G 07 What is the impact of the hydronic configuration on the performance of a hybrid heat production system?

PROJECT INFORMATION

Graphic Name: What is the impact of the hydronic configuration on the performance of a hybrid heat production system?

Submitted by: G 07-12973

Firm Name: G 07-12973

Other contributors or acknowledgements (optional) G 07-12973

What tools did you use to create the graphic?

  • Matlab

What kind of graphic is this?

Primary Inputs: The inputs required to create this extended type of Load Duration Curve (LDC) are heat loads throughout a year of an hydronic heat production system with its different components. The loads can originate from both measurements and simulations. In this simulation-based case the yearly loads of three components were taken as input: a cogeneration device (CHP), a storage tank and an auxiliary boiler.

Primary Outputs:

1) the total heat load of the building, which is the sum of the loads of the three components (CHP, tank and boiler), sorted in descending order = conventional LDC.

2) the heat loads of the three components separately, corresponding to the descending order of the total heat load.

3) the load duration curve of the CHP itself.

4) areas indicate total heat produced (or charged/discharged) by each component.

5) upper outputs are given for both the ‘expected’ behavior (i.e. what one can expect of a properly working installation) and the real ones for different design concepts.

6) in the graphs, three levels of load can be distinguished: peak, base and part load.

GRAPHIC INFORMATION

What are we looking at?

Most people who design heating systems of buildings are familiar with a ‘load duration curve’: it is a common graphical representation of the heat demand of a building. But did you know that in the same graphic much more information can be shown, to indicate the performance of the heat production system in the blink of an eye? Especially when different heat production components are combined into a ‘hybrid heat production system’, this is beneficial. In the four schemes (upper part of figure), a hybrid production system with its possible designs of hydronic configurations are shown as an example. The first, ‘expected’, explains the heat flows in general and in the three following ones particular design options for the hydronic configuration are shown. In the lower part of the figure, the load duration curves including the loads of the three components are shown, corresponding to the upper schemes. This is the part of the figure that is automatically generated and is proposed here as ‘the fingerprint of a hybrid heat production system’. The x-axis shows the duration in hours and the y-axis the loads, relative to the maximum load of the building. In the reference results (‘expected’) three levels of heat load are distinguished, according to how an in-theory-proper-working installation would perform: -the base heat load is provided by the CHP -the heat demand higher than the CHPs load (dotted line=load duration curve of CHP itself) is covered by an auxiliary boiler - at heat demands of the building lower than the heat load of the CHP, two possible situations can occur. First, the CHP is on and the heat that is not consumed by the building is stored in the tank. Second, the CHP is off and the stored heat is discharged from the tank to heat the building. If the behaviour of the three hydronic configurations are compared with this expected behaviour, some conclusions can be drawn, such as: - for the ‘parallel basic’-configuration, a part of the base load is covered by the boiler instead of the CHP: the figure clearly shows that the hydronics fail, causing the CHP to operate less hours than expected. This configuration should be excluded from the possible designs. -for the two other hydronic configurations the tank can cover partially the base load, for which the CHP can produce more heat than expected.

How did you make the graphic?

An algorithms post-processes the data of full year simulations. The total heat demand is calculated as the some of the loads of the three components, and that total demand is sorted in descending order. Then, the different loads are plotted as stacked areas.

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

- How to evaluate hybrid heat production systems?

- How should a CHP device be integrated in the heat production system of a building?

- What is the effect of buffer tank volume on CHP operation?

- How to ensure the priority of operation of the CHP above that of an auxiliary heater?

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

This type of LDC shows how a particular hydronic configuration influences the operation of a heat production system. Because it is compared with an in-theory-proper-working installation, it enables to detect faults and benefits of the configuration. By creating these figures I learned about the disadvantages of each configuration, without necessarily looking into the detailed dynamic simulation data.

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

Load Duration Curves (LDC) are widely used to communicate about the heat demand of a building. In this extended type of LDC, the load of each single heat-producing component is added, thereby providing much more insight. Indeed, not only the heat demand of the building can be seen, also the way this heat is delivered or stored is shown. Because LDCs as such are familiar, these graphics are easy accessible for both researchers and engineers working in the field of heating.

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

In the end, designers or owners are interested in fuel consumption and the resulting costs, and not necessarily in heat as such. The relation between both are, of course, the efficiencies of all the components, which are not shown in these figures. Hence, extra graphics showing the efficiencies or fuel consumption would be interesting, especially to incorporate the effect of temperature levels in the installation.

G 18 What is the impact of operable glazing fraction on thermal comfort and natural ventilation potential?

PROJECT INFORMATION

Graphic Name:  What is the impact of operable glazing fraction on thermal comfort and natural ventilation potential?

Submitted by: G 18-16695

Firm Name: G 18-16695

Other contributors or acknowledgements (optional) G 18-16695

What tools did you use to create the graphic?

