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مقایسه پیکربندی‌های لوور به منظور بهینه سازی نور روز و مصرف انرژی در شهرهای بندرعباس و تبریز | ||
| نشریه هنرهای زیبا: معماری و شهرسازی | ||
| دوره 26، شماره 3، آذر 1400، صفحه 71-84 اصل مقاله (1013.73 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jfaup.2022.333965.672718 | ||
| نویسندگان | ||
| نریمان رفعتی1؛ هانیه صنایعیان2؛ محسن فیضی* 3 | ||
| 1کارشناس ارشد معماری پایدار، گروه معماری، دانشکده معماری و شهرسازی، دانشگاه علم و صنعت ایران، تهران، ایران. | ||
| 22استادیار گروه معماری، دانشکده معماری و شهرسازی، دانشگاه علم و صنعت ایران، تهران، ایران. | ||
| 3استاد گروه هنرهای کاربردی (منظر،) دانشکده معماری و شهرسازی، دانشگاه علم و صنعت ایران، تهران، ایران | ||
| چکیده | ||
| افزایش عوامل موثر بر طراحی لوور، روند طراحی آنها را پیچیده‌تر از قبل کرده است. نه تنها عوامل موثر بر زیبایی لوور بلکه عوامل دیگری مانند مصرف انرژی و آسایش بصری نیز باید در روند طراحی لوور مورد نظر قرار گرفته شود. این تحقیق با در نظر گرفتن تاثیرگذارترین عوامل در طراحی لوور، گزینه‌های طراحی مناسب برای اقلیم‌های مورد مطالعه را نیز معرفی میکند تا بتوانیم بهترین گزینه‌های طراحی سازگار با هر اقلیم از نظر مصرف انرژی و دریافت نور روز را شناسایی کنیم. در این پژوهش میزان مصرف انرژی و آسایش بصری را بر اساس روش‌های کمی ارزیابی خواهیم کرد. به این منظور ابتدا پارامتر‌های متغیر عناصر تشکیل دهنده‌ی لوور را تعریف میکنیم و در قدم بعدی شهرهای بندرعباس و تبریز انتخاب شده‌اند تا شبیه سازی میزان مصرف انرژی و دریافت نور روز با در نظر گرفتن لوورها در این شهرها انجام بگیرد. نهایتا با توجه به اینکه چندین پاسخ ممکن بدست آمده است، با استفاده از نمودار پارتو به بهترین جواب‌های گرفته شده از بهینه‌سازی که از الگوریتم ژنتیک بهره میبرد، دست پیدا میکنیم. بنابراین، براساس طراحی‌های بدست آمده بهترین گزینه‌ی ممکن را انتخاب میشود تا در شهر و اقلیم انتخاب شده به‌کار گرفته شود. | ||
| کلیدواژه‌ها | ||
| آسایش بصری؛ شاخص‌های روشنایی؛ لوور؛ طراحی پارامتریک؛ الگوریتم نخبه‌گرای مرتب‌سازی ژنتیکی غیر‌مغلوب | ||
| عنوان مقاله [English] | ||
| Comparison of different louver configurations for daylight and energy optimization in Bandar Abbas and Tabriz | ||
| نویسندگان [English] | ||
| Nariman Rafati1؛ Haniyeh Sanaieian2؛ Mohsen Faizi3 | ||
| 1Master of Sustainable Achitecture, Department of Architecture, Faculty of Architecture and Urban Planning, Iran University of Science and Technology, Tehran, Iran. | ||
| 2Assistant Professor, Department of Architecture, Faculty of Architecture and Urban Planning, Iran University of Science and Technology, Tehran, Iran. | ||
| 3Professor, Department of Applied Arts )Landscape), Faculty of Architecture and Urban Planning, Iran University of Science and Technology, Tehran, Iran. | ||
| چکیده [English] | ||
| Selecting a proper daylighting system can help minimize artificial lighting, control energy consumption and, consequently downsize air-conditioning systems. This issue becomes more critical when building facades are mainly glazed. In fully glazed facade, daylighting systems perform sufficiently enough in terms of solar protection, daylighting harvesting, and interior heat gain. Simultaneously, they can block the daylight entity, cause the need for artificial light and prevent the winter solar radiation. Increasing the number of influential factors in louver design will complicate their design processes. Louvers are made up of numerous horizontal, vertical, or sloping slats. Louvre properties with complex features and several parameters, such as tilt angle and solar angle of incidence rotation angle, shape, size, configuration, and color of slats, all impact glare and visibility and build energy efficiency Not only several parameters of the louver device but also the variety of analyzing factors such as energy performance and visual comfort affect the design process. Overlooking each setting has a significant effect on optimization results. Due to this complexity, most research in this domain narrowed to a limited range of variables. Besides, since the focus of most research in this field has been on multi optimization, the final step of them has been the attainment of optimizing results. Optimization is the procedure of finding the minimum or maximum value of a function by choosing a number of variables subject to a number of constraints. The optimization function is