نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، دانشکده هنر و معماری، دانشگاه شیراز، فارس، ایران.

چکیده

شبیه­سازی انرژی در محیط شهری میتواند با دو هدف عمده تحلیل آسایش حرارتی خرداقلیم و یا تاثیر خرداقلیم شهری بر مصرف انرژی ساختمان انجام شود. اولین قدم جهت کاربرد شبیه­سازی انرژی، انتخاب ابزار مناسب است که بدون شناخت دقیق از نحوه عملکرد ابزارها میسر نمیشود. از طرف دیگر تعداد رو به رشد نرم­افزارهای شبیه­سازی، انتخاب ابزار مناسب را دشوار میسازد. با توجه به تمایل طراحان در چندین سال اخیر به این زمینه، آگاهی از قابلیتهای مدلسازی و محدودیتهای ابزارهای مورد کاربرد ضروری است. پژوهش حاضر با معرفی شاخصهای سنجش آسایش حرارتی در محیط خارجی و دسته بندی انواع شبیه­سازی انرژی در مقیاس شهری، شش نرم افزار انویمت، ریمن، یومی، متئودین، سولن و سولوگ را جهت سنجش آسایش پیاده معرفی کرده و در تحلیلی تطبیقی به بررسی نحوه عملکرد و مقایسه قابلیتهای آنها میپردازد. سه نرم‌افزار انویمت، سولن و ریمن بیشترین شاخصهای آسایش حرارتی خارجی را در نتایج خروجی ارائه میدهند. در یومی و متئودین داده ها به صورت تخمینی از میانگین تابش و سرعت باد و در بقیه ابزارها به صورت دقیق و در هر لحظه دلخواه قابل استخراج است. در حالیکه یومی ابزاری ساده و رایگان است، استفاده از انویمت و سولن غیر رایگان بوه و نیاز به آموزش دارد. هرچند در حال حاضر ابزار واحدی که بهترین ترکیب از همه عوامل را مدنظر قرار داده و همه فرایندهای فیزیکی را شامل شود وجود ندارد. نتایج این پژوهش میتواند معماران و طراحان شهری را در انتخاب نرم­افزار مناسب در هر مرحله از  طراحی و با توجه به اهداف پروژه یاری رساند.

کلیدواژه‌ها

عنوان مقاله [English]

Comparative Study of Thermal Comfort Simulation Software in Urban Environment

نویسنده [English]

  • Roza Vakilinezhad

Assistant Professor, School of Art and Architecture, Shiraz University.Fars. Iran.

چکیده [English]

Extended Abstract
Energy simulation in an urban environment can be accomplished considering two major objectives: first by analyzing the environmental thermal comfort and second by defining the impact of the environment on buildings energy consumption. Three factors affect the creation of pedestrian thermal comfort including climate (global radiation, air temperature, relative humidity and wind speed), microclimate (sky view factor, direct and indirect radiation, mean radiant temperature, surface temperature, ground temperature, building and ground albedo) and pedestrian physical properties (metabolic activity and coatings; these factors are usually not considered in current software. In order to evaluate the thermal components of urban or regional climates, it is necessary to gather accurate data about the radiant conditions in the surrounding environment. This data can be measured experimentally or calculated using the appropriate radiation model. Thus, there are two general methods of using questionnaires and computational simulations to evaluate the effective factors. For applying energy simulation, selecting the proper tool is the first step, which would not be possible without a detailed understanding of how the tool works. On the other hand, it is difficult to choose the proper tool among the growing number of simulation software. Considering designers’ recent tendency in this field, it is essential to be aware of the modeling capabilities and limitations of each tool. Some studies have compared the capabilities of building energy software, but no similar studies have been done on the urban scale.
By identifying outdoor thermal indices, this study classifies different types of energy simulation at urban scale while introducing six software of Envimet, Rayman, UMI, Meteodyn, Solene and SOLWEIG for pedestrian thermal comfort evaluation. This study aims to define the capabilities, potential, weakness and efficiency of the mentioned software in urban environment. By applying comparative and logical analysis research method, the study is conducted in four steps. In the first step, outdoor thermal comfort indicators have been identified considering effective factors in creation of urban microclimate. The second step is dedicated to identification and classification of related software to be distinguished from urban energy analysis software. In the third step, software performance and the related features are examined and in the final step, selected software properties have been compared in different fields to define strengths, weaknesses and proper application. Capability of the above-mentioned software are compared in terms of climatic parameters, outdoor thermal comfort indicators, solving equations, defaults and neglected factors, extractable parameters, numerical and graphical output data, application simplicity, interaction with other software, graphical interface, accessibility and cost.
In order to perform a simulation with proper accuracy, it is necessary to consider three basic models of radiation, heat transfer and CFD airflow in combination with each other. However, in many energy simulation tools, some equations in the analysis process are overlooked for simplification. In Envimet and Solene software, the three equations are analyzed. The analysis in Rayman, SOLWEIG and UMI models is based on the radiation and heat transfer models only ignoring the airflow model that is assumed to be constant. In Meteodyn, the radiation model in Urbasun and the airflow model in Urbawind tool are analyzed. In Envimet and Solone, four parameters of dry temperature, relative humidity, wind speed and radiant temperature are calculated. Apart from wind speed, Solone calculates three other parameters, while in Rayman, dry temperature and radiant temperature are considered regardless of relative humidity and wind speed. The only parameter examined in UMI and Meteodyn is wind speed. By computing radiant fluxes, Rayman calculates six thermal comfort indicators. Envimet and Solone calculate four indicators including PMV, PET, UTCI, and MRT and in SOLWEIG model, three indicators including PET, UTCI, and MRT are calculated, while none of the common indicators for thermal comfort is listed as the output data in UMI and Meteodyn. Envimet, Solene and Rayman provide more outdoor thermal comfort indicators as output results. In UMI and Meteodyn data is estimated as the average radiation and wind speed while in other the tools it could be extracted precisely at any desired time. While UMI is a simple free tool, using Envimet and Solene is not free and requires training. However, currently no single tool considers the best combination of all factors and includes all physical processes. The results of this research can help architects, urban designers and software users to choose the proper software in each design stage considering project goals

