Optimum balcony design for maximum solar energy generation and indoor visual comfort?

PROJECT INFORMATION

Submitted by:  Hoang

Firm Name: Transsolar

ASHRAE Climate Zone: 5B

Building/Space Type: Residential

Who performed the simulation analysis?  External Energy Consultant

What tools were used for the simulation analysis? 

  • Grasshopper Ladybug

  • Grasshopper Honeybee

  • TRNSYS

  • Python

What tools did you use to create the graphic?

  • Grasshopper Ladybug

  • Grasshopper Honeybee

  • D3.js

What phase of the project was analysis conducted?  Schematic Design

What are the primary inputs of the analysis?  Balcony Geometry

What are the primary outputs of the analysis?  Total solar energy generated per balcony, indoor visual comfort quality

PROCESS

List the investigations questions that drove your analysis process.

What would be the most optimum shape for a balcony in order to gain as much PV power as possible while ensuring the indoor visual comfort?

How was simulation integrated into the overall design process?

The simulation is carried on side-by-side with the evolution of architecture design, allowing crosschecking and providing feedback to each iteration of architecture design. An interactive html graphic was created allowing the team to "view and play" with it at the same time, understanding how each parameter can have an impact to the final design goal.

How did you set up the simulation analysis and workflow?

A dynamic Grasshopper model of the architecture design was set up in order to perform fast and reliable simulations as the floor-plan evolve through each iteration. The simulation model evaluates different balcony geometries through different criteria, including: PV power generated on the facade, solar exposure on the window, indoor visual comfort. After having the simulation results, a Python script was used to generate an interactive parallel-coordinate graph, visualizes all variants together as a part of a multi-objective study. This allows the architect to select the optimum geometry as well as understand how each parameter can impact the final goals of the design.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

The final graphic is html file (parallel coordinate, generate by Python plotly package) which can be easily opened by anyone via internet browser. In addition, the graphic is interactive, therefore the design team can "play" with the graphic to not only pick out the most optimum solution for a multi-objective study but also to understand the impact of each parameter to the design goals.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The investigation is a great opportunity to know the complexity of the building and understand the depth of involvement required to make accurate building model. Also this process allows us to apply our current knowledge to test the feasibility of design strategies.

What is the impact of toplighting on daylight and energy performance in a hot climate?

PROJECT INFORMATION

Submitted by:  Elliot J Glassman, Associate

Firm Name: WSP Built Ecology

ASHRAE Climate Zone: 1A

Building/Space Type: Office

Who performed the simulation analysis?  External Energy Consultant

What tools were used for the simulation analysis? 

  • Grasshopper Honeybee

  • DIVA for Grasshopper

What tools did you use to create the graphic?

  • Excel

  • Adobe InDesign

What phase of the project was analysis conducted?  Design Development

What are the primary inputs of the analysis?  Material reflectance and thermal properties, various toplighting options, internal loads

What are the primary outputs of the analysis?  sDA, ASE, DGP, EUI

PROCESS

List the investigations questions that drove your analysis process.

Can we bring top lighting to a below grade office space while managing the climate’s high angle sun and strong solar radiation? What are the different possible design directions that would work for both daylight and energy performance? What is the impact of different alternatives on daylight, visual comfort, and energy? Are there trade-offs or synergies between daylight and energy performance?

How was simulation integrated into the overall design process?

This study was conducted on the project during early design development. The design team had wanted to bring daylight to a below grade office space through a lightwell and toplighting. The initial proposal for the toplighting was a large, centralized horizontal skylight. Our building performance team expressed concern that this would introduce a great deal of direct sun to the space because of the high sun angles in the climate, imposing unwanted solar loads and greatly increasing the risk of glare.

Simulation was brought into the process to illustrate the concerns as well as propose alternative toplighting approaches that could successfully daylight the space while reducing solar loads and glare. The goal was not to restrict the design but demonstrate a variety of alternatives that would work, giving the design team the flexibility to choose the approach that they felt was compatible with their design intent.

A reference case was created with no toplighting that would have only the lightwell to provide daylight but would perform well in terms of energy relative to the toplit alternatives. In addition to the horizontal toplight design, we proposed alternative design approaches such as a louvered shading system over the toplight to diffuse sunlight, a north facing monitor, and smaller monitors distributed across the space. Within each design direction, we look at three different parameters which varied by scheme such as the VLT of the glass for the unprotected skylight, louver angle for the shaded skylight, or the area of glass for the monitors.

How did you set up the simulation analysis and workflow?

We utilized the power of Grasshopper to perform the simulation analysis on all the alternatives. Each of the toplight schemes were preprogrammed into the script along with the parameterized variable we wanted to explore within each scheme.

The simulation was set up to run daylight, glare, and energy analysis for each alternative and output images of the results with unique file names referenced to the scheme. The simulations were connected so that the daylight analysis would inform the lighting schedule used in the energy simulation.

In traditional modelling approaches, you may need to coordinate inputs across multiple models to make sure you are getting an apples to apples comparison of alternatives. In our workflow, the geometry was parametric so we had one model where our inputs for material properties, thermal constructions, and internal loads were entered once and remained constant through each iteration. The only thing that varied was the lighting schedule which was automatically generated by the daylight performance.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

Looking at multiple alternatives can produce a great deal of outputs. We wanted to balance being able to understand the relative performance of each alternative at a glance with the ability to dig deeper into the metrics if desired.

We conceived of the result visualization as a matrix for each of the metrics. Each cell of the matrix contained the visual output of the simulation and the border of each cell was color coded based on the result to give a quick indication of the performance. Grasshopper helped us assign the colors based on gradients so that we could look at the performance of the alternatives both relative to each other and against fixed performance thresholds.

Based on the color coding, it was clear that while the unprotected toplight appeared to be well daylit, it was actually letting in too much direct sun, negatively impacting visual comfort and energy.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The interesting conclusion illustrated by the study was that there were a number of schemes that performed better in terms of energy than the reference case without a toplight since the daylight reduced the lighting load and subsequently the cooling load as well.

This meant that there was not a trade off we needed to make between providing daylight or good energy performance. Rather, by sensibly managing the way that daylight entered the building, we could provide a well daylit and comfortable indoor environment for the occupants while reducing energy consumption.

There were a number of different alternatives that performed well giving us some flexibility to choose based on aesthetic or constructability criteria.

What is the impact of orientation and other design decisions on daylight quality?

PROJECT INFORMATION

Submitted by: Melissa Kelly and Elliot Glassman

Firm Name: WSP Built Ecology

Other contributors or acknowledgements: Cortney West developed the scope and structure of the study in an earlier iteration

ASHRAE Climate Zone: 4A

Building/Space Type: Education

Who performed the simulation analysis?  External Energy Consultant

What tools were used for the simulation analysis? 

  • Rhino - DIVA

What tools did you use to create the graphic?

  • Excel

  • Adobe Indesign

What phase of the project was analysis conducted?  Post Occupancy

What are the primary inputs of the analysis?  Orientation, window configuration, shading configuration, and interior finishes

What are the primary outputs of the analysis?  Spatial daylight autonomy (sDA), annual sunlight exposure (ASE)

PROCESS

List the investigations questions that drove your analysis process.