called cost or fitness or objective function and is usually calculated using simulation tools Yet, the further level, which is proposing proper design alternatives for each climate, has been neglected. This research not only proposed a workflow to optimize building louvers by considering the most promising influential factors but also proposed design alternatives in understudied climate zones. These alternatives will be proposed by categorizing the results from Pareto Front GA optimization. We will come up with specific louver features which compatible with a specific climate. These design alternatives are assessed by energy performance, visual and thermal comfort in the mentioned climates. The research will be carried on by a mixed-method research approach. We will evaluate energy, visual, and thermal comfort based on the quantitative methods, and then try to categorize obtained cases by climate based on the quantitative method. To do that, Firstly, we define variable parameters of louver forms and their position on building façades. Secondly, we set three different climate zones based on the Koppen classification. We will analyze the energy performance, thermal and visual comfort of incorporating louvers in these climates. Thirdly, since several possible solutions will be gained, we employ a Pareto chart to reach optimized outcomes. Furthermore, Finally, based on the achieved design alternative, we will use qualitative research on categorizing results in each climate zone. The results show the significant change between louver parameters in Bandar Abbas and Tabriz in depth and angles. Since louver materials don’t affect energy consumption for heating and cooling, the optimization algorithm has been benefitted from material with high reflectance. | ||
| کلیدواژه‌ها [English] | ||
| Visual comfort, Daylight metrics, Louver, Parametric design, Non-dominated sorting genetic algorithm 2 | ||
| مراجع | ||
|
Al-Masrani, Salwa M., Karam M. Al-Obaidi, Nor Azizah Zalin, and M. I. Aida Isma. 2018. “Design Optimisation of Solar Shading Systems for Tropical Office Buildings: Challenges and Future Trends.” Solar Energy 170 (April): 849–72. https://doi.org/10.1016/j.solener.2018.04.047.
Alrubaih, M. S., M. F.M. Zain, M. A. Alghoul, N. L.N. Ibrahim, M. A. Shameri, and Omkalthum Elayeb. 2013. “Research and Development on Aspects of Daylighting Fundamentals.” Renewable and Sustainable Energy Reviews 21: 494–505. https://doi.org/10.1016/j.rser.2012.12.057.
Associates, Robert McNeel &. 2018. “Rhinoceros.
” Bellia, Laura, Concetta Marino, Francesco Minichiello, and Alessia Pedace. 2014. “An Overview on Solar Shading Systems for Buildings.” Energy Procedia 62: 309–17. https://doi.org/10.1016/j.egypro.2014.12.392.
Borgstein, E. H., R. Lamberts, and J. L.M. Hensen. 2016. “Evaluating Energy Performance in Non-Domestic Buildings: A Review.” Energy and Buildings 128: 734–55. https://doi.org/10.1016/j.enbuild.2016.07.018.
Bre, Facundo, Nadia Roman, and Víctor D. Fachinotti. 2020. “An Efficient Metamodel-Based Method to Carry out Multi-Objective Building Performance Optimizations.” Energy and Buildings 206: 109576. https://doi.org/10.1016/j.enbuild.2019.109576.
Brembilla, E., D. A. Chi, C. J. Hopfe, and J. Mardaljevic. 2019. “Evaluation of Climate-Based Daylighting Techniques for Complex Fenestration and Shading Systems.” Energy and Buildings 203: 109454. https://doi.org/10.1016/j.enbuild.2019.109454. Brownlee, Alexander E I, Jonathan A Wright, and Monjur M Mourshed. 2011. “A Multi-Objective Window Optimisation Problem.” In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, 89–90.
Caldas, Luisa, and Luis Santos. 2016. “Painting with Light: An Interactive Evolutionary System for Daylighting Design.” Building and Environment 109: 154–74.
Chan, Ying Chieh, and Athanasios Tzempelikos. 2013. “Efficient Venetian Blind Control Strategies Considering Daylight Utilization and Glare Protection.” Solar Energy 98 (PC): 241–54. https://doi.org/10.1016/j.solener.2013.10.005.
Choi, Jaepil, Taekyoung Lee, Euisoon Ahn, and Gensong Piao. 2014. “Parametric Louver Design System Based on Direct Solar Radiation Control Performance.” Journal of Asian Architecture and Building Engineering 13 (1): 57–62. https://doi.org/10.3130/jaabe.13.57.
