Upcoming SlideShare
×

# Thesis Midterm Presentation

2,767 views
2,698 views

Published on

Benchmarking Daylight Autonomy

Published in: Technology, Art & Photos
4 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

Views
Total views
2,767
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
0
0
Likes
4
Embeds 0
No embeds

No notes for slide

### Thesis Midterm Presentation

1. 1. Occupant Behavior MDesS Thesis Midterm | Cynthia Kwan | Advisor Christoph Reinhart |
2. 2. Current Dominant Daylight Metrics Daylight Factor method prevalent in the building industry  existed for a long time, easy to use, also used in LEED certification Shortcoming of the Daylight Factor method:  Only considers worst case scenario (illuminance of overcast sky)  Task surface illuminance : sky illuminance  Assumes the identical sky conditions of same latitude locations (ignores sunny or cloudy climate) Daylight Factor Calculation DF = 100 * Ein / Eext Ein = Inside illuminance at a fixed point Eext = Outside horizontal illuminance under an overcast (CIE sky) or uniform sky. http://www.learn.londonmet.ac.uk/packages/clear/visual/daylight/analysis/hand/daylight_factor.html MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
3. 3. Climate-based Metrics Arguments for climate-based metrics:  Previously mentioned limitations of DF as a design metric  Climate-based metrics allow to custom-tailor the analysis with regards to local climate and occupant use of a space MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
4. 4. Climate-based Metrics – Daylight Autonomy  Uses Radiance as the calculation engine  Physically correct backward ray-tracing method http://www.contrib.andrew.cmu.edu/~yihuang/ls/raytrace.jpg  Light source = imaginary sky dome divided into 145 segments  Illuminance of each patch defined by direct & diffuse irradiances in the weather data file (Perez Sky Model,1993)  Sky conditions can be converted down to 1-minute time step  min. time-resolution of task surface illuminance simulation Image source: Reinhart, Christoph F. (2006). Tutorial on the Use of DaySim Simulation for Sustainable Design. Ottawa, Canada: National Research Council - Institute for Research in Construction. P.25. MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
5. 5. Climate-based Metrics - Daylight Autonomy Barrier towards the use of climate-based metrics:  Access to simulation software (\$\$ & initial training)  Time  A lack of published data as to what constitutes a high, medium or low daylight autonomy level in various space types. MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
6. 6. Research Question What constitutes a „good‟ Daylight Autonomy distribution in a Gymnasium? MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
7. 7. Methodology  Compare simulation results of a series of real spaces  Ecotect models of real world projects created by translucent panel manufacturer Kalwall  170+ models including atria, classrooms, offices, gymnasia, museums and libraries.  Select one category of spaces (gymnasia) for detailed study • Reasonable number of cases (30) available • Nature of activity requires certain controlled lighting characteristics which can be provided by daylight • Use of active shading devices uncommon  eliminate one uncertainty factor in the simulations and study  Base evaluation on existing IESNA standard MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
8. 8. Methodology 1. Quality and consistency check for the selected 30 Ecotect gymnasiums models (take model as is assuming no obstruction) 2. Set up photo sensor grid in Ecotect 3. Export model to DaySim and load in corresponding weather EPW 4. Run annual illuminance simulation 5. Run Daylight Autonomy analysis with target illuminance advised in the IESNA RP-6-01 Sports & Recreational Area Lighting standard 6. Calculate Uniformity Ratio (UR; max/min illuminances) by Excel spreadsheet functions 7. Compile graphs for DF, DA and UR for all spaces and derive the average numbers 8. Each model (space) will be scored according to its performance rank in each category (or a credits matrix) 9. Account for and discuss high-scoring and low-scoring designs. MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