کلیدواژه‌ها [English]

  • urban environment
  • Simulation
  • Thermal Comfort
  • Energy
-          پوردیهیمی، شهرام. (1390). فرهنگ و مسکن، مسکن و محیط روستا، 30 (134)، 3-18.
-          حیدری، شاهین؛ منعام، علیرضا (1392). ارزیابی شاخصه های آسایش حرارتی در فضای باز. جغرافیاوتوسعهناحیه ای، (20)، 197-216.
-          غیائى، محمدمهدی، مهدوى نیا، مجتبى، طاهباز، منصوره، مفیدى شمیرانى، سیدمجید (1392). روش شناسى گزینش نرم افزارهای کاربردى شبیه ساز انرژی در حوزه معماری، هویتشهر، 13 (7)، 45-55.
-          Allegrini, J., Orehounig, K., Mavromatidis, G., Ruesch, F., Dorer, V., Evins, R. (2015). A review of modelling approaches and tools for the simulation of district-scale energy systems, Renewable and Sustainable Energy Reviews, 52, 1391–1404.
-          Blazejczyk, K., Epstein, Y., Jendritzky, G. et al. (2012). Comparison of UTCI to selected thermal indices. International Journal Biometeorology, (56),515–535.
-          Bouyer, J., Inard, CH., Musya, M. (2011), Microclimatic coupling as a solution to improve building energy simulation in an urban context, Energy and Buildings, 43, 1549–1559.
-          Bruse, M., Fleer, H. (1998). Simulating surface-plant-air interactions inside urban environments with a three dimensional numerical model, Environment Modelling Software, 13, 373-384.
-          Bruse, M; Fleer, H. (1998). Simulating surface plant air interactions inside urban environments with a three dimensional numerical model. Environmental Modelling and Software, 3, 373-384.
-          Bueno, B., Norford, L., Hidalgo, J., and Pigeon, G. (2013). The urban weather generator. Journal of Building Performance Simulation, 6(4), 269-281.
-          Cheng,V; Ng, E; Chan, C; Givoni, B. (2011). Outdoor thermal comfort study in a subtropical climate: A longitudinal study based in Hong Kong. International Journal of Biometeorol, 56, 43-56.
-          Crawley1, D., Hand, J., Kummert, M. et al. (2005), Contrasting the capabilities of building energy performance simulation programs, Ninth International IBPSA Conference, 15-18.
-          Dogan, T., Reinhart, C., Michalatos, P. (2012). Urban daylight simulation: Calculating the daylit area of urban designs, In: Proceedings of SimBuild.
-          envi-met, 2019. Retrieved from: https://www.urbanclimate.net/rayman/, at October, 2019; 09:20:00PM.
-          Gros, A., Bozonnet, E., r Inard, Ch. Et al. (2016). Simulation tools to assess microclimate and building energy – A case study on the design of a new district, Energy and Buildings, 114 (2016) 112–122.
-          Herrmann, J., Matzarakis, A., (2010). Influence of mean radiant temperature on thermal comfort of humans in idealized urban environments. In: Proceedings of the 7th Conference on Biometeorology, 20, 523-528.
-          Höppe, P. (2002). Different aspects of assessing indoor and outdoor thermal comfort. Energy and Buildings, 34, 661-665.
-          Huttner, S., Bruse, M., Dostal, P. (2008). Using ENVI-met to simulate the impact of global warming on the microclimate in central European cities. 5th Japanese-German Meeting on Urban Climatology, 2008, 307-312.
-          Jendritzky, Gerd and W. Nübler. (1981). A model analysing the urban thermal environment in physiologically significant terms.” Archives for meteorology, geophysics, and bioclimatology, Series B, (29) 313-326.
-          Krüger, E. L., Minella, F. O., Rasia, F. (2011). Impact of urban geometry on outdoor thermal comfort and air quality from field measurements in Curitiba, Brazil. Building and Environment, 46(3), 621-634.
-          Leech, J.A; Burnett, R; Nelson, W; Aaron, Raizenne, M. (2000). Outdoor air pollution epidemiologic studies. American Journal of Respiration and Critical Care Medicine, 161(3), A308.
-          Lenzholzer, S., Klemma, W., Vasilikou, C. (2016). Qualitative methods to explore thermo-spatial perception in outdoor urban spaces. Urban Climate,
-          Lin, Tp; Matzarakis, A; Huang, JJ. (2006). Thermal comfort and passive design strategy of bus shelters. The 23rd Conference on Passive and Low Energy Architecture. Geneva, Switzerland.
-          Lin, TP; Matzarakis, A; Hwang, RL. (2010). Shading effect on long-term outdoor thermal comfort. Building and Environment, 45, 213-221.