What are the key design decisions that may help or limit achievement of the LEED v4 Daylight credit? To what extent is daylight quality determined by orientation? Are there any specific considerations for rooms based on orientation?

How was simulation integrated into the overall design process?

The simulation process began with the single goal of coming as close as possible to meeting the LEED v4 Daylight credit criteria for as many configurations of a prototype classroom as possible. Parametric variables were determined through discussion with the design team in order to map out which aspects of the design were fixed and which were flexible. We then ran cases illustrating each potential design change, and presented initial results for discussion at a team meeting. With their feedback, we then looked at a few other opportunities of interest and re-rand the simulations. While the simulations themselves could not be interactive, the setup and results allowed us to have meaningful and detailed discussions.

How did you set up the simulation analysis and workflow?

The initial setup involved building out a shoebox model from floorplans and creating an Excel spreadsheet of each design change under consideration. This spreadsheet then became an input source for a Grasshopper for Rhino script which automated the creation and running of DIVA simulations. The script automatically exported ASE and sDA results into a spreadsheet and saved image files for each case. We then post-processed the results in Excel, and created an InDesign file with linked images which could automatically update. This saved a lot of time and effort at the beginning and end of the process.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

To answer the question of which design decisions create the greatest risk (or best opportunity) for achieving the LEED v4 credit, we plotted the range of results for each parameter by orientation. This helped to illustrate that there are a few variables which are critical to achieving high quality daylighting, while other areas could be more flexible without risking daylight access.

One challenge of presenting daylighting results, particularly for the LEED v4 criteria, is that it requires the balance of two opposing metrics rather than the optimization of a single metric. To look at the results on a granular level, we initially plotted ASE and sDA individually by orientation, but it didn't tell the whole picture on daylight quality. To solve this, we created compact graphs with both sDA and ASE results for each case, with a shaded background showing the LEED credit thresholds. Having one metric where lower is better and another where higher is better could be confusing, but the shaded background and color-coding helps to frame the data.

Lastly, we compiled spatial ASE and sDA results for each case. Superimposing the floorplan onto these results helps to clearly communicate how light levels and especially glare levels (through the proxy metric of ASE) could affect students, and provides a more tangible counterpart to the metric-driven visualizations.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The extent to which orientation determined achievability was one key takeaway from the process. We learned that interior parameters such as surface reflectivity can only go so far, but strategic, climate-oriented shading strategies are almost mandatory to achieve LEED v4 thresholds for all but north-facing classrooms. This wasn’t a revelation, but the study helped to reinforce and illustrate exactly why that’s the case and that there are real benefits beyond LEED to doing so.

What is the impact of daylighting on creating dynamic learning environments?

PROJECT INFORMATION

Submitted by: Helena Zambrano

Firm Name: Overland Partners

Other contributors or acknowledgements: LAM Partners - Architectural lighting consulants

ASHRAE Climate Zone: 2A

Building/Space Type: Education

Who performed the simulation analysis?  Architect - Design Staff

What tools were used for the simulation analysis? 

  • Rhino DIVA

What tools did you use to create the graphic?

  • Adobe Illustrator

What phase of the project was analysis conducted?  Schematic Design, Design Development, Construction Documentation

What are the primary inputs of the analysis?  Daylight as the primary light source for the four levels of the atrium.

What are the primary outputs of the analysis?  Daylight autonomy, illuminance, Radiance visualizations, and false color imaging.

PROCESS

List the investigations questions that drove your analysis process.

 

Can we provide a daylit atrium in the center of the building to support occupant well-being without overheating the building? How do we provide appropriate daylight levels for various task areas on different levels from a single source?

How was simulation integrated into the overall design process?

 

Daylighting was identified as one of the most important design attributes to provide the necessary mental and visual stimulation and improve occupant productivity and comfort. However, increased cooling loads and glare potential were potential negative effects of a glazed atrium roof in the project’s hot and humid climate zone.

An additional challenge was to provide adequate light levels from a single source—skylights on the roof—to four receding stories, avoiding an overlit top floor and an underlit ground floor, with different task areas requiring different light levels throughout.

Exterior light scoops and interior baffles were designed in response to these challenges. The following variables determined their performance:

  • Distance of the light scoop from the top of the glass to admit appropriate light levels

  • Size of the light scoop to fully or partially cover the glass to avoid overheating

  • Angle of the light scoop to redirect sunlight

  • Rotation of the light scoop to catch light from different sky orientations, to add dynamism by reflecting sunlight of various color temperature and intensity.

The iterative process was fully integrated; the daylight simulations were performed in-house and reviewed by the lighting consultant as a form of peer review. In-house simulation was very beneficial for the project, the feedback was extremely fast, and the team could test as many iterations and variables as necessary. As well as reevaluate the design of the shading structure in terms of constructability constraints during the construction documents phase to maintain performance.

How did you set up the simulation analysis and workflow?

A Revit model was imported into Rhino. The modeler cleaned up the base geometry, converting meshes into Nurbs and window components to single surfaces. The roof, skylights, baffles and light scoops were created from scratch in Rhino and parametrized with Grasshopper.

Initially DIVA for Rhino and then DIVA for Grasshopper, with Radiance and Daysim as their calculation engines, were used to simulate daylight autonomy, an annual, climate-based metric that uses climatic data from *.epw files.

Daylight autonomy was used to see the overall performance of the design. Point-in-time illuminance was used to see the performance during specific times of the day and year, as well as to look for excess light. Radiance visualizations and false color imaging were studied to see the apparent illumination in the space and in different surfaces.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

The process was highly collaborative. Being integrated in the design team, the modeler didn’t provide a formal report of each iteration; rather the flow of information was frequent and informal using diagrams, radiance renderings, and false color daylight autonomy and illuminance results.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The ability to quickly test design iterations very early in the design process was key in the success of the goals for the atrium. The simulation results informed the overall strategy and refined the geometry of each component of the atrium’s roof to achieve the goals of modulating the gradients of light in the baffles in a dynamic pattern and maintaining a general illumination levels of 300 lux.

The daylighting simulations also allowed the design team to reduce the amount of glazing from a fully glazed atrium roof by 92 percent, which had the double benefit of making the atrium design more economically feasible and achieve the goal of avoiding overheating.

What is the impact of Architectural and MEP Systems on Energy and Construction Costs?

PROJECT INFORMATION

Submitted by: Amir Rezaei

Firm Name: CannonDesign

ASHRAE Climate Zone: 5A

Building/Space Type: Office

Who performed the simulation analysis?  High Performance Building Analyst

What tools were used for the simulation analysis? 

  • EnergyPlus

  • jEplus+EA + PowerBI

What tools did you use to create the graphic?

  • OpenStudio

  • PowerBI

What phase of the project was analysis conducted?  Conceptual Design, Schematic Design

What are the primary inputs of the analysis?  Envelope thermal properties, window-wall ratios, zone HVAC type, AHU coil types, boiler/chiller sizes

What are the primary outputs of the analysis?  EUI, Energy cost, Construction cost

PROCESS

List the investigations questions that drove your analysis process.

Designing an office in a specific climate (Buffalo, NY), I want to know what are most economical energy conservation measures either architecturally or mechanically? What are the impact of different WWRs on different facades on the mechanical system sizes? How does the envelope thermal properties affect the overall building energy use and construction costs? How much energy savings can I achieve using a more efficient HVAC system for my building? I only need ballpark numbers for early stages of design! Should I spend more money on more insulation or upgrade to a more efficient HVAC system?

