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 climate and ubication on the energy balance of an initial model versus an optimized model of customer service agencies?

PROJECT INFORMATION

Graphic Name: What is the impact of climate and ubication on the energy balance of an initial model versus an optimized model of customer service agencies?

Submitted by: Luis Godoy

Firm Name: NA

Other contributors or acknowledgements (optional) Sebastian Romero, Andrea Lobato-Cordero, Geovanna Villacreses and Cristian Espinosa. Instituto Nacional de Eficiencia Energética y Energías Renovables.

What tools did you use to create the graphic?

  • Design Builder

  • Climate Consultant

  • Excel Adobe

  • Photoshop

  • Meteonorm

  • ArcGis

  • Power Point

What kind of graphic is this? 

Primary Inputs: Average per hour of meteorological information, geographical ubication, initial and optimized architectural models.

Primary Outputs: Overall and hourly energy balance (gains-losses) for an extreme climate conditions day.

GRAPHIC INFORMATION

What are we looking at?

This graphic shows how is the energy balance (loses and gains) established, for buildings that will work as customer service agencies, to be built in affected areas by the 2016 earthquake in Manabi, Ecuador. Firstly, a weather condition study was carried out by using data of a typical meteorological year of the cities were the building will be located (Pedernales, Manta, Flavio Alfaro, Portoviejo and Calceta). From this information, the most probable hottest day of all cities was selected (April 11th). An initial model (IM), offered by the contractor, was compared with an optimized model (OM) that uses passive strategies for design. The energy performance, from the simulation, of the building with its IM and OM was analyzed, always considering the building functioning requirement such as: space, distribution, usage, etc. The set strategies must be replicable and show energy savings in all the cities. The total energy balance showed in the donut charts indicates the characteristic thermal gains and loses throughout the building materials according to their type. Even though Manabi corresponds to the ASHRAE climatic zone known as “humid very hot” each one of the agencies shows a particular behavior in which the inner temperature increased at afternoon hours due to the typical weather conditions. As first result, the solar gains on windows and roofs are representative, this is why the usage of efficient windows and reflective paint on roof and walls, is proposed. The second result of the bar plot indicate he hourly behavior of loses-gains. The internal gains on external windows on afternoon hours can be relieved by accurately orientated vegetation. Besides, it is possible to note that, for loses, these are grater on ground floor, which is a cooling advantage. Finally, it is possible to conclude that in most cases, energy savings are found after passive strategies of design were implemented, on all four cities. Portoviejo city was a special case because heat loses were diminished.

How did you make the graphic?

Firstly, with the coordinates of the five cities were the agencies will be place, through Meteonorm, the .epw (energy plus weather) files were generated, for the typical meteorological years (TPM) that will be used for weather study and energy simulation. With this data, the agencies were ubicated on ArcGis to confirm whether the cities are located on the same climate zone. Simultaneously, the weather study was carried out with Climate Consultant for each city. The day that showed the greater gains on the building was selected. The input data entered on Design Builder included: climate conditions, agencies characteristics IM and OM proposed. With the energy simulation, the hourly results of gains and loses were obtained, in .csv format. The analysis and calculations obtained were plotted as donut and bar charts by using Excel. The showed graphs were edited on Adobe Photoshop for a better display. After that, the final display was diagramed with the analisis content.

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

- Which are the main energy gain and loss, as product of climate conditions, for a building model of customer service that will be built on affected areas by the earthquake in Ecuador? - How great is the energy saving that can be obtained by optimizing the initial model with the implementation of passive strategies for design, on the hottest day of the year? - How is the energy balance established and what aspects must be considered to optmize the IM?

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

In Ecuador and other countries, it is proposed the construction of buildings with similar facilities and services to satisfy the customer demands. Besides, the buildings must be replicable in different locations. These buildings can be constituted by companies such as: banks, restaurant franchises, payment agencies, customer services, etc. Nevertheless, the influence of the energy and thermal behavior of the building is not considered. This is why it is important that prior the construction, efficient passive strategies on the design are analyzed and proposed, in order to obtain energy savings. The lessons learned were: though a climatic zone can be normalized (for instance, humid very hot), the characteristic weather of the area can affect differently each zone even if they are near to each other. The energy balance is a simple and useful analysis that allows to detect and prioritize sensitive zones of the building to energy gain or lose.