Chołodowicz, Ewelina, and Przemyslaw Orłowski. 2017. “Comparison of SPEA2 and NSGA-II Applied to Automatic Inventory Control System Using Hypervolume Indicator.” Studies in Informatics and Control 26 (1): 67–74. https://doi.org/10.24846/v26i1y201708.
Datta, Gouri. 2001. “Effect of Fixed Horizontal Louver Shading Devices on Thermal Perfomance of Building by TRNSYS Simulation.” Renewable Energy 23 (3–4): 497–507. https://doi.org/10.1016/S0960-1481(00)00131-2.
Deb, Kalyanmoy, Nikhil Padhye, and Ganesh Neema. 2007. “Multiobjective Evolutionary Optimization-Interplanetary Trajectory Optimization with Swing-Bys Using Evolutionary Multi-Objective Optimization.” Lecture Notes in Computer Science 4683: 26–35.
DiLaura, David L, Kevin Houser, Richard Mistrick, and Gary R Steffy. 2011. “The Lighting Handbook: Reference and Application.
” Edwards, L, and P Torcellini. 2002. “A Literature Review of the Effects of Natural Light on Building Occupants.” Contract, no. July: 55.
Eiben, Agoston E, and James E Smith. 2003. Introduction to Evolutionary Computing. Vol. 53. Springer.
Ekici, Berk, Cemre Cubukcuoglu, Michela Turrin, and I Sevil Sariyildiz. 2019. “Performative Computational Architecture Using Swarm and Evolutionary Optimisation: A Review.” Building and Environment 147: 356–71. https://doi.org/https://doi.org/10.1016/j.buildenv.2018.10.023.
Eltaweel, Ahmad, and Yuehong Su. 2017. “Controlling Venetian Blinds Based on Parametric Design; via Implementing Grasshopper’s Plugins: A Case Study of an Office Building in Cairo.” Energy and Buildings 139: 31–43. https://doi.org/10.1016/j.enbuild.2016.12.075.
Emmerich, Michael T M, and André H Deutz. 2018. “A Tutorial on Multiobjective Optimization: Fundamentals and Evolutionary Methods.” Natural Computing 17 (3): 585–609.
Fang, Yuan, and Soolyeon Cho. 2019. “Design Optimization of Building Geometry and Fenestration for Daylighting and Energy Performance.” Solar Energy 191 (July): 7–18. https://doi.org/10.1016/j.solener.2019.08.039.
Gago, E. J., T. Muneer, M. Knez, and H. Köster. 2015. “Natural Light Controls and Guides in Buildings. Energy Saving for Electrical Lighting, Reduction of Cooling Load.” Renewable and Sustainable Energy Reviews 41: 1–13. https://doi.org/10.1016/j.rser.2014.08.002.
Ghaderian, Mohammadamin, and Farzad Veysi. 2021. “Multi-Objective Optimization of Energy Efficiency and Thermal Comfort in an Existing Office Building Using NSGA-II with Fitness Approximation: A Case Study.” Journal of Building Engineering 41: 102440.
González, Javier, and Francesco Fiorito. 2015. “Daylight Design of Office Buildings: Optimisation of External Solar Shadings by Using Combined Simulation Methods.” Buildings 5 (2): 560–80. https://doi.org/10.3390/buildings5020560. Hashemi, Arman. 2014. “Daylighting and Solar Shading Performances of an Innovative Automated Reflective Louvre System.” Energy and Buildings 82: 607–20. https://doi.org/10.1016/j.enbuild.2014.07.086.
Hiroyasu, Tomoyuki, Seiichi Nakayama, and Mitsunori Miki. 2005. “Comparison Study of SPEA2+, SPEA2, and NSGA-II in Diesel Engine Emissions and Fuel Economy Problem.” In 2005 IEEE
Congress on Evolutionary Computation, 1:236–42. IEEE. Hoffmann, Sabine, Eleanor S. Lee, Andrew McNeil, Luis Fernandes, Dragan Vidanovic, and Anothai Thanachareonkit. 2016. “Balancing Daylight, Glare, and Energy-Efficiency Goals: An Evaluation of Exterior Coplanar Shading Systems Using Complex Fenestration Modeling Tools.” Energy and Buildings 112: 279–98. https://doi.org/10.1016/j.enbuild.2015.12.009.
Holland, John Henry. 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT press.