How was simulation integrated into the overall design process?

A fully parametrized shoebox model was created in Energyplus with EPMacro capabilities and connected to the optimization engine JEPlus+EA. A large number of architectural and mechanical equipment were modeled and the goal was to look at the combination of different variables that land the project on the intersection of low energy/cost and construction cost.

The visualized results were used at the onset of the project to come up with the ballpark WWR and envelope properties that the architect took responsibility to design to. The results were also used to quantify the ballpark range of EUI and energy cost associated with different AHU and zone HVAC systems, i.e. the impact of having a plant loop versus gas/DX/electrical solutions. The inputs to the simulations also has construction cost associated with all the design variables including amount and type of insulation, glazing properties, HVAC system type, chiller/boiler size, etc. so that the energy implications of design decisions can be looked at in conjunction with the building construction cost implications.

How did you set up the simulation analysis and workflow?

1) Fully parametrized a shoebox model that can look at multiple combination of different design inputs. 2) Connected that parametrized model to JEPlus+EA 3) Simulated overnight for a few thousand simulations 4) Collected the data from simulations and post-processed the data in PowerBi to make the data interactive and digestible

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

Using PowerBi. Interactive nature of data was successful. The interactive data started conversations among team members on what strategies were most economical depending on other elements of design. The results showed how each design elements's performance is truly dependent on the previous one. For example, the optimized WWR conversation and how that impacts the size of central plant equipment that in turn drives the overall cost of the HVAC solutions and how those two are interdependent based on the thermal performance of glazing and other envelope properties...

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

1. Learnt that building science concepts are best discussed with data when multiple stakeholders at the table.

2. The large datasets can be confusing but with the expert at the table to navigate the data the design direction can significantly be influenced and maybe changed!

3. Not everyone is ready for large data shock and it takes time to get used to this way of approaching design.

4. Was able to demonstrate how even with a traditional mechanical system it is possible to deliver high performance office projects with more investment in the envelope solutions.

5. Users could click on different points (hold SHIFT to select multiple design points) or filter the parallel coordinate graphs to compare the results early in the design process. This was done as a case study for office spaces with Buffalo, NY climatic data and the plan is to perform similar simulations for different climatic conditions and building typologies to have the EUI and energy cost numbers ready for every project we design in major climates we design for!

What is the impact of glazing options on comfort for a ground level lobby in an urban environment?

PROJECT INFORMATION

Submitted by:  Ken Takahashi - Energy Modeler

Firm Name: Integral Group

Other contributors or acknowledgements: Michael Martinez

ASHRAE Climate Zone: 3C

Building/Space Type: Office

Who performed the simulation analysis?  MEP Firm, External Energy Consultant

What tools were used for the simulation analysis? 

  • OpenStudio + Eplus

  • EnergyPlus

  • Grasshopper Ladybug

  • Grasshopper Honeybee

What tools did you use to create the graphic?

  • Adobe Photoshop

  • Grasshopper Honeybee

  • Adobe Indesign

  • Adobe Illustrator

What phase of the project was analysis conducted?  Construction Documentation

What are the primary inputs of the analysis?  Glazing, Lobby Geometry, Surrounding Buildings, HVAC system, Finishes

What are the primary outputs of the analysis?  Comfort, Surface Temperature, Microclimate Analysis

PROCESS

List the investigations questions that drove your analysis process.

Is low-e glazing necessary in order to maintain lobby comfort in a well shaded urban environment? Can the glazing be replaced with cheaper laminated glazing while still maintaining a comfortable lobby throughout the year?

How was simulation integrated into the overall design process?

This analysis was done late in the design process because the architect wanted a second option from a different MEP/energy consultant firm. The design was mostly complete at this point but the VE process put the expensive insulated glazing unit into question for the ground level lobby surrounded by neighboring tall buildings. The team was hired to investigate different glazing options and see the impact on comfort for select days and for the entire year.

First, a 180° fisheye photo was taken from the site where the lobby glazing will be located. Then a sun path diagram of the site was overlaid onto fisheye photo. This way, the design team can identify when the sun will hit the lobby and when it will be shaded by neighboring buildings. This was compared the the Rhino model of the site provided by the architect and compared with their shading analysis to verify the validity of the geometry.

How did you set up the simulation analysis and workflow?

First, key days were decided based on site shading and local weather conditions. Then three glazing options were decided. the three options were a laminated glass with no Low-E coating, laminated glass with Low-E coating, and an insulated glazing unit (IGU) with Low-E coating.

The geometry of the site and surrounding buildings provided from the architect was simplified and exported as a gbXML file using the Honeybee Plugin for Grasshopper. Then the model was taken into OpenStudio where an energy modeling analysis was done for the lobby only. This model took information such as the site shading, envelope construction, and design HVAC system into account to accurately simulate the air temperatures, surface temperatures, humidity, etc. for the entire year. This was done in OpenStudio instead of running the entire simulation in Honeybee so we have full control of the energy model and have confidence in the accuracy of the results. This path also saves time in the long run by not having to re-run the simulation.

The results of the model was then re-imported into Honeybee to create the micro-climate maps for key days identified in the shading analysis. A point analysis of areas of interest were doing to show how many hours of the year exceeds the comfort limit.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

The results were visualized in Honeybee for Grasshopper using the energy modeling results from OpenStudio. A micro-climate analysis shows the standard effective temperature and predicted mean vote for key days when the sun hits the lobby glass from noon to 8pm for each of the glazing options. This graphic shows the movement of the direct sun onto the lobby floor where the hot discomfort will be felt.

The results where also visualized for key points in the lobby using a floodplot showing hourly results for the entire year in terms of PMV. Then discomfort hours were calculated for hot, warm, cool, and cold hours. The first point was on the receptionist desk where few occupants will be sitting there for the entire duration of the work day. The second and third points were on the benches located close to the glazing where people would not stay for long periods of time.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

From this study, we learned that although the hot discomfort can be intense when sitting in direct sunlight, only a few hours a day will the discomfort be felt and in locations where occupants will not be sitting for long periods of time. The study also showed that cold discomfort was more of the issue when considering different glazing options for a well shaded lobby in the San Francisco Bay Area that the IGU reduced significantly in the mornings.

This study helped the architect and the client make an informed decision about which glazing options they should use.

What is the impact of automated blinds on daylight performance by facade orientation?

PROJECT INFORMATION

Submitted by:  Alen Mahic, Amir Nezamdoost, Kevin Van Den Wymelenberg

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

ASHRAE Climate Zone: 4B

Building/Space Type: Office

Who performed the simulation analysis?  University

What tools were used for the simulation analysis? 

  • OpenStudio + Eplus

  • Design Builder + Eplus

  • Radiance + custom scripting

What tools did you use to create the graphic?

  • R

  • Adobe Photoshop

What phase of the project was analysis conducted?  Design Development, Construction Documentation

What are the primary inputs of the analysis?  Daylighting model, energy model, blinds operation algorithms, and AMY/TMY weather data.