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

From the geographical location, the climate conditions and architectural characteristics of the building, it is possible to obtain the hourly and total energy balance, to detect energy saving opportunities in each city. In this case, it was observed that windows and roof are sensitive zones for heat gain, for this reason, their intervention must be prioritized to obtain greater energy savings. By using window and wall protection and specific paints (that do not increase considerably the final budget), effective energy savings can be obtained in all the agencies in the studied cities.

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

Firstly, we will try to find a correlation between the geographical location of every building and the energy consumption, considering the meters above sea level in which the building is located. Also, we would like to try or to combine other passive strategies for instance: external ventilation and/or ventilation on floor over terrain. Furthermore, we will monitor the energy consumptions of the building before, during and after its construction, to corroborate implemented the design strategies.

What percent of the day does the public space get exposed to direct sunlight?

PROJECT INFORMATION

Graphic Name: What percent of the day does the public space get exposed to direct sunlight?

Submitted by: Elizabeth de Regt

Firm Name: ZGF Architects LLP

Other contributors or acknowledgements (optional)

What tools did you use to create the graphic?

  • Excel

  • Grasshopper Diva

What kind of graphic is this?

Primary Inputs: Building massing, topography, weather data

Primary Outputs: Annual Shadow/Daylight data

GRAPHIC INFORMATION

What are we looking at?

This graphic displays a heat map of direct daylight, similar to overlaying a year's worth of shadow studies on one image. It is a simple method of showing which areas get more and less daylight throughout the year. Similar methods can be used to chop the year into seasons, time of day, or other more detailed analyses as needed.

How did you make the graphic?

This graphic was created by manipulating the output data created while using Diva to run annual daylight analyses. Excel was used to divide the number of hours of direct daylight at each grid location by the number of hours of direct daylight received by a flat surface in this location. This information as then fed back into Diva to produce the graphic display of the site.

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

What areas of the site are too dark? Which areas get lots of sunlight? Should the massing be adjusted to change these results? Are certain exterior programmatic pieces more suited to some areas than others?

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

While laying out building massing and exterior program elements, it became necessary to analyze the brightest and darkest areas of the site. While a series of typical shadow studies displaying shading at various times of day and year would give an idea of the lighting levels, this graphic combines that information into a single visual. This allows a viewer to immediately understand the levels of sunlight throughout the site. It also allows designers to manipulate the massing in order to shift the results according to design constraints.

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

This graphic is able to combine a year's worth of exterior daylighting data into a single graphic. It is easily decipherable by those outside the construction industry and can be used throughout the design process to inform massing decisions.

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

It would be useful to make this a live graphic, such that any changes to the massing would be immediately reflected in the graphic. Daylight simulation constraints, as well as Diva for Grasshoppers interface, make this difficult to achieve.

What is the impact of Electrochromic Glazing on thermal comfort and outdoor view vs LowE with manual shades?

PROJECT INFORMATION

Graphic Name: What is the impact of Electrochromic Glazing on thermal comfort and outdoor view vs LowE with manual shades?

Submitted by: Ranojoy Dutta

Firm Name: NA

Other contributors or acknowledgements (optional)

What tools did you use to create the graphic?

  • EnergyPlus

  • Grasshopper

  • Honeybee

  • Excel

What kind of graphic is this? Pie chart, Bar chart, Line plot

Primary Inputs: The inputs for the Predicted Mean Vote analysis are hourly values of surface temperatures, air temperature, radiant temperatures. They were generated by running an annual energy simulation for two glazing options.

Primary Outputs: Predicted Mean Vote; Manual Shade Usage Hours ; EC Tint State Hours

GRAPHIC INFORMATION

What are we looking at?

This is a winter thermal comfort analysis for an occupant sitting 1.2 m away from glazing on the south side of a commercial office. The data is recorded from 8 am to 6 pm daily, for the coldest week as per the EPW file fro Chicago, IL. This week represents the effect of intense solar radiation on the south along with the lowest average outdoor air temperature (OAT < -10 ºC).

