How can the thermal environment of an office space be improved by passive and active cooling strategies?

PROJECT INFORMATION

Graphic Name: How can the thermal environment of an office space be improved by passive and active cooling strategies?

Submitted by: Johannes Bracke

Firm Name: Baumann Consulting

What tools did you use to create the graphic?

  • IES

  • Excel

  • Power Point

What kind of graphic is this? Matrix of spatial heatmap and scatter plot

Primary Inputs: Building geometry, location, building envelope specs (U-Value, SHGC, internal gains), mechanical systems (ventilation system, chilled ceilings)

Primary Outputs:

Room air temperature, operative room temperature.

GRAPHIC INFORMATION

What are we looking at?

The graphic compares the indoor air temperature for selected office spaces before and after the implementation of optimization measures. The upper six visualizations show a colored representation of the room air temperature for three different times on a summer day in August. The scatter plot below shows the room air temperature as a function of the outside air temperature. 

How did you make the graphic?

IESVE was used to create the 3D building model. Detailed room models were created for selected office spaces, including the internal loads, cooling equipment and control of the external shading device. A dynamic thermal simulation was then created for the designed and optimized technical parameters. As a result, the room air temperature for the office spaces was calculated over the course of the year. I exported the perspective images from IESVE and created the scatter plots in Excel. I then combined the information in Power Point.

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

  • How can the thermal comfort of the selected office spaces be improved?

  • Which passive and/or active measures lead to an improvement of the thermal comfort?

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

The results of the study show that the installed cooling equipment is significantly undersized. Furthermore, the installed shading device does not effectively prevent the penetration of solar radiation. Overall, this leads to massive thermal comfort issues (high room air temperatures as well as high temperatures of the glass surfaces). The installation of an optimized external shading device and additional cooling capacity allows the room air temperatures to be kept within an acceptable range.

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

The clear color visualization of the room air temperature in the 3D model has supported the decision-making process and helped to convince the client of the proposed measures.

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

The integration of the optimization measures into the graphic would have helped to support the decision-making process. Furthermore, the cooling loads could have been integrated into the graphics. The information should be added as additional layers of information to maintain the simplicity of the graphic.

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: Carl Sterner

Firm Name: Sefaira

Other contributors or acknowledgements (optional) 

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.

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: Carl Sterner

Firm Name: Sefaira

Other contributors or acknowledgements (optional) 

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.

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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 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 orientation and WWR on the energy use?

PROJECT INFORMATION

Graphic Name:  What is the impact of orientation and WWR on the energy use?

Submitted by:

Firm Name:

Other contributors or acknowledgements (optional)

What tools did you use to create the graphic?

  • Sefaira

  • Excel

What kind of graphic is this?

Primary Inputs: Energy use comparison of higher glazing area with 40% WWR

Primary Outputs: Percentage increase in energy use

GRAPHIC INFORMATION

What are we looking at?

The graphic compares 2 orientations with different WWRs for the building. The baseline for both orientations is 40% WWR. The graphic compares the effect of increasing the WWR between 50% to 60% for all the facades. Given the higher glazing area in the 'triangular section' Facade D moves the needle on energy use in Orientation 1. However reorienting the building may help in reducing energy use while maintaining the same glazing area in the 'triangular section'.

How did you make the graphic?

Exported all the cases to excel and post processed it

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

1) What is the effect of the orientation, given the higher glazing area n certain facades?

2) What is the effect of reducing the glazing area?

3) What is the effect of reorienting the building

4) What is the effect of various combinations of WWR with different orientations?

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

Optimizing the facade, interior layout, circulation and other aspects of the architectural design would be affected by the orientation and the WWR.

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

Stacked graph with 2 different scenarios quickly says which is the ' problematic facade in each of the cases.

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

Daylight or glare anaysis

What is the impact of the most important design parameters on multiple outputs: energy demand, thermal comfort and daylight availability?

PROJECT INFORMATION

Graphic Name: What is the impact of the most important design parameters on multiple outputs: energy demand, thermal comfort and daylight availability?

Submitted by: Torben Østergård

Firm Name: MOE Consulting Engineers

Other contributors or acknowledgements (optional) Aalborg University

What tools did you use to create the graphic?

  • D3

  • JS

What kind of graphic is this? 

Primary Inputs: The design space is represented by ten uniformly distributed inputs (sort right to left by overall sensitivity)

Primary Outputs: Energy demand, over temperature (unmet hours), and daylight factor (in teaching rooms)

GRAPHIC INFORMATION

What are we looking at?