What are the primary outputs of the analysis?  Blinds operation schedules, blind movement and occlusion metrics, Hourly daylighting performance, spatial Daylight Autonomy and Annual Sunlight Exposure, Lighting schedule, hourly energy analysis, Annual energy end use, Non-energy benefits of automated blinds, Blind life cycle cost analysis

PROCESS

List the investigations questions that drove your analysis process.

What are the effects of manual and automated blinds on annual and point-in-time daylight performance, and energy end use? How can we leverage the simulation data to predict occupant productivity and absenteeism? What is the payback period of automated and manual blind systems?

How was simulation integrated into the overall design process?

This study was conducted on a mid-rise large open plan office during late design development to investigate the relative differences in operation patterns and resultant annual energy and daylighting performance between manual and automated system. Designers and building owners were interested in reducing the cost of shading systems by opting for manual blinds where automation might not be necessary.

Per facade blind performance analyses showed that northwest, southwest and southeast facades would benefit from automated systems by managing direct sunlight, and reducing interior glare. The lowest ASE value is recorded in northeast zone with total 5.75% (minimum=4.91% for January) while the highest ASE percentage is reported in southwest zone with total 41.72% (maximum=68.13% on December), following by southeast (26.59%) and northwest (23.53%) zones.

These findings confirm that northwest, southwest and southeast facades do benefit from automated controls while the northeast facade showed ASE value, which is low enough where manual blinds would be sufficient to control direct sunlight and minimize interior glare. However, the monthly ASE values reveal that there is still an excess of direct sunlight during summer months (12.68%), and there is an equivalent annual average number of blinds movements between the northeast (2.76) and northwest facades (2.72).

Moreover, we found that the payback period of automated blind system is lower (9.5 years) when compared to the fully manual alternative (16 years).

How did you set up the simulation analysis and workflow?

For annual climate-based daylight simulation, detailed digital interior models and relevant massing of surrounding buildings and site context were generated in SketchUp. When the model was completed and verified for accuracy against floor plans; it was exported to the RADIANCE daylight simulation engine. Rcontrib ambient calculations were used in RADIANCE to compute light source contributions. Materials and blinds were assigned to digital models based on the information from construction specifications using CIBSE surfaces and reflectance charts and bi-directional scattering and distribution functions (BSDF).

The Energy Management System (EMS) in EnergyPlus was used to create blind occlusion schedules. In order to calculate indoor horizontal illuminance for daylight sensing lighting control and creating the lighting/dimming fractional schedule, an overlaying procedure between EnergyPlus thermal zones and RADIANCE daylight zones was needed. We started with (1) EnergyPlus model including all the thermal zones, (2) a script-based analysis grid was generated based on four daylight zones in RADIANCE, (3) EnergyPlus thermal zones overlaid onto the RADIANCE analysis grid in order to isolate the illuminance grid points that fall inside the EnergyPlus thermal zone. These can be averaged, or a single point specified to generate the fractional lighting schedule, and (4) reconciling zone overlays in R-studio. This is a critical step to ensure accurate energy simulation of the interactive effects of daylight sensing electric lighting control.

Shade schedules were generated according to the combination of exterior illuminance data obtained from RADIANCE, plus horizontal profile angle from EnergyPlus.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

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. 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.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The primary takeaway from this investigation confirmed our initial assumptions that the north-facing facades would require mitigation of direct sunlight, particularly in the summer and shoulder seasons where both sunrise and sunset hours introduce high potential of visual glare and heat gain discomfort. The daylighting analysis also provided data to inform the interior organization of partitions and work stations to minimize visual glare.

Additionally, 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.

What is the impact of facade WWR and U-Value on whole-building EUI

PROJECT INFORMATION

Submitted by: Ajit Naik

Firm Name: Baumann Consulting

Other contributors or acknowledgements: Kreuck + Sexton Architects, Thomas Phifer and Partners, Yuna Zhang & Oliver Baumann - Baumann Consulting

ASHRAE Climate Zone: 5A

Building/Space Type: Mixed Use (Retail)

Who performed the simulation analysis?  External Energy Consultant

What tools were used for the simulation analysis? 

  • Design Builder + Eplus

  • BuildSimHub

What tools did you use to create the graphic?

  • Excel

  • Adobe InDesign

What phase of the project was analysis conducted?  Conceptual Design

What are the primary inputs of the analysis?  WWR, U-Value

What are the primary outputs of the analysis?  Whole-Building (Site) EUI

PROCESS

List the investigations questions that drove your analysis process.

What is the required glazing thermal performance to ensure energy code compliance for different high-rise curtainwall window to wall ratios?

How was simulation integrated into the overall design process?

 

Prospective tenant requests for panoramic views prompted the high-rise project development team to prioritize a high window-to-wall ratio (WWR) of 80%. The current Chicago Energy Conservation Code, which references 2015 IECC (and so ASHRAE 90.1-2013) requires high-WWR projects to demonstrate energy performance better than a baseline with prescribed 40% WWR, U-0.42 window assembly U-Value, SHGC-0.40 window solar heat gain coefficient, and U-0.055 wall U-Value . To meet this goal, high-WWR projects must employ envelope or system performance improvements to offset increased glazing heat loss relative to the baseline.

The design team recognized that code compliance through demand reduction via envelope improvements could be more cost-effective than through subsequent consumption reduction via HVAC efficient systems, and wanted to understand the aesthetic façade design implications. Having selecting an aggressive SHGC-0.26 target at project outset to reduce required cooling equipment capacity, the project team integrated simulation to establish a feasible combined WWR and curtainwall assembly U-value target.

The study parametrically analyzed combinations of WWR and U-value ranging from 35-85% and U-0.22 – U-0.42. The study identified code baseline performance and WWR-U-Value pairs resulting in code-compliant performance without system efficiency improvements; this information informed preliminary façade cost estimation prior to MEP systems design. By testing and presenting a wide range of possibilities in response to a design query about specific design concepts, integrated simulation offered the design team feedback on future potential concepts in addition to those under consideration.

How did you set up the simulation analysis and workflow?

The simulation team set up a simplified core-perimeter energy model in DesignBuilder with all WWR, envelope (except SHGC-0.26 glazing), HVAC, lighting, and domestic hot water performance parameters set at code baseline efficiencies for systems described in the preliminary concept package. The team exported the IDF file from DesignBuilder and uploaded to BuildSimHub for cloud-based batch manipulation and execution. BuildSimHub functions created IDF files, executed simulations, and reported whole-building energy usage intensity (EUI) for each possible combinations of WWR and U-value spanning 35-85% at 5% intervals and U-0.22 – U-0.42 at U-0.02 intervals, respectively. Site energy performance was selected as the single simulation output for comparative analysis to understand the cost of meeting energy code via improved façade U-value alone.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

The preliminary strategy involved a simple table to list the curtainwall assembly U-value meeting code baseline energy performance for each WWR option tested. Though this method indicated maximum allowable insulation levels, it provided solely static value targets. The design team wanted to visualize the full range of compliant combinations with aesthetic context, and so employed a graphic-based strategy. An excel graph with curves depicting EUI vs WWR for multiple glazing assembly U-values allowed the design team to identify code baseline EUI and draw a horizontal line representing the cutoff for compliant (below the line) and non-compliant (above the line) WWR-U-Value combinations. Simultaneously, the original table was represented visually as colored overlays representing required U-Value on several of the team’s façade concepts. This dual strategy targeted both data-focused and experiential-focused design team members.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The combined visualization scheme proved to be an effective strategy for achieving an energy code compliant façade. Rather than developing and modeling efficiency packages to accommodate a given façade, parametrically analyzing the full set of façade design options maximized simulation impact and minimized design workflow and timeline interruptions. Visualizing the entire dataset together with colored façade options afforded the design team flexibility and the ability to quickly predict code compliance for preliminary concept modifications without waiting for further analysis results. The design team ultimately selected a façade option with 68% WWR that featured sections of floor-to-ceiling glass, ensuring high-quality views, and a feasible U-0.26 glazing assembly, ensuring energy code compliance prior to inclusion of high-efficiency MEP systems.