Due to the low sun altitude (< 30º) in winter whenever there is a clear sunny day the occupants on the south side will experience severe discomfort from glare and overheating even though the OAT is well below 0 C. This condition exists for days 1 & 4 (Panel 5) with high incident radiation throughout the day. As a result, the Electrochromic(EC) glazing stays at Tint 4 (darkest) and the shades are also modeled as fully down. When glare is not a problem EC transitions back to a lighter tint state with higher Tvis and SHGC to allow more daylight as well as passive heating. This is evident for days 3 & 5, where there are only a couple of hours which require darker tints but as the incident radiation levels go down EC selects lighter tint states (Panel 3). On the other hand, once the manual blinds come down they stay down (Panel 4) regardless of improved outside conditions, blocking useful solar gain, reducing daylight and blocking outdoor views for 85% of the hours in that week. On the other hand using predictive controls EC only uses the darkest tint state ( Tint 4) for only 35% of hours (Panel 4), which will reduce daylight for those hours but still maintains full outdoor views.

The primary cause of winter discomfort near windows is inside glass surface temperature, which is dependent on the fenestration U-Value and the OAT. Hence, given the extreme low OAT for the chosen period the PMV is almost always below -0.5 for both glazing scenarios. EC offers comparable winter comfort with substantially more daylight, unobstructed views and greater scope for passive heating. The improvement in U-value from shading devices is minimal unless a relatively air-tight seal is made between the shade and window frame (e.g., with a track). Hence despite the shades being down for most of the time the PMV is only marginally better than VDG, but still mostly below -0.5.

How did you make the graphic?

A commercially available EC product was modeled with 4 tints using the Energy Management System (EMS) feature within Energy Plus v8.9 to replicate the exact manufacturer specified multi-criteria control algorithm . Outputs from this multi-state model were used for thermal comfort analysis using Honeybee. PMV results were then analyzed in Excel. Hourly values for EC Tint states and manual shade status (Up/down) was extracted from the energy plus models and add to the graphic in excel.

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

  • This study was aimed at evaluating occupant comfort and access to outdoor views by increasing window wall ratio (WWR) with EC glazing to 55% for a commercial office in Chicago vs a 40% WWR with standard Low-E glazing with manual shades. The 55 % WWR for EC was determined by annual energy and peak HVAC analysis.

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

This study found that the WWR for a mid-rise office could be increased by 25 - 50% with EC without any HVAC or energy penalty across three distinct climate zones. This translated to significantly higher daylight, greater viewing area and full glare control with year-round outdoor views vs LowE_Shd at 40% WWR. This specific graphic showed that while EC might have marginally more winter discomfort under very low outdoor temperatures it still provides unobstructed views and greater scope for passive heating and daylight in winter. The winter discomfort can be rectified by selecting a lower U-Value.

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

This graphic shows the impact of manual shades staying down regardless of changing outdoor conditions, resulting in blocked view and daylight. The EC glazing however automatically tracks the outside solar conditions ( sun angles and intensity )to chose an appropriate tint state. The outdoor conditions chart show that despite subzero temperatures the incident solar radiation is still very high with a potential for discomfort from overheating and glare unless shades are fully down or EC is in its darkest state. However unlike manual shades EC never blocks outdoor views.

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

I would have liked to make this a web based graphic such that a user could adjust the date period from one week to one month or even a year. Also it would be nice to look at the other façade orientations and other cities. I had all the data but could not present it all due to lack of time.

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

PROJECT INFORMATION

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

Submitted by: Rufei Wang

Firm Name: Harvard Graduate School of Design

Other contributors or acknowledgements (optional) Huishan He, Holly W. Samuelson, Panagiotis Michalatos

What tools did you use to create the graphic?

  • Grasshopper Ladybug
  • Grasshopper Honeybee
  • Adobe InDesign
  • Grasshopper - WinEnergy

What kind of graphic is this? Scatter Plot

Primary Inputs: Glazing U-value, Glazing SHGC

Primary Outputs: Peak load in W/m2 and energy use in kWh/m2

GRAPHIC INFORMATION

What are we looking at?

The graph provides energy performance results for the full range of possible window properties at one time for a given climate, building type and orientation. Vertical and horizontal axes plot U-value and SHGC respectively, the most important window properties in energy performance. Gradient colors represent normalized total heating and cooling energy, and each plot represents one window type. Users can plot window products, using SHGC and U values on the graph to compare their respective performances.

The colorstep value is 2 kBtu/ft2 [6.3 kWh/m2], meanings that window products falling in the same color area produce similar on-site normalized total cooling and heating results (i.e. within 2 kBtu/ft2 [6.3 kWh/m2]).  Note: the heating and cooling values were produced from a full energy model of a perimeter zone.  Thus, the values take into account the window, internal gains, and other comprehensive energy model inputs.

How did you make the graphic?