  • Sefaira
  • Adobe Photoshop
  • SketchUp

What kind of graphic is this? Scatter Plot

Primary Inputs: Building geometry (zoned) with glazing; location (weather file); envelope properties, internal conditions, and HVAC parameters.

Primary Outputs: Comfort: % occupied hours within setpoint range (dry bulb temperature). Free area: the ratio of operable window area to floor area for each zone, measured against a target of 5%.

GRAPHIC INFORMATION

What are we looking at?

The graphic is showing two pieces of data relevant for determining the optimal amount of operable glazing for natural ventilation. The first chart (on the left) shows the impact of increasing operable glazing on predicted thermal comfort (the percent of occupied hours with dry bulb temperatures within setpoints). Thermal comfort improves until approximately 75% of glazing is operable, and tops out at 72% of occupied hours being comfortable. The second graphic (on the right) shows the current design’s “free area” -- the ratio of operable window area to floor area for each zone. A free area of 5% is a typical target value for naturally ventilated buildings, and has been codified in standards such as BREEAM in the UK and Title 24 in California. This graphic shows that a number of the building’s zones fail to meet this prescriptive criteria as currently designed. Taken together, the graphics indicate that more operable glazing is needed in many of the zones in order to provide good natural ventilation.

How did you make the graphic?

  1. Modeled building geometry in SketchUp
  2. Uploaded the model to the Sefaira web application
  3. Set up the various simulations (envelope, internal conditions, and HVAC parameters)
  4. Ran a Response Curve for Operable Glazing %, looking at the impact on Thermal Comfort. Exported the resulting graphic.
  5. Ran a Free Area assessment, with a target of 5% free area. Exported the resulting graphic.
  6. Combined the two graphics in Photoshop

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

  • Is there enough operable window area in each zone to provide good natural ventilation?

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

This graphic was combined with other analyses related to the window area, related to daylighting and solar gain. The purpose of these combined investigations was to evaluate whether the glazing strategy and facade design was effective from energy, daylight, and natural ventilation perspectives. The initial glazing ratios were fairly conservative, and the analyses showed that the design would benefit from more glazing in many of the zones, for both natural ventilation and daylighting.

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

The graphic summarizes and clearly presents fairly complex analysis, making the results actionable. It is easy to see how operable glazing affects comfort, and where the problem areas are in the existing design.

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

More clearly connected to two graphs, perhaps by finding a way to put them both in terms of absolute glazing area. The Response Curve is currently terms of % of glazing that is operable, while the Free Area chart is currently based on operable glazing area. This makes it difficult to clearly relate the two.

G 17 What is the impact of building massing on annual energy end uses and heat gains & losses?

PROJECT INFORMATION

Graphic Name: What is the impact of building massing on annual energy end uses and heat gains & losses? 

Submitted by: G 17-13891

Firm Name: G 17-13891

Other contributors or acknowledgements (optional) G 17-13891

What tools did you use to create the graphic?

  • Sefaira
  • Excel
  • Adobe Photoshop
  • SketchUp

What kind of graphic is this? Bar chart

Primary Inputs: Building geometry, and location- and building-use-specific assumptions related to building envelope (utilizing prescriptive values from ASHRAE Std. 90.1) and mechanical systems (utilizing system templates in Sefaira).

Primary Outputs:

  1. Annual energy end use breakdown, normalized per square foot (kWh/ft2/yr)
  2. Sources of heat gain and heat loss, shown as a % of total heat gains and total heat losses (rather than in BTUs)

GRAPHIC INFORMATION

What are we looking at?

The graphic is designed to assess the relative performance of the three massing options, and to provide enough information to help the design team come up with possible strategies for improvement. The bars on the left show energy end uses, normalized per square foot to facilitate quick comparison. For example, one can quickly see that the third option has the highest heating energy use per square foot among the three designs.

On the right are two bars showing annual sources of Heat Gains and Heat Losses, which shed additional light on the heating and cooling end uses. For example, the third option’s “Heat Losses” bar reveals much higher conduction losses through walls than the other options. Because the envelope properties are held constant in this analysis, this difference is due to the greater wall area (higher surface-area-to-volume ratio) of this design compared to the others.

How did you make the graphic?

  1. Created massings in SketchUp and performed energy analysis using Sefaira.
  2. Exported perspective images from SketchUp (without glazing, in order to maintain focus on massing and not facade design).
  3. Logged results in Excel, including total energy end uses and annual heat gains and heat losses.
  4. Normalized the results based on the floor area of each massing.
  5. Created graphs in Excel.
  6. Combined the graphs and model images in Photoshop, added a gray background, and adjusted text and alignment.

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

  • Which design option performs best, and why?
  • What is driving the energy use of each design option?
  • What is driving the heating and cooling loads of each design option?
  • What strategies could improve the performance of each design option?