What is the impact of daylight study on building layout and envelope system?

PROJECT INFORMATION

Submitted by: Lei Fu - Senior Associate

Firm Name: KGM Architectural Lighting

Other contributors or acknowledgements: Francis Mekhail / Roxanna Bailey

ASHRAE Climate Zone: 3A

Building/Space Type: Office

Who performed the simulation analysis?  Lighting Designer

What tools were used for the simulation analysis? 

  • Rhino - DIVA

  • Grasshopper DIVA

What tools did you use to create the graphic?

  • Adobe Illustrator

  • Adobe InDesign

  • Adobe Photoshop

What phase of the project was analysis conducted?  Schematic Design, Design Development, Construction Documentation

What are the primary inputs of the analysis?  Daylight Autonomy and Maximum Daylight Autonomy

What are the primary outputs of the analysis?  Percentage of floor area that meets target illuminance with original design, compared with percentage of floor area that meets target illuminance with improved design

PROCESS

List the investigations questions that drove your analysis process.

Our project is located in a high density neighborhood and the main entrance is from the street to the northwest. The main facade is solid with random cuts, while the back facade, facing southeast, is floor to ceiling glazing. As a result, our primary challenge were: How can we reduce the glare from the southeast facade without cutting too much useful daylight? How can we utilize daylighting simulations to optimize the balance between quantity and quality of natural light? Which facade strategies are most effective in decreasing glare?

How was simulation integrated into the overall design process?

We performed radiance HDR rendered videos, daylight autonomy (DA) studies and maximum daylight autonomy (DA.max) studies. The DA and DA.max studies are the primary evaluation matrix we used to determine the lighting quantity and quality of the space. We determined that spaces with high DA.max values are over lighted and are not appropriate to be used as workspace, while spaces with low DA values are under lighted and require electric lighting to be used as workspace. We presented our findings to the architect, who then reconceptualized some aspects the southeast facade based on our daylighting analysis.

How did you set up the simulation analysis and workflow?

The simulation was set up through a series of steps starting with Revit. The Revit model, provided by the client, was exported to Rhino for analysis and simulation. Revit layers were cleaned up in Rhino making the application of materials through DIVA a more seamless process. The specific epw (weather data) was downloaded and applied to the "application" portion of the simulation. Nodes were then placed (distance of sampling node: .76, distance between calculation nodes: .45) on each floor. Materials were applied according to the architects direction and, finally, metrics were defined. Prior to the simulation, grasshopper was used to investigate the sun path over the site. We initiated a series of shadow studies and developed a facade strategy for testing. Metrics: Daylight Grid-Based > Climate-Based > Metric: Daylight Autonomy, Occupancy Schedule: 8 to 6 (office space), Target Illuminance: 300 Lux. We tested a series of facade strategies until we established target metrics that were satisfied.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

Output focused on a series of graphic representations paired with diagrams. We first defined DA and DA(max). We communicated the significance and impact on the exterior facade strategy and interior programming. Through the use of diagrammatic sections we were able to explain our approach and walked the design team through the output of each facade strategy. We relied heavy on the Adobe suite (Illustrator, Photoshop and InDesign) to create a clear narrative for presentation.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The simulation allowed us to determine which areas of the building should be reorganized programmatically based on daylight. It also enabled us to quantify the cost/benefit of proposed facade strategies on the quality of natural light in the space. With the information from our simulation, the architect reprogrammed the space to include more amenities on the southeast side of the building, where glare is most prominent. The architect is exploring facade strategies to help mitigate the glare we observed in our studies.

What is the impact of active radiant surfaces on environment thermal comfort?

PROJECT INFORMATION

Submitted by:  Stefan Gracik

Firm Name: Integral Group

Other contributors or acknowledgements: Ken Takahashi, Neil Bulger

ASHRAE Climate Zone: 2A

Building/Space Type: Entertainment/Public Assembly Lodging/Residential

Who performed the simulation analysis?  MEP Firm

What tools were used for the simulation analysis? 

  • OpenStudio

  • Eplus

  • IES-VE

  • Grasshopper Ladybug

  • Grasshopper Honeybee

What tools did you use to create the graphic?

  • Excel

What phase of the project was analysis conducted?  Schematic Design, Design Development

What are the primary inputs of the analysis?  building envelope properties

What are the primary outputs of the analysis?  HVAC load, standard effective temperature, predicted mean vote

PROCESS

List the investigations questions that drove your analysis process.

The client had very specific project requirements for this historic townhouse restoration. Essentially it's a new home hidden behind an existing townhouse facade. The new construction is to abut directly to the adjacent building on the east side, but the west side leaves a gap between the residence and the building next door. The historic facade is south of the new home, facing the street. A piece of cor-ten steel will be placed completely over the adjacent building on the west's party wall. The new construction building is placed to have a view of the cor-ten steel on the west side. Cor-ten steel rusts and weathers over time, so the residence is meant to have a clear view of this process, like an art gallery.

The owner requires the west side of this building to be constructed out of a single pane of unbroken clear laminated glass, so that the view of the cor-ten steel is not obstructed in any way. In addition, the owner has requested an HVAC system which is near silent and invisible to occupants. It was clear based on the project requirements that a hydronic radiant system was the most obvious way to achieve the silent and invisible requirement, but the west facade envelope load was estimated to be very high in this hot climate, even though it is highly shaded from the neighboring building. Analysis was done to prove that a radiant system could meet the HVAC load while still providing acceptable thermal comfort.

How was simulation integrated into the overall design process?

We built are loads model at the very beginning of design, since it was needed to provide input on if the proposed HVAC design had any chance of keeping the space comfortable. We were unable to suggest any architectural changes that would change the aesthetics of the space, though our modeling resulted in major changes to the structure. The opaque conduction loads were quite high without insulation, so we were able to communicate to the project team that the project goals would not be met without some way of reducing the envelope load.

The structure had to look like a solid concrete block from both the inside and outside, though we were able to get the structural engineer to embed insulation inside the concrete. Once we got this design change from the team, we found through the loads modeling that a radiant system could meet the load if both floor, ceiling, and east wall (the largest by far opaque wall) were embedded with hydronic radiant tubing.

Our work was mostly siloed since our main analysis goal was to estimate if the system would work, and we had very few levers to pull except embedded wall insulation and amount of radiant tubing, but our results had significant effects on the building design at an early stage.

How did you set up the simulation analysis and workflow?