The graphics displays the interactive parallel coordinate plot (PCP), which contains the results from 5.000 simulations of a 15.000 m² educational institution. The first 10 coordinates represent uniformly distributed inputs, which are sorted (right to left) by their combined sensitivity on the three outputs represented by the three rightmost coordinates. The outputs are constrained by three criteria: energy demand maximum of 41 kWh/m², overtemperature maximum of 0 kWh/m², and daylight factor of minimum 2%. Minimum and maximum limits are shown above the PCP. Moreover, the bar plots made using “real-time” sensitivity analysis (SA) indicate how much the different parameters have been affected by the current user-defined criteria. Here, the applied constraints have affected (room) reflectance and solar panels area the most. On contrary, the three input distributions to the far left show no or very small changes, which indicate they have no or insignificant influence on meeting the current constraints. For each coordinate, a histogram shows the distribution meeting the user-defined requirements. For instance, most solutions are found for the largest values for room reflectance and solar panels, whereas a SHGC of 0.42 is slightly favorable. The parallel coordinate plot, histograms, bar plots, and limits are updated interactively when adding or changing the user-defined constraints for both input and output coordinates. Thereby, the solution space and consequences are immediately highlighted and displayed when exploring the design space using this interactive graphics.

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How did you make the graphic?

The graphics displays the results from Monte Carlo based building simulations. These simulations were made using Excel and the Danish normative software Be15. Excel is used to describe the parameter variations and run the “black box” Be15 calculations. The results are aggregated in Excel and uploaded to the PCP using a comma separated file. As part of my industrial PhD, we extended the features of a parallel coordinate plot available from D3.js. We added the histogram functionality and I developed sensitivity methods to rank inputs according to multiple outputs and to indicate how much each parameter is affected by user-defined constraints. The visualization is available at https://buildingdesign.moe.dk/phd/ibpsa.html. The site also allows the user to construct rapid metamodels based on neural networks. Thereby, millions of new predictions can be made in a few seconds. These will be displayed in another PCP below the original.

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

In general: How can we explore a multi-dimensional design space in a multi-actor decision-making process? How can we help decision-makers navigate the PCP and highlight the most influential parameters and the most favorable input spans? How can we meet the computational challenges of Monte Carlo simulations? (leading to the metamodeling feature) How can we answer “what-if” questions Case specific for the shown example: Can we avoid photovoltaics and still meet the requirements? What is the appropriate window-to-wall-ratio which leads to the largest possible solutions space? How should the louvres (side fins) be design while balancing daylight availability with energy demand and thermal comfort? Is venting necessary and if so, how much air flow should it provide? Should the contractors aim for the lowest level of airtightness (infiltration) or focus on insulation levels of the facade and windows?

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

This type of interactive graphic is essential when dealing with multidimensional data such as Monte Carlo building simulations, which are growing in popularity. Indeed, a Monte Carlo based framework fits very well with the large uncertainties and variability, which are dominant i building design. The plot can be used recurrently in the iterative design process and help identify the solutions that meet the requirements of all decision-makers.

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

The extension of the PCP with histograms, sensitivity analysis and metamodeling strengths the already popular PCP and makes it easier for the decision-makers to investigate an enormous multi-dimensional design space and observe the consequences of different decisions.

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

Primarily, I would like to develop and test another method for real-time SA based on variance-decomposition, which may be better than the current one based on the two-sample Smirnov test. In addition, I would like to extent and improve the metamodeling features (using larger neural networks) to improve accuracy of those. In general, I would like an experienced front-end develop to enhance user-flow and design. Finally, there are a few bugs that must be dealt with.

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's the impact of facade design on thermal comfort?

PROJECT INFORMATION

Graphic Name: What's the impact of facade design on thermal comfort?

Submitted by: Andrew Corney           

Firm Name: Sefaira

What tools did you use to create the graphic? Sefaira

What kind of graphic is this? Floor Plan Heat Map

GRAPHIC INFORMATION

What are we looking at?

Provides a floor plan view of the building, coloured with information per zone describing whether they achieve a thermal comfort target.

The comfort target is shown in the criteria above. The user can quickly hone in on the zones that are failing one floor at a time.

By presenting the results in plan view, with the rooms shown, it presents results visually in a way that an audience familiar with the floor plate layout can process easily.

How did you make the graphic?

Sefaira makes these outputs automatically with any analysis run including thermal comfort.

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

This graphic tries to help the designer understand “which zones are having the most trouble in my building” and then communicate that as needed to a wider team.

Traditionally consultants have to do this by:

  • Declaring for themselves what they expect the worst zone to be based on experience
  • Building thermal models of the zones that are declared as likely to be the worst

Relying on the trust between them on the design team to use that to steer design attention on those spaces.

This workflow is tedious and also in part risky, especially if the analyst is inexperienced and the reviewer is not privy to all the envelope details on a project.

The goal here is to help a designer hone in quickly on the spaces they should worry about first, while providing an easy way of proving that declaration to the rest of the design team.

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

The graphic helps with facade design by identifying where thermal comfort goals are being missed. They should be incorporated into studies looking into peak loads, energy and daylight because all of these issues are affected by the envelope design.

The learning is that thermal comfort can be used as a design tool and that it can provide very helpful insight into how successfully a design is working.

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

Thermal comfort analysis is typically time consuming and hard. It therefore requires a lot of preparation before analysis can be useful.

This graphic is helpful because it makes it really easy to see which spaces are struggling to meet the thermal comfort goals. It reduces the effort needed to hone in on the key spaces requiring attention and serves to prove visually the design team that those spaces are worse.

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

We ended up adding a gradient option to this graphic. The reason was so that people could identify which zones were just failing vs those that were the worst on the floor plate.

We’re interested in exploring how to convert this graphic into a comparative analysis, where a project has a series of design options and wants to compare PMV for the worst zone against those options and somehow communicate it on the floor plan.

What is the impact of building form on solar radiations contribution to peak cooling requirements?

PROJECT INFORMATION

Graphic Name: What is the impact of building form on solar radiation's contribution to peak cooling requirements?

Submitted by: Michael Sawford

Firm Name: ESDL USA, Inc.

Other Acknowledgements: EDSL Tas consultancy services

What tools did you use to create the graphic? EDSL Tas

What kind of graphic is this? 3-dimensional Heat map

Primary Inputs: building and site geometry

Primary Outputs: Solar radiation in kWh/m2

GRAPHIC INFORMATION

What are we looking at?

3D model of part of a building depicting the annual insolation (solar radiance over time) on the external building envelope using the solar data from the weather data for the building’s location. The compass rose is shown at the ground plane indicating the North direction. The kWh/m2 scale for annual insolation is found on the left-hand side of the image.

How did you make the graphic?

EDSL Tas was used to create the 3D model. The results of the annual insolation can be displayed by choosing the weather data for the building's location. The annual insolation is shown visually in the tool for live interaction. The 3D model view can be interrogated in real time in an interactive fashion. However, using automated reporting tools in EDSL Tas software you are able to export images (as uploaded), a series of images, or videos.

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

How could the building form impact peak cooling requirements? Investigation of the building form and its interactions with solar data for the building's location. What would be the impact of the south facing circular spaces found at each end of the curved façade.

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

This is one view of the visual data used to communicate the impact of the building form on annual insolation (solar radiance over time) on a surface by surface basis, which can highlight areas of interest regarding peak cooling due to high exposure to solar gain to the design team/owner. Videos orbiting the annual data shown on the 3D model were created as well as showing the data from within the EDSL Tas software.

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

Being able to show the annual insolation data, whether in still images or in videos orbiting the building, allowed for easier communication of the data in context with the building. Flexibility in choosing which information to display in the graphic and how the output is created was a great benefit in visualizing the data. (For example, options to create a single image for a particular view of the model, or a series of images created stepping through an orbit of the 3D model could be automatically created. Or creating a video to capture the orbit around the model.) When viewing the 3D model view within the EDSL Tas software (prior to export), if you mouse over a particular surface in the building envelope, information about the surfaces annual insolation is displayed, this allows you to interrogate the data in real time in an interactive fashion.

 

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

Adjusting the geometry, by importing a new Revit model or directly altering the EDSL Tas model, and analysing the impact on the annual insolation could have been an interesting study to compare building form options.
 

What is the impact of building type and form on the different heat gains and losses for an extreme summer week?

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PROJECT INFORMATION

Graphic Name: What is the impact of building type and form on the different heat gains and losses for an extreme summer week?

Submitted by: Jeff Geisinger

Firm Name: NA

Other contributors or acknowledgements: NA

What tools did you use to create the graphic? EnergyPlus. Grasshopper ArchSim, Excel, Adobe InDesign

What kind of graphic is this? Donut Pie Charts

GRAPHIC INFORMATION

What are we looking at?

This graphic shows a breakdown of energy loads for two building types for an extreme summer week in the climate of San Luis Potosí, Mexico. The size of each segment of the donut chart represents the relative significance of a given energy load. Heat gains are represented in greys, reds, and yellows on the right of the chart, and heat losses are shown as shades of blue on the left of the chart.

How did you make the graphic?

I ran an energy simulation using Archsim on basic Rhino geometry. Using Excel, I processed the .csv output data into a donut chart using Excel, and assigned colors and fonts in that tool for the various energy load components. I further annotated the graphic in InDesign, and accompanied the chart with Rhino screenshots to make the relationship to base building geometry.

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

I was interested in developing a visual way to communicate what energy conservation measures were the highest priority for a given building type in this climate.

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

Because this was part of an environmental analysis for a large master plan project in a hot climate, the graphic was one of the earlier analysis to set the stage for subsequent energy studies, such as the effect of solar orientation and glazing ratios on cooling load.

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

I think this one graphic successfully combines both the energy balance of a building (gains and losses equal on both sides of a graph) along with the relative sizes of different loads.

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

Sometimes it's difficult to squeeze in text labels directly on top of graph elements, so I might try using a legend next time.

What is the impact of operable window openings on the distribution of peak air velocities at different levels of a facade?

PROJECT INFORMATION

Graphic Name: What is the impact of operable window openings on the distribution of peak air velocities at different levels of a facade?

Submitted by: Michael Sawford

Firm Name: EDSL USA, Inc.

Other contributors or acknowledgements (optional) EDSL Tas consultancy services

What tools did you use to create the graphic? EDSL Tas 

What kind of graphic is this? Natural Ventilation Vectors / Spatially Integrated

Primary Inputs: building geometry, window operability information

Primary Outputs: Peak air velocities by hour (m/s), mass flow rate (kg/s)

GRAPHIC INFORMATION

What are we looking at?

3D model of part of a building depicting the natural ventilation air flows in and out of modelled spaces taking into account; the wind speed/direction of the location at a given hour (day 74 hour 14, as shown at the top of the image), the altitude, and the stack effect. Light blue arrows at the base of the building section indicate the wind direction for the hour being displayed. Magnitude of each arrow (ligh blue, red, navy blue) represents the speed of the air. Direction of the red/navy blue arrows show the direction of flow. The wind rose is depicted on the ground plane, the metres per second scale for wind speed is found on the right hand side of the image. The mass flow rate in kg/s for the wind speed is found on the left hand side of the image. A key is located in the lower left hand side for the flow arrows.

How did you make the graphic?

EDSL Tas was used to create the 3D model, input natural ventilation openings and their control functions, and simulate the building. The results were then showed visually in the tool for live interaction. Using automated reporting tools in EDSL Tas software the image was produced.

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

Validation of a natural ventilation scheme. Understanding the impact of the location of openings and the building form together with the wind data for the location. Understanding the impact of the Chinook winds on the natural ventilation strategy. Understanding the impact of the full building section height atrium space on the concave side of the building and its interaction with open plan office spaces in the greater floor plate of the building.

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

This is one snap shot of the visual data used to communicate the natural ventilation strategy to the design team/owner. Videos showing the natural ventilation scheme varying by hour were also produced to communicate the results. Natural ventilation could maintain comfort levels in the office spaces.

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

Being able to show the natural ventilation scheme in action, whether in still images or in videos, allowed for easier communication of the data in context with the building and subdivided spaces. Flexibility in choosing which information to display and how the output is created was a great benefit in visualising the data. (For example, options to create a single image for one hour, or creating a series of images in order to step through each hour of a particular day for a slide deck could be automatically created. It is also possible to create a video to capture the responses of the building to changing wind conditions hour by hour.) The visual data can also be accompanied with tabulated hourly data of aperture air flow rates where peak flow rates can be assessed in a more granular fashion. When viewing the 3D model view uploaded within the EDSL Tas software (prior to export), if you mouse over a particular surface/arrow in the model, information about the surface/space/air flow rate is displayed, this allows you to interrogate the data in real time in an interactive fashion.

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

I could have opted to remove the compass (showing the North arrow) from the view as it is not visible in the current view. By orbiting around, zooming out further I could have navigated the 3D model view whereby the North arrow information could have been useful to the graphic output. Accompanying the data with an indication of peak air velocities would have provided context to the feasibility of naturally ventilating the building.