The graph is the visualization from our proposed window selection tool. We’ve created 14 Grasshopper components for parametric simulation and data visualization, the code is written in C# using Visual Studio Platform. The workflow is as follows: 1. Conduct parametric simulation using self developed components and Grasshopper – Ladybug & Honeybee components. 2. Post process the data using self developed components. 3. Make screenshot from the tool visualization and further annotated the graph in Indesign.

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

Choosing from the multitude of available fenestration products remains challenging. The existing window selection methods have limitations. We are proposing a new parametric workflow and visualization to better integrate with the early-design process.

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

The graphic is part of our research for proposing a new workflow for window selection. We’ve researched the shortcomings of existing methods for selecting windows. We believe that the ability to visualize the impact of full range of window properties could be a beneficial aid to the early design process. A parametric simulation-based methodology was proposed. During the research, we’ve focused on exploring methods to simplify window system modelling and to increase simulation speed while maintaining desired accuracy.

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

The graph allows the users to view the energy results of the entire set of window choices (assembly U-factor and solar heat gain coefficient) and immediately recognize trends in their energy performance. Users can also plot window products of interest on the graph to compare their respective performances.

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

The tool visualization legend is automatically determined from the maximum result value and the minimum result value. The visualization does not provide the option to fix the legend ranges. It can create confusion when two graphs are put together for comparison.

What is the impact of shade on ASHRAE 55 thermal comfort acceptability limits?

PROJECT INFORMATION

Graphic Name: What is the impact of shade on hourly ASHRAE 55 thermal comfort acceptability limits? 

Submitted by:  Madeline Gradillas

Firm Name: Atelier Ten

Other contributors or acknowledgements (optional) An Vo - Atelier Ten, Kyosuke Hiyama - Meiji University

What tools did you use to create the graphic?

  • Design Builder
  • Excel
  • Adobe Illustrator
  • Adobe InDesign
  • Archsim

What kind of graphic is this? 12/24 Plot

Primary Inputs: outdoor climate/weather data, shade vs. sun

Primary Outputs: hourly temperature delta to achieve ASHRAE 55 80% acceptability limit (<20% PPD)

GRAPHIC INFORMATION

What are we looking at?

Examples of a chart plotting annual hourly values, where the horizontal shows each day of the year and vertical each hour of the day.

Each data point is plotted according to a conditional relationship with established standards and/or project specific design questions.  The light blue color represents hours that meet ASHRAE 55's 80% acceptability limits (<20% PPD), while the other colors in the legend show how many degrees Fahrenheit (in operative temperature) it would take to achieve the 80% acceptability limit threshold.  

Two sample uses of the hourly chart are shown here:

Example 1 (top image) - Operative Temperature, per ASHRAE 55 Adaptive Thermal Comfort Model 80% Acceptability Limits

Example 2 (bottom image) - Perceived Operative Temperature of a Body in Direct Sun, per ASHRAE 55 Adaptive Thermal Comfort Model 80% Acceptability Limits

How did you make the graphic?

Excel was used to post-process the data. Illustrator and InDesign were used to edit the original chart into a legible graphic.

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

This graphic was first developed to be a flexible tool for post-analysis assessment, to understand the relationship between energy model/calculative outputs and project-specific questions such as:

-When does the value fall out of acceptability limits? What is the magnitude and frequency of these occurrences over a year?

-Do client or designer expectations of acceptability match those of the established standards’?

-Do the values show daily and/or seasonal trends?

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

This is a useful as tool to establish baseline performance during conceptual design, and then as a comparison tool during design development.

Several plots can be easily viewed side by side to track the effect of design decisions on project performance.

Because data is plotted according to a series of conditional statements explicitly defined by the analyst, the resulting graphics' communicative success depends on the clarity of questions the analyst asks during its making.

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

We continue to adapt this graphic at Atelier Ten to help ourselves and clients better understand increasingly complex questions and discover correlations between performance and design decisions, without having to re-run time-consuming models every time a design changes. Example 2 is one of the more recent plots that shows not only the relationship of operative temperature to ASHRAE adaptive comfort limits, but the "perceived" operative temperature an occupant experiences when in direct sun.

We found both graphics helpful when viewed side by side to communicate the importance of an extensive exterior shading system that was in danger of being VE'd out of a project.

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

Our current  Excel to Illustrator to InDesign workflow is clunky. Ideally this tool would auto-populate a legend and chart text that are easy to customize.

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.