The geometry was built using the SketchUp Openstudio plugin, since this gave us many options for geometry export and use with different tools. The geometry was initially imported into IES VE to perform the peak thermal loads analysis. IES VE is the preferred tool based on industry standards and the high quality visualization of loads simulations. The load results were compared against internal radiant load tools developed at our firm to estimate the radiant feasibility.

Once we had a path forward showing that radiant could work to meet the HVAC load, we needed to analyze the comfort in the space. Since the west wall is so highly glazed and the floorplate thin, there is a concern regarding radiant temperature of the glass

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

The results were successful in breaking down the major thermal load components for the project team. This helped the team understand that there was very little that could be done to reduce the load since the glazing on the project was non-negotiable. This conveyed the need to fit as many radiant panels as possible. Additionally the extra insulation is expensive to embed, but our graphs showed that it could make a big enough difference to allow the project to meet it's goals of a silent HVAC system, so it was put into the project. The thermal comfort maps provided additional insight to the team and further illustrated the need to run radiant through the east wall, since comfort was significantly improved with this addition. Overall the graphics were successful because they broke down complex topics into simple images that clearly conveyed how the goals could be met.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The analysis was extremely useful for establishing additional constraints on the project. In a way, the team already had an idea that the there would need to be substantial radiant tubing to meet the load, but the analysis quantified that it was even possible. Without the analysis, the team likely wouldn't have added the expensive insulation to the project. The team may have steered away from using radiant since the assumption may have been that the load was too high, and the project wouldn't have met its goals.

It also taught me that simple images can convey a great meaning regarding complex simulations. The thermal comfort maps in particular do a great job at showing the difference between options for clients, without having to see any numbers.

What is the impact of skylight geometry on museum daylighting?

PROJECT INFORMATION

Submitted by: Morgan Maiolie

Firm Name: Waggoner and Ball

ASHRAE Climate Zone: 2A

Building/Space Type: Education, Museum

Who performed the simulation analysis?  Architect - Design Staff

What tools were used for the simulation analysis? 

  • Ecotect

What tools did you use to create the graphic?

  • Excel

  • Adobe Illustrator

  • Adobe InDesign

  • Adobe Photoshop

What phase of the project was analysis conducted?  Conceptual Design, Schematic Design

What are the primary inputs of the analysis?  Material surface reflectance, time of day, date, location

What are the primary outputs of the analysis?  Daylight levels in footcandles

PROCESS

List the investigations questions that drove your analysis process.

Can we daylight a museum with sensitive pen & ink exhibits? Can we daylight a first floor from a skylit third floor? How does our solar resource on site drive programming decisions? Can we use daylight to add dramatic and / or dynamic lighting to the museum experience? Can we integrate views to the west and maintain control of daylight levels? What is the experiential effect of providing surface brightness with daylight in the space?

How was simulation integrated into the overall design process?

We used an iterative process of daylight analysis to test myriad design questions.
One question was whether we could get useful daylight on three floors of the project. We were renovating a 3-story brick building where windows were feasible only on one side and we were curious about whether we could wash the windowless back wall with daylight from the skylit floors above. Daylight modelling showed this required a larger floor area loss than the project could support, so we focused our design on bright, daylit spaces on the top floors. The two lower floors were improved by an atrium after a separate series of daylighting analyses. Another early design question was whether we could hit an 18 fc / 4000 fc / yr target for sensitive pen and ink works. Analysis showed we could, but wouldn't like the aesthetic and financial result, which changed our space programming for those works.                                            
Moving through schematic design, we used daylight analysis to determine the shape and orientation of skylight monitors, as well as the window coverings and material surface brightness of top floor museum spaces. We decided on different daylighting strategies for the two top floor rooms based on their differing geometries / programs with a light scoop and sawtooth roof represented.                                                                                                                  
The daylight modeling process was integrated with the design process in that it was carried out by a designer on the team and presented with each formal design idea. We presented analysis to the client when we discussed design options.

How did you set up the simulation analysis and workflow?

We created a model from scratch in Ecotect and used Radiance to analyze it. We then exported graphic results and added information for presentation with Illustrator and Indesign.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

We found it wasn';t intuitive to read the typical red-to-blue outputs from Ecotect and vizualized daylight intensity with a white-to-yellow-to gray palette instead. This helped our client and design team better understand the experiential difference between bright and dim areas. We also relied heavily on perspective outputs paired with daylight analysis grids to provide an experiential understanding of the different design decisions (while always mentioning that the way these show on screen isn't a direct relationship with reality). Finally, we took daylight readings at museums the design team and client had visited and compared these with our results to give them a real-world baseline.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

We really didn't know how we could use daylight within the space - perhaps because of our relative newness to daylight design - so analyzing questions like whether an opening between a daylit space and the floor below could wash a wall sufficiently was really useful. Quatifying our daylight limits for museum exhibits was invaluable because the museum had only used electric lighting in their exhibits before. Attaching numbers to the different works (pen&ink versus oil paintings versus sculpture) helped us all understand programming for these works and their ability to connect with daylight.

What is the impact of facade geometry on space daylight distribution and heating/ cooling load?

PROJECT INFORMATION

Submitted by: Xiaofei Shen

Firm Name: AECOM

Other contributors or acknowledgements: Aman Singhvi, Jason Vollen

ASHRAE Climate Zone: 4A

Building/Space Type: Office

Who performed the simulation analysis?  Architect - Internal Sustainability Personnel

What tools were used for the simulation analysis? 

  • Grasshopper Ladybug

  • Grasshopper Honeybee

  • Rhino-DIVA

What tools did you use to create the graphic?

  • Adobe Illustrator

What phase of the project was analysis conducted?  Schematic Design

What are the primary inputs of the analysis?  Window Geometry, Shading Geometry

What are the primary outputs of the analysis?  Daylight Distribution, Solar Radiation, Heating and Cooling Load

PROCESS

List the investigations questions that drove your analysis process.

What is optimum Window/ Wall Ratio for Daylighting? What is the reduction in annual daylight based on external shading systems? How fenestration and shading design holistically affect the building performance? How to obtain the most optimal facade trade-offs for balanced daylight and energy?

How was simulation integrated into the overall design process?

The study was conducted when the design team reached out for quantifiable design guidelines to inform the facade conceptualization process during the schematic design phase. The aim was to achieve best performance of the space with respect to daylight and energy.

The number of design iterations are massive due to multiple variables involved. More than 200 solutions were generated with various window widths, height and location as well as shading types (horizontal or vertical) and shading depth. Moreover, the performance related objectives, including minimizing glare area, minimizing under-daylit area, minimizing solar irradiation on the glazing, and minimizing heating/ cooling loads are all conflicting to each other due to their mutually exclusive dependencies.

An automatic Multi-Objective Optimization was utilized for rapid exploration of options and optimization at early stage of design for quick decision making.

How did you set up the simulation analysis and workflow?

A typical south-faced bay space in an office building located in DC with a total floor area of 2,500 sf was modeled using Rhinoceros 3D modeling software and Grasshopper plugin. The pre-determined variables, such as windows and shadings, were parametrically constructed. Parameters controlling window dimensions, type of shading and shading depth were setup to vary within predetermined limits set based on design constraints.

The initial iteration was connected to DIVA daylight simulation software. Sensors were placed at a 12-inch square grid. Two daylight simulations were carried out to get Illuminance levels at 9 am and at 3 pm at the design day (Sep 21st). Colored Illuminance maps and numeric values of lux levels on each floor were outputted. The iteration was also connected to DIVA thermal analysis software. Room boundaries were set up as typical climate zone 4A materials. Heating and cooling loads related to the facade elements were simulated and recorded. 

Colibri for Grasshopper was selected to automatically trigger the simulations and save both inputs and outputs for this experiment. The recorded values were connected to a customized script in Grasshopper for further result sorting and finding.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

The building performance simulation is traditionally time-consuming and the report is usually hard to understand with redundant information showed. To visualize such a complex process of multi-objective optimization, variable clusters as well as energy data was an equally challenging task as well to represent in one or more graphics. The resulting visual simplified the raw data in Adobe Illustrator with minimized information presented but in a more graphic way, such as variables with 3D geometries and objectives with 3D bar charts. This visualization made it considerably easier for clients and designers to understand the results as compared to just stats and numbers.

Additionally, multiple recommendations were provided with their performance values listed through a filtering and sorting process. Preferred options were selected based on constraints and bios after discussion with the designers. A more detailed 3D section was also provided for a closer look of the potential development of the selected solution.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

This evolutionary trade-off analysis tool is the best way to provide performance feedback for design decision support in a quicker and comparatively more accurate way, especially at the beginning of the conceptual stage when there are multiple iterations generated from the parametric models. By managing the Window/Wall Ratio, shading dimensions and wall construction types, the rich-data multi-task methodology were utilized for better performance on daylighting, thermal comfort, energy load and cost, making the optimized facade result more reliable.

The user-friendly graphics made the design decision making more efficient. Multiple stakeholders could get involved into the discussion when the complex data was well visualized.

Findings from this analysis: the optimized modular design of the facade with Window/ Wall Ratio 50% and 6’ horizontal shadings properly mediates summer solar exposure but still ensures daylight penetrating reducing both lighting load and heating/cooling load. Glare control and reduced contrast ratio improves the visual comfort. The facade is further integrated with building systems. For example, outdoor air is pre-cooled and pre-warmed through the localized DOAS units and Heat Exchangers embedded in the modules.

What is the impact of various retrofit strategies on energy saving?

PROJECT INFORMATION

Submitted by: Biwen Sun, Yingjia Wang

Other contributors or acknowledgements: Made Bagus Yudha, Nima Maghzi

ASHRAE Climate Zone: 5A

Building/Space Type: Mixed Use (Office, Lab, Classroom)

Who performed the simulation analysis?  University Student

What tools were used for the simulation analysis? 

  • eQuest

  • Climate Consultant

  • Grasshopper Ladybug

  • Grasshopper Honeybee

  • Computational Fluid Dynamics (CFD)

What tools did you use to create the graphic?

  • Excel

  • Adobe Photoshop

What phase of the project was analysis conducted?  Post Occupancy

What are the primary inputs of the analysis?  Equipment loads

What are the primary outputs of the analysis?  Electricity consumption, gas consumption

PROCESS

List the investigations questions that drove your analysis process.

How to build an energy simulation model close to the actual energy consumption of the existing building? Which zone in the building cost most energy? How can we improve the daylight environment in the building? How can we improve the ventilation? What are the possible strategies to improve the performance of the current building? How much energy can be saved possibly?

How was simulation integrated into the overall design process?

The energy simulation is to evaluate the current energy consumption of the building. The result can be used as the reference for the future renovation. The target is intended to cut down about 30% of total energy consumption. To achieve that, several strategies are simulated in the software to improve ventilation, indoor lighting environment, and building envelope performance. Based on the baseline model, improvement strategies are applied as EEM in eQuest, to show how much energy each strategy would possibly save. All the analysis and strategies are presented to the Facilities and Services Department of the University. They gave opinions on whether those strategies are feasible. Then, improvements are modified based on their advice.

How did you set up the simulation analysis and workflow?

The thermal load and occupancy schedule are generated into graphics through Grasshopper Ladybug and Honeybee. CFD is also a good tool to show the ventilation result in graphics. Other data like total electricity and energy consumption can be transferred into bar charts by using excel. The graphics clearly show the problem area and how much the performance would be possibly improved with the proposed strategies. With the graphics, the audience could understand the process easily and get a direct image of the outcome after renovation.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

The thermal load and occupancy schedule are generated into graphics through Grasshopper Ladybug and Honeybee. CFD is also a good tool to show the ventilation result in graphics. Other data like total electricity and energy consumption can be transferred into bar charts by using excel.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

The investigation is a great opportunity to know the complexity of the building and understand the depth of involvement required to make accurate building model. Also this process allows us to apply our current knowledge to test the feasibility of design strategies.

Flood Protection Labyrinth

passive.jpg

PROJECT INFORMATION

Submitted by: 

Jacob Dunn

Firm Name:

Eskew+Dumez+Ripple

Other contributors or acknowledgements:

Shawn Preau - Eskew+Dumez+Ripple Project Manager

Ian OCain - Eskew+Dumez+Ripple Desigern

Z Smith - Eskew+Dumez+Ripple Director of Building Performance and Sustainability

Project Name:

Southern-Style Labyrinth

ASHRAE Climate Zone:

2A

Building/Space Type:

Gym

Who performed the simulation analysis? (select all that apply)

Architect - Internal Sustainability Personnel

What tools were used for the simulation analysis? (select all that apply)

OpenStudio + EnergyPlus

What tools did you use to create the graphic?

Excel / Adobe Indesign

What phase of the project was analysis conducted? (select all that apply)

Design Development

PROCESS

List the investigations questions that drove your analysis process.

Is the air temperature in the thermal mass “labyrinth” cooler in the summer and warmer in the winter than the ambient outside air?  How much so?  How much heating and cooling energy could be saved by drawing air from the labyrinth using exhaust fans in the gym?

How was simulation integrated into the overall design process?

New projects in New Orleans typically share an interesting condition where they are raised 3’ up above the base flood elevation as required by FEMA. This creates a crawlspace condition which, when filled with concrete foundation walls, is similar to that of a thermally massive labyrinth. However, thermal mass passive strategies typically aren't recommended for the New Orleans climate given its small diurnal swing, but drawing air from the crawlspace could still save energy. The question is, how much?

The project explored this idea during the design development phase, and designed a passive/active ventilation system that uses exhaust fans in the gymnasium to pull air through the crawlspace from underneath the bleachers through motorized dampers. The space is zoned separately from the rest of the HVAC equipment of the project, houses activities with wider comfort zones, and utilizes robust materials to withstand higher humidity levels. Simulation was used to understand how just how much energy this type of passive strategy could save.  

How did you set up the simulation analysis and workflow?

One goal of the analysis was to get an estimate of savings without using CFD, as the firm at the time did not have this capability.  Thus, EnergyPlus was used for the simulation process.  While the software is capable of providing bulk airflow calculations through its Airflow Network post-processing module, the team took a more simplified route.  First, the team explored the difference in temperature between the crawlspace and outside air to determine if the magnitude was large enough to warrant further exploration.  A rough air change per hour calculation was performed based on the inlets and outlets of the raised, vented crawlspace.  This number was then input into EnergyPlus's zone:infiltration object, which is then subject to pressure coefficients and wind patterns of the .epw file during annual simulation.  Finally, the energy model was modified to take natural ventilation air from the crawlspace zone with the as-designed HVAC still available to makeup any load difference or to condition the space when temperature or humidity conditions were out of range.  This provided a quick and adequate workaround to test the natural ventilation strategy without the use of CFD. 

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

While visualizing the data in this case study was fairly straightforward, the sequencing of when the data was communicated was key to pushing the design forward.  The initial question was a gut check to see if there was a substantial difference between the temperature of the crawlspace and the outside air given the climate.  Once quantified, we showed the data to the team in 3 ways. 

1) histogram – this chart provided a good way to, at a glance, visually represent the difference in temperature bins between the pre and post labyrinth condition. 

2) Line charts - The line charts aimed at visually representing the magnitude of temperature difference over time, but in a slightly less abstract way than the histogram.  The annual chart is shown alongside a weekly profile, the former showing the sheer difference throughout the year, while the latter allows you to see the daily swing between the two approaches.

3) Value call outs – neither the histogram or line charts provides a single quantification of the effect of the labyrinth, so some analysis of the data was warranted.  The % of hours warmer and cooler in the labyrinth during occupied times was calculated, along with the % reduction of hours of above 90 deg F.   

This combination approach was successful in showing both the magnitude of difference between the two approaches, along with how often the labyrinth condition performed better than the outside air.  The numbers showed that the strategy was promising, so we moved forward on defining the natural ventilation model and HVAC system to quantify the effect on both heating/cooling energy and overall energy.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

We learned that even in this climate’s hot and humid context, this type of pseudo-labyrinth could be promising from an energy efficiency standpoint. The simulation results showed that 12% of the heating and cooling energy could be saved using this type of passive measure, or 6% of the overall energy.  This was a large enough difference for the design team to decide to move forward with integrating this move into the envelope, HVAC design, and mechanical sequence of operations.  Advocating for this strategy to the MEP and owner took some convincing, so having the numbers, and more importantly the support of the design team, was critical in carrying the design intent into the construction documents.  Additionally, this approach shows a synergy between resilience (flood protection) and energy efficiency (labyrinth) – a relationship often thought to be in opposition to one another.

California Shading Scrim Study

PROJECT INFORMATION

Submitted by: 

Jacob Dunn

Firm Name:

ZGF

Other contributors or acknowledgements (optional)

Jason Essel - ZGF Project Architect

Project Name:

California Shading Scrim Study 

ASHRAE Climate Zone:

3C

Building/Space Type:

Education

Who performed the simulation analysis? (select all that apply)

Architect - Internal Sustainability Personnel

What tools were used for the simulation analysis? (select all that apply)

Grasshopper Honeybee

What tools did you use to create the graphic?

Design Explorer / Excel

What phase of the project was analysis conducted? (select all that apply)

Design Development

PROCESS

List the investigations questions that drove your analysis process.

Can we increase the openness of our shading screen and stay under radiant cooling capacity targets?  What combinations of other shading screen elements are needed to stay under the target?

How was simulation integrated into the overall design process?

This study was conducted on the project during early design development after the mechanical engineer warned the design team that increasing the window to wall area ratio of the façade to 100% would increase the cooling load beyond the proposed radiant panel system's capacity.  When a perforated shading screen was proposed, the mechanical engineer stipulated that a 40% openness factor would be sufficient to prevent the costly impact increasing the size of the DOAS system's capacity and ductwork to handle the increased load.   
A 40% open screen was deemed to be too occluded by the design team from an experiential standpoint, so they worked with an internal sustainability team member to conduct a more detailed study of the screen and its components.  The following additional variables beyond screen openness were identified: 

• Shading structure fin depth and distance from the facade
• Shading structure fin rotation
• Catwalk presence and depth
• Frit coverage
• Window to wall area ratio

Ranges of values for each variable  were defined and 3 rounds of analysis and design iterations were tested.  The first round included analyzing how much over the target load capacity openness factors greater than 40% produced.  This effort produced the amount of load that various other measures had to mitigate.  In the next two cycles of iterations the design team would draw up various screen designs and the energy modeler would test them across the full range of possibilities. 

How did you set up the simulation analysis and workflow?

The first step in the process involved reaching out to the mechanical engineer to get the load breakdowns for a selected worst-case scenario west-facing zone.  The solar load was extracted from the load data sheets and used to validate a separate model created using a Grasshopper Honeybee workflow.  Once the new model produced a similar solar load, the team could be confident moving forward to test different solutions using that model's outputs.   

Given only a single zone was isolated for analysis, the modeler re-created the geometry from scratch in Rhino.  Additionally, only solar load outputs were needed so the energy model could be defined using minimal Honeybee components and relying on defaults for things like HVAC systems, envelope components (other than the glass), and internal gain inputs/schedules.  To simulate the openness factor of the perforated screen, the EnergyPlus window shade generation Honeybee component was used, which allows detailed blind slat modeling and control.  Various slat depths and spacing were defined as a proxy for openness factor, while the other geometric components of the screen (structural fins, catwalks, etc.), were modeled directly in Rhino and parametrically controlled using Grasshopper.  The solar load from each run was extracted from the .eio output using Honeybee components, so each simulation produced a single output used to compare the effectiveness of each iteration.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

Originally, the modeler used a basic bar chart/table in Excel to report the cooling loads of each iteration during pinups about the screen with the design team.  The target load was identified as a line across the charts and a table below the bars identified the inputs for each run.  The widths of the cells lined up with the bar widths, while the rows represented each type of input and its range value.  While this method clearly showed which iterations met the criteria, it was difficult to keep track of what combination of inputs each run included.  Therefore, a completely different method was used to communicate with the design team at the end of this whole process.   

The data was massaged into a .csv file and uploaded in a format that Design Explorer 2.0, a free online data viewer, required to produce an interactive parallel coordinate diagram.  In this type of graphic, each column represents either an input or output and it's constituent range of values.  The user can then drag regions around each column to filter simulation cases according to different input combinations or output values.  One added benefit of the Design Explorer 2.0 interface is that images can be associated with various simulation cases, so a visual representation is shown alongside the data.  This feature is critical in both testing the concepts aesthetically and keeping track of input combinations, which are shown as a line on the coordinate diagram and listed once a case is clicked on. 

To view the interactive parallel coordinate diagram, click here.

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

Visualizing the data in an interactive parallel coordinate diagram facilitated a much more interactive and engaging way for the designers to navigate the parameter space of the study.  It was very easy for them to filter the solar load values for cases below the roughly 9,000 Btu load target, and to then filter the remaining options by desirable inputs and design considerations.  Nothing new or novel was discovered as part of this investigation, rather, the effort produced a range of about 8 input combinations that met the design criteria.  This gave the designers ample flexibility in designing the screen to meet both aesthetic and performance considerations.  The results showed that to achieve the desired 60% open perforated screen, the window to wall area ratio had to be reduced by about 25%.  Alternatively to the keep the fully glazed façade, either 50% frit coverage was needed, or rotated structural fins were necessary, both of which weren't desirable from either an aesthetic or view preservation standpoint.  In the end, the design team chose a spiral wire screen (an openness factor of 65%) and reduced the window to wall area ratio while adding a horizontal catwalk for maintenance and shading purposes.