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

This graphic is a very early-stage analysis. The most important learning is where to focus moving forward -- in this case, on plug loads, glazing U-factor, and wall R-value. The graphic is designed to generate questions and hypotheses -- for instance, “How high would the wall R-value have to be in Option 3 for it to perform as well as Option 2?” These questions are the logical next steps for subsequent analyses..

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

Analysis alone rarely determine a design’s direction. Instead, such analyses are successful if they provide insight on why a design performs the way it does, and what can be done to improve that performance. This graphic sought to provide just the right information to inform decision-making, presented as clearly as possible. We avoided pie charts, as these are more difficult to compare. We used common-sense colors (e.g. red for heating, blue for cooling), and kept them consistent across all charts (e.g. lighting and equipment are the same in both the “end uses” and “heat gains” charts. The result is legible, aesthetically compelling, and above all useful for quickly assessing a design, understanding the factors affecting performance, and generating clear, actionable hypotheses that can be tested with further analysis.

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

Experimented more with the color palette, and with alternate compositions for heat gains and losses.

.

What is the impact of window-to-wall ratio on Energy Use Intensity (kBTU/ft2/yr)?

PROJECT INFORMATION

Graphic Name: What is the impact of window-to-wall ratio on Energy Use Intensity (kBTU/ft2/yr)?

Submitted by: Carl Sterner

Firm Name: Sefaira

Other contributors or acknowledgements (optional) 

What tools did you use to create the graphic?

  • Sefaira

  • Adobe Illustrator

  • SketchUp

What kind of graphic is this? Scatter Plot

Primary Inputs: Building geometry, location, and location- and building-use-specific assumptions related to building envelope (utilizing prescriptive values from ASHRAE Std. 90.1) and mechanical systems (utilizing system templates in Sefaira).

Primary Outputs: Energy Use Intensity in kBTU/sf/yr

GRAPHIC INFORMATION

What are we looking at?

This graphic shows how the optimum southern window-to-wall ratio (WWR) for a building changes as other elements of the design change--illustrating that WWR cannot be optimized in isolation, or sized based upon generic rules of thumb. Each row illustrates an optimum south WWR given a specific envelope and mechanical system design, and shows whether the calculated optimum aligns with the range described by a prescriptive rule of thumb (illustrated as a shaded bar on the sensitivity graph). The optimum WWR changes dramatically based upon the properties of the envelope and mechanical systems, ranging from 0% to 65%.

How did you make the graphic?

  1. Modeled building geometry in SketchUp

  2. Uploaded the model to the Sefaira web application

  3. Set up the various simulations (envelope, internal conditions, and HVAC parameters)

  4. For each scenario, a ran a Response Curve to find the optimum South Window-to-Wall ratio looking at the impact on EUI. Exported the resulting graphic.

  5. Used SketchUp to illustrate the optimum Window-to-Wall ratio for each scenario, and exported a high-resolution image of each.

  6. Combined the model image and Response Curve image in Illustrator, adding linework and descriptive text.

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

  • What is the optimum south window-to-wall ratio for this design?

  • How do changes to the building envelope and mechanical systems affect the optimum?

  • How accurate are rules of thumb regarding WWR?

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

The major takeaway was that WWR cannot be optimized in isolation from the rest of the design. While we knew this in theory, the magnitude of the variation was still startling. It was particularly interesting to see the difference between the option that used “Passive House windows” (U-0.15) and the full “Passive House” option that included high insulation, high air-tightness, and high-efficiency mechanical systems with energy recovery ventilation. The full Passive House design reduced heating loads to such an extent that the design did not benefit from as much solar gain, resulting in a small optimum WWR. This exercise reinforced the need for continual stimulation throughout the process to ensure the design stays on track as the design evolves.

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

The image graphically communicates analysis results, which makes the variation in window-to-wall ratio immediately apparent. It illustrates how changes to inputs create wide variation in outputs, and that rules of thumb cannot be relied upon for optimum results.

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

The inputs for each scenario are summarized on the right-hand side of the graphic. I would have liked to better clarify inputs vs. outputs, and perhaps find a way to represent the inputs graphically through iconography or sparkline graphics, so viewers can better understand what was changing between the various scenarios.

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.

G 14 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: G 14-50652

Firm Name: G 14-50652

Other contributors or acknowledgements (optional) (G 14-50652)

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.

G 10 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: G 10-73210

Firm Name: G 10-73210

Other contributors or acknowledgements (optional) G 10-73210

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.

G 09 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: G 09-44540

Firm Name: G 09-44540

Other contributors or acknowledgements (optional) G 09-44540

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.

G 08 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: G 08-22633

Firm Name: G 08-22633

Other contributors or acknowledgements (optional) G 08-22633

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.

G 06 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: G 06-22498

Firm Name: G 06-22498

Other contributors or acknowledgements (optional) G 06-22498

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.

G 12 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: G 12-96341

Firm Name: G 12-96341

Other contributors or acknowledgements (optional) G 12-96341

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.