Hviid, Christian Anker, Toke Rammer Nielsen, and Svend Svendsen. 2008. “Simple Tool to Evaluate the Impact of Daylight on Building Energy Consumption.” Solar Energy 82 (9): 787–98. https://doi.org/10.1016/j.solener.2008.03.001.
IEA SHC. 2000. “Daylight in Buildings - a Source Book on Daylighting Systems and Components.” IEA SHC Task 21 - ECBCS Anexo 29, 262.
Jiang, Fujian, Yanping Yuan, Zhengrong Li, Qun Zhao, and Kaiming Zhao. 2020. “Correlations for the Forced Convective Heat Transfer at a Windward Building Façade with Exterior Louver Blinds.” Solar Energy 209 (July): 709–23. https://doi.org/10.1016/j.solener.2020.07.014.
Kirimtat, Ayca, Basak Kundakci Koyunbaba, Ioannis Chatzikonstantinou, and Sevil Sariyildiz. 2016. “Review of Simulation Modeling for Shading Devices in Buildings.” Renewable and Sustainable Energy Reviews 53: 23–49. https://doi.org/10.1016/j.rser.2015.08.020.
Kirimtat, Ayca, Ondrej Krejcar, Berk Ekici, and M. Fatih Tasgetiren. 2019. “Multi-Objective Energy and Daylight Optimization of Amorphous Shading Devices in Buildings.” Solar Energy 185 (July 2018): 100–111. https://doi.org/10.1016/j.solener.2019.04.048.
Konstantoglou, Maria, and Aris Tsangrassoulis. 2016. “Dynamic Operation of Daylighting and Shading Systems: A Literature Review.” Renewable and Sustainable Energy Reviews 60: 268–83. https://doi.org/10.1016/j.rser.2015.12.246. LBNL. 2020. “Radiance.” Machairas, Vasileios, Aris Tsangrassoulis, and Kleo Axarli. 2014. “Algorithms for Optimization of Building Design: A Review.” Renewable and Sustainable Energy Reviews 31 (1364): 101–12. https://doi.org/10.1016/j.rser.2013.11.036.
Magnier, Laurent, and Fariborz Haghighat. 2010. “Multiobjective Optimization of Building Design Using TRNSYS Simulations, Genetic Algorithm, and Artificial Neural Network.” Building and Environment 45 (3): 739–46.
Mangkuto, Rizki A., Deasty Kusuma Dewi, Annisa Azalia Herwandani, Mochamad Donny Koerniawan, and Faridah. 2019. “Design Optimisation of Internal Shading Device in Multiple Scenarios: Case Study in Bandung, Indonesia.” Journal of Building Engineering 24 (November 2018): 100745.
https://doi.org/10.1016/j.jobe.2019.100745. Manzan, Marco. 2014. “Genetic Optimization of External Fixed Shading Devices.” Energy and Buildings 72: 431–40. https://doi.org/10.1016/j.enbuild.2014.01.007.
Marler, R Timothy, and Jasbir S Arora. 2010. “The Weighted Sum Method for Multi-Objective Optimization: New Insights.” Structural and Multidisciplinary Optimization 41 (6): 853–62.
Mirjalili, Seyedeh Zahra, Seyedali Mirjalili, Shahrzad Saremi, Hossam Faris, and Ibrahim Aljarah. 2018. “Grasshopper Optimization Algorithm for Multi-Objective Optimization Problems.” Applied Intelligence 48 (4): 805–20. https://doi.org/10.1007/s10489-017-1019-8.
Nabil, Azza, and John Mardaljevic. 2006. “Useful Daylight Illuminances: A Replacement for Daylight Factors.” Energy and Buildings 38 (7): 905–13.
https://doi.org/10.1016/j.enbuild.2006.03.013. Nguyen, Anh Tuan, Sigrid Reiter, and Philippe Rigo. 2014. “A Review on Simulation-Based Optimization Methods Applied to Building Performance Analysis.” Applied Energy 113: 1043–58. https://doi.org/10.1016/j.apenergy.2013.08.061.
Nielsen, Martin Vraa, Svend Svendsen, and Lotte Bjerregaard Jensen. 2011. “Quantifying the Potential of Automated Dynamic Solar Shading in Office Buildings through Integrated Simulations of Energy and Daylight.” Solar Energy 85 (5): 757–68. https://doi.org/10.1016/j.solener.2011.01.010.
Nondy, J, and T K Gogoi. 2021. “Performance Comparison of Multi-Objective Evolutionary Algorithms for Exergetic and Exergoenvironomic Optimization of a Benchmark Combined Heat and Power System.” Energy, 121135.