-          Lindberg, F., Grimmond, C.S.B., Gabey, A. et al. (2018), Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services, Environmental Modelling & Software, 99, 70-87.
-          Lindberg, F; Holmer, B; Thorsson, S. (2008). SOL-WEIG 1.0 – Modelling spatial variations of 3D radiant fuxes and mean radiant temperature in complex urban settings. International Journal Biometeorol, 52, 697-713.
-          Matzarakis, A., Mayer, H. (1996). Another kind of environmental stress: Thermal stress. WHO Newsletter, 18, 7-10.
-          Matzarakis, A., Rutz, F., Scott, D. (2007). RAYMAN: a tool for tourism and applied climatology, Developments in Tourism Climatology, 129,
-          Matzarakis, A; Rutz, F. (2005). Application of RayMan for tourism and climate investigations. Annalen der Meteorologie, 41(2), 631-636.
-          Matzarakis, A; Rutz, F; Mayer, H. (2007). Modeling radiation fluxes in simple and complex environments – Application of Rayman model. International journal Biometeorol, 51, 323-334.
-          Matzarakis, A; Rutz, F; Mayer, H. (2010). Modeling radiation fluxes in simple and complex environments: Basics of the RayMan model. International Journal of Biometeorol, 54, 131-139.
-          Mayer, H; Höppe, P. (1987). Thermal comfort of man in different urban environments. Theoretical and Applied Climatology, 38, 43-49.
-          Meteodyn, 2019. Retrieved from: https://meteodyn.com, at September, 2019; 09:10:00AM.
-          Naboni, E., Marco Meloni, M., Coccolo, S., Kaempf, J., Scartezzini, J. (2017). An overview of simulation tools for predicting the mean radiant temperature in an outdoor space, Energy Procedia, 122 (2017) 1111–1116.
-          Ooka, R. (2007), Recent development of assessment tools for urban climate and heat-island investigation especially based on experiences in Japan, International journal of climatology, 27, 1919–1930.
-          Rakha, T., Reinhart, C. (2012). Generative Urban Modeling: A Design Work Flow for walkability optimized cities. In: Proceedings of SimBuild.
-          Reinhart, C., Dogan, T., Jakubiec, J. et al. (2013). UMI - an urban simulation environment for building energy use, daylighting and walkability, In: Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association.
-          Reinhart, C., Fitz, A. (2006). Findings from a Survey on the current use of daylight simulations during building design, Energy and Buildings, 38, 824‐835.
-          SOLENE, (2019); Retrieved from https://aau.archi.fr/crenau/solene/
-          Spagnolo, Jennifer; de Dear, Richard. (2003). A field study of thermal comfort in outdoor and semi-outdoor environments in subtropical Sydney Australia. Building and Environments, 38, 721-738.
-          Swan, L., Ugursal, I. (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques, Renewable and Sustainable Energy Reviews, 13(8),  1819-1835.
-          t4su, 2019. Retrieved from: https://t4su.wordpress.com/, at November, 2019; 11:20:00AM.
-          Targhi, M. Z.,& Van Dessel, S. (2015). Potential Contribution of Urban Developments to Outdoor Thermal Comfort Conditions: The Influence of Urban Geometry and Form in Worcester, Massachusetts, USA. In: Procedia Engineering, 118, 1153-1161.
-          Thorsson, S; Lindqvist, M; Lindqvist, S. (2004). Thermal bioclimatic conditions and patterns of behavior in an urban park in Goteborg, Sweden. International Journal of Biometeorology, 48, 149-156.
-          Toudert, F. A. (2005). Dependence of Outdoor Thermal Comfort on Street Design in Hot and Dry Climate. 80 .
-          umep docs.readthedocs, 2019. Retrieved from: https://umepdocs.readthedocs.io/en/latest/OtherManuals/ SOLWEIG.html, at October, 2019; 11:10:00PM.
-          Urbanclimate, 2019. Retrieved from: https://www.urbanclimate.net/rayman/, at November, 2019; 10:30:00AM.
-          walkscore, 2019. Retrieved from: http://www.walkscore.com, at September, 2019; 11:30:00AM.
-          Watson ID, Johnson GT (1987) Graphical estimation of skyview-factors in urban environments. Journal of Climatology, 7: 193–197.
-          web.mit.edu, 2019.  Retrieved from: web.mit.edu/sustainabledesignlab/projects/umi/index.html, at November, 2019; 09:40:00AM.