day, while a 3d axon image at the top of the graphic shows when the automated shading is deployed for each of the 3 windows.  The labels next to each illuminance map describe the run name and displays the % of the space above the 500 lux threshold for the workshop/maker space being analyzed.  Weather symbols at the bottom of the graphic provide insight into the sky condition during that hour, which relates to whether or not the external radiation trigger (>50 w/m2) on each of the windows is activated.  Larger text labels at the top of the page cover at a glance the key insights of the analysis, i.e. that the blinds option provides 2-3 times more daylight that the diffuse shade due to their light redirection and reflection characteristics.

How did you make the graphic?

Leveraging the graphic output capability of grasshopper was crucial in the creation of this graphic.  Honeybee was used to run the single-point-in-time analyses, while Ladybug components were used for grid visualization.  But instead of looking at a single illuminance map for a single run case, the components allow you to input a list of .ill files (the output file of the illuminance simulation) within a single folder, while defining ‘move’ vectors within Grasshopper to place each subsequent grid next to the one before it.  This group of components was then copied for each of the other cases and the result is the matrix that you see in the graphic.  Custom labeling can be included to name the runs and report different text labels, such as the % threshold values.  This process saved a lot of time when taking the imagery into Indesign to add the other graphic elements.  Typically, each grid would need to be placed individually within Indesign, but Grasshopper’s ability to automate the visualization and placement of the 30 grids added significant efficiency to the visualization process.  

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

Is there a difference in the amount of daylight admitted between automated roller shades and automated exterior blinds for a typical day in October?  If so, what is the magnitude of this difference and is it worth the extra cost? 

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

The single-space analysis targeted a heavily glazed workshop/maker-space zone where some form of automated shading was needed for glare control, and potentially for solar load control. The two options on the table from the design team were an automated external blind case, and a less expensive automated interior roller shade case. To truly understand the difference in performance, two things needed to be modeled accurately: 1) the automation control based on an external radiation trigger, and 2) the rotation of the blind slats, since this would have an impact on how the case redirected reflected sunlight. Blind slat rotation is typically simplified in most simulation programs, so a custom Grasshopper script was created to rotate the blinds to achieve full solar occlusion based on the position of the sun in the sky. This ensured the blinds would perform as they would in the real world, and thus capture accurately any sunlight redirection in comparison to the diffuse characteristics of the roller shade.

We knew that the blinds would perform better than the roller shades, but we didn’t realize the magnitude would be 2-3 times more, depending on the hour and blind occlusion rates. Additionally, we learned that given the high amount of glazing, the roller shades still allowed adequate ambient lighting of the space (500 lux), but the blinds case could potentially provide enough illuminance to meet ambient and task lighting requirements of a workshop space (1000 lux).   

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

The most successful aspect of this graphic lies in its different depths of engagement.  At a glance, it tells a viewer key takeaways from the large text labels at the top of the graphic, i.e. that the blinds perform 2-3 times better than the roller shades through large format text at the top of the page.  Alternatively, you can compare each hours’ % of space above 500 lux label for a more granular comparison.  You can even compare spatial qualities of the different strategies from the illuminance maps if necessary.  Sky condition or blind state is also available as quality assurance checks to help internalize the numbers.  A lot of context is needed to understand the performative difference between the two cases and the baseline.  The ability to present this information in a clear, concise, and compelling way can generally only be handled through a combination of qualitative and quantitative graphics, combined into a single composition.    

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

I would have increased the size of the %>500 lux threshold values, they are very small in the overall graphic.  Additionally, the axon graphic is over-simplified, meaning it shows the space as a single box with glazing from top to bottom.  The actual space has an overhang at about 10’, and the glazing below it does not have any automated shading.  Therefore, some of the fully occluded hours in the afternoon still have some red squares (direct sun) inboard of the southwest façade, which is confusing given the axon.  I should have split up the glazing in the diagram, but ran out of time.  Finally, given the presence of the large legend in the lower right hand corner, the individual legends on each illuminance map are not redundant and could have been removed to clean up the graphic.

What's the impact of different glazing patterns on daylight illuminance?

PROJECT INFORMATION

Graphic Name:  What's the impact of different glazing patterns on daylight illuminance?

Submitted by: Jacob Dunn

Firm Name: Eskew+Dumez+Ripple

Other contributors or acknowledgements (optional): Z Smith - Eskew+Dumez+Ripple

What tools did you use to create the graphic? Autodesk Insight 360 (Daylight) / Indesign

What kind of graphic is this? Graphic Table

Primary Inputs: building geometry, material reflectances, glazing specifications

Primary Outputs: % area above illuminance threshold, % window to wall area ratio

GRAPHIC INFORMATION

What are we looking at?

This is a combination graphic that shows nine design iterations, each with two pieces of information simultaneously: the percentage of the space that is daylit and the amount/distribution of glazing for each iteration.  The number in the upper right hand corner of each option shows the % daylit value for the project at a single point in time.  The illumination criteria, time, and sky condition are listed at the top of the graphic (>30 footcandles, 9/21 9am, sunny sky).  The second percentage number represents the window to wall area ratio for that option (E, W, and N façade).  Additionally, a simplified diagram of the façade window pattern is shown, unfolded, for those three orientations. Smaller thumbnails are shown for all of the images, while the recommended option is enlarged at the top.  

How did you make the graphic?

I exported the three Revit model elevations for a couple of the options and drew over them in Indesign to get the window patterns.  Once I had all of the different window shapes I could just draw and reconfigure the diagrams without having to export from Revit.  Once that was completed, I simply added the output daylight values from the Autodesk Lighting Analysis for Revit simulations on the right hand side of each diagram.

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

What was the highest amount of daylight we could achieve with the lowest window to wall area ratio that still met all of the functional and aesthetic requirements of the project.

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

The project goal was to design a net zero single-space classroom building that was 100% daylit.  The analysis team was brought into the project after a form and glazing pattern had been decided, which was close to 80% WWR on the north, west, and east sides of the building.  Given the space was fairly small and thin (roughly 25' by 50'), we knew that daylighting shouldn't be an issue.  However, since we were targeting net zero, we wanted to make sure we reduced the WWR as much as possible to lower impact on heating and cooling, while still saving as much lighting energy as possible with a fully daylit environment.  Between the different options, the lowest WWR (17%) produced a 75% daylit space, while baseline WWR (50%, whole building) produced a 99% daylit space.  Most importantly, the analysis showed that WWR between as low as 23%, with the right distribution, could achieve a 99% daylt space. The team ended up reducing the amount of windows to 35% based on this analysis, which was deemed the right balance between façade patterning and performance. This value was then used as one of multiple efficiency measures in a comprehensive net zero modeling analysis.

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

I think the main benefit of this type of combination graphic is that it simultaneously allows viewers to understand both the design and performance impact between the different options.  This is especially helpful when working internally with design teams who need to understand the formal implications of the analysis, which is where spatially-integrated graphics can go along way.  

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

Despite how simplicity of the diagrammatic representations of the façade and the two metrics, the graphic is still quite dense.  It still takes a lot of focus to look between the different options and compare them to eachother.  Locating each one on a scatter plot, color coding the % values in a heat map, or simply ranking them by daylight performance would have made it more apparent which options performed best.