O’Connor, Jennifer. 1997. “Tips for Daylighting With Windows,” 1–107. http://windows.lbl.gov/daylighting/designguide/dlg.pdf.
Omer, Abdeen Mustafa. 2008. “Energy, Environment and Sustainable Development.” Renewable and Sustainable Energy Reviews 12 (9): 2265–2300. https://doi.org/10.1016/j.rser.2007.05.001.
Palmero-Marrero, Ana I., and Armando C. Oliveira. 2010. “Effect of Louver Shading Devices on Building Energy Requirements.” Applied Energy 87 (6): 2040–49. https://doi.org/10.1016/j.apenergy.2009.11.020.
Pauley, Stephen M. 2004. “Lighting for the Human Circadian Clock: Recent Research Indicates That Lighting Has Become a Public Health Issue.” Medical Hypotheses 63 (4): 588–96. https://doi.org/10.1016/j.mehy.2004.03.020.
Reinhart, Christoph F., J. Alstan Jakubiec, and Diego Ibarra. 2013. “Definition of a Reference Office for Standardized Evaluations of Dynamic Façade and Lighting Technologies.” Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, 3645–52.
Reinhart, Christoph F., John Mardaljevic, and Zack Rogers. 2006. “Dynamic Daylight Performance Metrics for Sustainable Building Design.” LEUKOS - Journal of Illuminating Engineering Society of North America 3 (1): 7–31. https://doi.org/10.1582/LEUKOS.2006.03.01.001.
Roudsari, Mostapha Sadeghipour, and Michelle Pak. 2013. “Ladybug: A Parametric Environmental Plugin for Grasshopper to Help Designers Create an Environmentally-Conscious Design.” Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, 3128–35.
Shi, Long, and Michael Yit Lin Chew. 2012. “A Review on Sustainable Design of Renewable Energy Systems.” Renewable and Sustainable Energy Reviews 16 (1): 192–207. https://doi.org/10.1016/j.rser.2011.07.147.
Shukla, Pradyumn Kumar, Kalyanmoy Deb, and Santosh Tiwari. 2005. “Comparing Classical Generating Methods with an Evolutionary Multi-Objective Optimization Method.” In International Conference on Evolutionary Multi-Criterion Optimization, 311–25. Springer.
Sjarifudin, Firza Utama, and Laurensia Justina. 2014. “Daylight Adaptive Shading Using Parametric Camshaft Mechanism for SOHO in Jakarta.” EPJ Web of Conferences 68. https://doi.org/10.1051/epjconf/20146800037.
Toutou, Ahmed, Mohamed Fikry, and Waleed Mohamed. 2018. “The Parametric Based Optimization Framework Daylighting and Energy Performance in Residential Buildings in Hot Arid Zone.” Alexandria Engineering Journal 57 (4): 3595–3608. https://doi.org/10.1016/j.aej.2018.04.006.
Tzempelikos, Athanassios. 2008. “The Impact of Venetian Blind Geometry and Tilt Angle on View, Direct Light Transmission and Interior Illuminance.” Solar Energy 82 (12): 1172–91. https://doi.org/10.1016/j.solener.2008.05.014. Tzempelikos, Athanassios, and Andreas K. Athienitis. 2007. “The Impact of Shading Design and Control on Building Cooling and Lighting Demand.” Solar Energy 81 (3): 369–82. https://doi.org/10.1016/j.solener.2006.06.015.
Wagdy, Ayman, and Fatma Fathy. 2015. “A Parametric Approach for Achieving Optimum Daylighting Performance through Solar Screens in Desert Climates.” Journal of Building Engineering 3: 155–70. https://doi.org/10.1016/j.jobe.2015.07.007.
Yamín Garretón, Julieta, Ayelén María Villalba, Roberto Germán Rodriguez, and Andrea Pattini. 2021. “Roller Blinds Characterization Assessing Discomfort Glare, View Outside and Useful Daylight Illuminance with the Sun in the Field of View.” Solar Energy 213 (April 2020): 91–101.
Ye, Yunyang, Peng Xu, Jiachen Mao, and Ying Ji. 2016. “Experimental Study on the Effectiveness of Internal Shading Devices.” Energy and Buildings 111: 154–63. https://doi.org/10.1016/j.enbuild.2015.11.040.
Zitzler, Eckart. 1999. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Vol. 63. Citeseer. Zomorodian, Zahra S., and Mohammad Tahsildoost. 2019. “Assessing the Effectiveness of Dynamic Metrics in Predicting Daylight Availability and Visual Comfort in Classrooms.” Renewable Energy 134: 669–80. https://doi.org/10.1016/j.renene.2018.11.072. | ||
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