What is the impact of building form on solar radiation gains for an atrium space? 

PROJECT INFORMATION

Graphic Name: What is the impact of building form on solar radiation gains for an atrium space? 

Submitted by:  Michael Sawford

Firm Name: EDSL USA, Inc.

Other contributors or acknowledgements (optional) EDSL Tas consultancy services

Who performed the simulation analysis? (select all that apply) Architect - Internal Sustainability Personnel

What tools did you use to create the graphic? EDSL Tas 

What kind of graphic is this? 3D projection of internal solar loads / spatially integrated

Primary Inputs: building and site geometry

Primary Outputs: solar radiation in (W/m2)

GRAPHIC INFORMATION

What are we looking at?

3D model of part of a building, isolating the spaces associated with an atrium space facing South, depicting the solar gain in the atrium spaces (the solar radiance transmitted through the building envelope) into the zone adjacent to the envelope for a given day and hour (day 74 hour 12, as shown at the top of the image) using the solar data from the weather data for the building’s location and building envelope performance parameters. The atrium spaces are colored by solar gain and the remaining areas of the 3D model are included only in wireframe so that the context of the model is included. The W/m2 scale for solar gain is found on the left-hand side of the image. The Sun position is also shown for the particular day and time as a yellow orb with a yellow dotted line indicating the Sun direction. There are multiple blue lines showing the varying sun arcs and analemma’s including the current Sun arc on which the yellow orb is located. The current Sun arc is indicated by yellow lines on the Sun arc extending from the yellow orb. Analemma’s and Sun arcs are also displayed below the horizon by grey lines. In addition, the sky dome is displayed by a grey grid. The wind rose is depicted on the ground plane, the metres per second scale for wind speed is found on the right-hand side of the image. The current wind direction is also displayed by the light blue arrows on the ground plane. Small inlet and outlet arrows can be seen at the bottom and top level of the atrium indicating the flow of air in at lower level and out at the upper level. (More display options have been chosen intensionally to highlight the need for clear intelligible graphics when communicating BEM results data).

How did you make the graphic?

EDSL Tas was used to create the 3D model. Building envelope performance data was then added to the model utilising the Tas constructions databases and created by user data entry. An example database included is the international glazing database for glazing materials, making it easy to create glazing constructions for investigation. New materials can be created as needed and saved in Tas Constructions databases for reuse as necessary also. The results of the solar gain in the model spaces can then be displayed after simulation of the building. The solar gain in the atrium spaces has been isolated by creating a group of zones for the atrium as an ‘output selection’. The results for this output selection of zones can then be shown visually in the tool for live interaction or report exports. The 3D model view can be interrogated in real time in an interactive fashion. However, using automated reporting tools in EDSL Tas software you are able to export images/videos. The uploaded image is an example of the output for one particular hour of one day from a particular view point.

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

Investigation of the building form and its interactions with solar data for the buildings location. Solar gain values for different times of day, and days of the year can be investigated to see implications for the atrium space for extreme temperatures at the top of the atrium and the potential natural ventilation options driven by the stack effect to alleviate those peak temperatures at the top of the atrium space..

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

This is one view of the visual data used to communicate the impact of solar gain in the atrium space and the potential for natural ventilation driven by the stack effect. Air flow arrows indicate air flowing in lower level apertures and exiting through upper level apertures in the atrium space driven by the stack effect and the prevailing wind direction for this hour. Videos stepping through hours of particular days can be created as well as showing the data from within the EDSL Tas software.

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

Being able to show the solar gain, whether in still images or in videos stepping through a particular day, allowed for easier communication of the data in context with the building. Flexibility in choosing which information to display and how the output is created was a great benefit in visualising the data from EDSL Tas. (For example, options to create a single image for a particular view of the model, or a series of images created stepping through hours of a particular day could be automatically created. Or creating a video to capture the responses of the building to changing solar and wind conditions hour by hour). When viewing the 3D model view within the EDSL Tas software (prior to export), if you mouse over a particular zone/surface/air flow arrow in the view information about the zone/air flow rate is displayed. For example, the solar gain in a particular zone in the atrium or the flow rate in/out of the atrium is shown. This allows you to interrogate the data in real time in an interactive fashion.

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

I could have opted to exclude some extra visual data not pertinent to the analysis goals. For example, removing the Sun arcs and analemma’s below the horizon and the sky dome grid. By removing the extra data, the graphic could be understood more easily and therefor communicated the results in a more intelligible fashion.