9. 9. IESNA RP-6-01 Sports & Recreational Area Lighting Criteria for basketball facility:  Target Illuminance 500 lux for Class III (Some provisions for spectators) 300 lux for Class IV (No provision for spectators)  Uniformity ratio (max/min illuminance) ≤3.0 for Class III (Some provisions for spectators) ≤4.0 for Class IV (No provision for spectators)  Glare avoidance MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
10. 10. 30 Gyms in the US and Europe Alameda, CA Muncie, IN Gloucester, UK Bedford, MA Omaha, NE Gloversville, NY Berea, OH Palmdale, CA Greensburg, PA Bronx, NY Ravenna, OH Holland Patent, NY Brownsville, VA Scottsdale, PA Lake Los Angeles, CA Clouston, WV Spokane, WA Largo, FL Colbert, WA Syracuse, NY Lewiston, NY Elgin, Scotland, UK Syracuse, NY Maldegem, Belgium Fort Wayne, IN Utica, NY Mars, PA Foxburg, PA Walton, NY Maywood, CA MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
11. 11. 30 Gyms in the US and Europe (Made by Google My Maps) MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
12. 12. 30 Gyms in the US and Europe  Gymnasia of similar sizes from across the US and Europe Greensburg, PA Colbert, WA Brownsville, VA Bedford, MA Area = 784 m2 Area = 534 m2 Area = 699 m2 Area = 772 m2 Class IV Class IV Class IV Class III Largo, FL Alameda, CA Gloucester, United Kingdom Omaha, NE Area = 603 m2 Area = 1741 m2 Area = 589 m2 Area = 2694 m2 Class IV Class III Class IV Class III Bronx, NY Berea, OH Maldegem, Belgium Area = 1151 m2 Area = 502 m2 Area = 2440 m2 Class III Class IV Class III
13. 13. 30 Gyms in the US and Europe  Gymnasia of similar sizes from across the US and Europe Clouston, WV Elgin, Scotland Fort Wayne, IN Foxburg, PA Area =1397 m2 Area = 1440 m2 Area = 6537 m2 Area = 731 m2 Class III Class III Class III Class III Mars, PA Scottsdale, PA Muncie, IN Utica, NY Area = 715 m2 Area = 863 m2 Area = 2901 m2 Area = 2983 m2 Class IV Class III Class III Class III Gloversville, UK Holland Patent, NY Walton, NY Area = 1577 m2 Area = 698 m2 Area = 850 m2 Class III Class III Class III
14. 14. Model Consistency Check  Roof depth = 450 mm  Wall thickness = 150 mm  Material definitions (especially translucent and transparent) • KalwallTranslucent06,08,12,15,20 validated; 30 approximated • Clear40, GenericDoubleGlazing72 • CeilingForLighting = 80% reflectance • WallForLighting = 50% reflectance • FloorForLighting = 30% reflectance  Material naming consistency, export library  Set up visualization camera for Radiance rendering (Ambient bounces 4; Ambient division 1000) MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
15. 15. Example Simulation  Sensor point grid set-up: Work plane defined at 1.6m (typical eye level) Grid extend = 15.24 m x 28.65m (full basketball court) Node points = 7 x 13 = 91 points (~2x2 m grid) MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
16. 16. Simulation Parameters  Annual illuminance simulation parameters (for DaySim): Ambient bounces 7 Ambient division 1500 Ambient sampling 100 Ambient accuracy 0.1 Ambient resolution 300 Direct Threshold 0 Direct sampling 0 (Other parameters kept at default) MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
17. 17. DA Analysis Parameters  Daylight Autonomy (DA) analyses parameters: Time frame: 07.00 - 18.00 (time step = 60 minutes) No lunch breaks Minimum illuminance level: 500 lux (Class III) 300 lux (Class IV) MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
18. 18. Uniformity Ratio  Import ILL file into Excel spreadsheet  Find minimum and maximum lux for each time step (each row)  Find ratio of max/min  Find % of time the required ratio is met (when daylight possible) MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
19. 19. Results – DA and Mean DA MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
20. 20. Results – DA vs Daylight Factor >2% MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
21. 21. Results – DA vs Uniformity Ratio % of time meeting Uniformity Ratio MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
22. 22. High-scoring Designs DF ≥ 2% = 12% Mean DA = 92.5% Mars, PA Uniformity met = 99.6% DF ≥ 2% = 100% Mean DA = 89.5% Uniformity met = 96.8% Berea, OH MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
23. 23. Low-scoring Designs DF ≥ 2% = 0% Mean DA = 22.65% Omaha, NE Uniformity = 75.84 % DF ≥ 2% = 0% Mean DA = 33.5% Scottsdale, PA Uniformity met = 50.7% MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
24. 24. Data Analysis % of time meeting Uniformity Ratio MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
25. 25. Data Analysis MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
26. 26. Data Analysis – Next Steps Each case could be fully accounted for Discuss extreme cases (e.g. seemingly mismatched DA, DF and UR) Point Scoring System using multiple (weighed) criteria:  DA, DF and UR calculated  Continuous DA, i.e. a sensor illuminance reading will still receive partial credits even if it falls short of the target lux level (300 or 500) e.g. if a sensor only receives 240 lux when the target is 300 lux, it still receive partial credit = 240/300 = 0.8  Hunt probability – likelihood that a user will flip on the light switch based on the initial assessment of the space at point of entry (based on office occupancy behavior  transferable to gym?) MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
27. 27. Discussion Questions  Applying metrics geared towards electrical lighting to daylighting  Significance of weather data  (Use of climate analyzer)  Metrics displayed a wide range of results - Average DA range 39.5 – 70.9% - Overall Average DA = 55.2% - Average 67.9% of time meeting uniformity ratio  Use of Hunt probability (office user pattern applied to gyms)  Use of continuous DA (rationale described in methodology)  Rules of weighing score criteria MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
28. 28. Discussion Questions Preliminary design guidelines:  High-scoring designs for Daylight Autonomy + Uniformity - Continuous ribbon windows at the crown (clerestory) - Skylights in the center  Adding side windows enhances overall light level, but hurts uniformity  Climate-responsiveness  Environment – obstructions not included in this study MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
29. 29. Deliverables  A paper describing the methodology, results and discussions of this study, with detailed case studies for each of the 30 gymnasium in the appendix  A standard for what is a “good” Daylight Autonomy and Uniformity Ratio for Gymnasia  [Design guidelines on how to achieve good daylighting in gymnasia to meet DA levels advised by the study] MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
30. 30. References Bourgeois, D. , C.F. Reinhart & G. Ward (2008) A Standard Daylight Coefficient Model for Dynamic Daylighting Simulations. Building Research and Information, 36(1), 68-82. Galasiu, A.D. & C.F. Reinhart (2008). Current Daylighting Design Practice: A Survey. Research & Information, 36(2), 159-147. doi: 10.1080/09613210701549748. Guzowski, Mary (2000). Daylighting for sustainable design. New York : McGraw-Hill. IESNA Sports and Recreational Area Lighting Committee (2001). IESNA RP 6-01:Recommended Practice for Sports and Recreational Area Lighting. New York: IESNA. Lee , E.S. & S.E. Selkowitz (2006). The New York Times Headquarters daylighting mockup: Monitored performance of the daylighting control system. Energy and Buildings, 38, 914–929. Perez R., Seals R. and Michalsky J. (1993) All-weather model for sky luminance distribution – preliminary configuration and validation. Solar Energy, 50, 235-245. Reinhart, Christoph F. & Valerio M. LoVerso (2007). Rules of Thumb Based Design Sequence for Diffuse Daylighting, Lighting Research and Technology. Reinhart, Christoph F. & Marilyne Andersen (2006). Development and validation of a Radiance model for a translucent panel. Energy and Buildings, 38, 890–904. Reinhart, Christoph F., John Mardljevic & Zsck Rogers (2006). Dynamic Daylight Performance Metrics for Sustainable Building Design. Leukos, 3(1), 7-31. Reinhart C. F. & S. Herkel (2000) The simulation of annual daylight illuminance distributions – a state-of-the-art comparison of six RADIANCE-based methods. Energy and Buildings, 32, 167-187. Reinhart, Christoph F. (2006). Tutorial on the Use of DaySim Simulation for Sustainable Design. Ottawa, Canada: National Research Council - Institute for Research in Construction. Walkenhorst, O., J. Luther, C. Reinhart & J. A.Timmer (2002). Dynamic annual daylight simulations based on one-hour and one-minute means of irradiance data. Solar Energy, 72(5), 385-395. MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |
31. 31. Thank you! MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |