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بهینهسازی سقف ساختمان سه طبقه مسکونی با کمک الگوریتم ژنتیک (مورد پژوهی: اقلیم گرموخشک شهر شیراز) | ||
معماری و شهرسازی پایدار | ||
دوره 10، شماره 1، فروردین 1401، صفحه 141-167 اصل مقاله (2.59 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22061/jsaud.2022.7298.1770 | ||
نویسندگان | ||
پری ناز کشتکاران1؛ خسرو موحد* 2؛ زهرا برزگر3 | ||
1دانشجوی دکتری، گروه معماری، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران. | ||
2دانشیار، معماری، گروه معماری، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران. | ||
3استادیار مدعو، معماری، گروه معماری، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران. | ||
تاریخ دریافت: 02 خرداد 1400، تاریخ بازنگری: 18 مهر 1400، تاریخ پذیرش: 11 دی 1400 | ||
چکیده | ||
دیواره های بنا به لحاظ تبادل حرارت و کنترل مصرف انرژی ساختمان مهم است، زیرا خارجیترین پوسته بناست که در تماس مستقیم با هوا وتغییرات دما قرار دارد. بدنه سقف نسبت به سایر دیوارههای ساختمان از اهمیت بیشتری برخوردار است، زیرا تبادل حرارتی آن، با توجه به مساحت و زمان، نسبت به سایر دیوارها، بیشتردر معرض تابش خورشید و سایر عوامل قرار دارد. هدف این مقاله کاهش مصرف انرژی در ساختمان مسکونی در شهر شیراز و دستیابی به آسایش حرارتی در فضاهای ساختمان با بهینهسازی سقف ساختمان است. در این راستا پرسش زیر طرح شد که: بهینهترین حالت طراحی سقف ساختمان (مصالح و روشهای غیر فعال طراحی) چگونه باشد تا مصرف انرژی ساختمان کمینه شود و فضاهای بنا در محدوده آسایش حرارتی قرارگیرد؟ میزان کاهش انرژی مصرفی ساختمان نیز مد نظر این پژوهش بوده است. روش پژوهش، کمی است و میزان مصرف انرژی ساختمان و شاخص آسایش حرارتی با نرمافزار انرژی پلاس و روند بهینهسازی با الگوریتم ژنتیک انجام شده است. متغیرهای سقف بنا در سه دسته کلی: سیستم غیر فعال انرژی، ویژگیهای کالبدی سقف و موقعیت قرارگیری سقف، استخراج و تعریف شد. نتایج به دست آمده از محاسبات شبیهسازی ساختمان و خروجی تابع هدف الگوریتم ژنتیک، نشان داد که بهترین مدلهای سقف ارایه شده، مصرف انرژی را از سیونه تا پنجاه درصد کاهش و میانگین شاخص آسایش حرارتی را نیز به میزان0.9 و 0.68 به ترتیب در گرمترین و سردترین ماههای تابستان و زمستان بهبود میدهد. | ||
کلیدواژهها | ||
مصرف انرژی؛ آسایش حرارتی؛ بهینهسازی سقف ساختمان؛ الگوریتم ژنتیک؛ شبیهساز انرژی | ||
عنوان مقاله [English] | ||
Roof Optimization of Three Floor Residential Building Using G.A Case Study: Shiraz, Iran | ||
نویسندگان [English] | ||
Parinaz Keshtkaran1؛ Kh. Movahed2؛ Zahra Barzegar3 | ||
1PhD. Student of Architecture, Department of Architecture, Shiraz Branch, Islamic Azad University, Shiraz, Iran. | ||
2Associate Prof. of Architecture, Department of Architecture, Shiraz Branch, Islamic Azad University, Shiraz, Iran. | ||
3Associate Prof. of Architecture, Department of Architecture, Shiraz Branch, Islamic Azad University, Shiraz, Iran. | ||
چکیده [English] | ||
Among the components of the building envelope, the roof section is the most important medium of energy loss, because, compared to vertical walls, a larger area of the rooftop is exposed to sunlight and atmospheric condition for a longer period, which results in greater heat exchange through this section. The goal of this research is to reduce the energy consumption of residential buildings in the city of Shiraz (Fars, Iran) through the optimization of their roof sections by answering the bellow question: ”What is the most optimal combination of materials and passive roof design technique for reducing energy consumption in this climate and how much will it reduce energy consumption?” The purpose of the research, which is to identify the best combination of roof specifications and passive design methods for minimizing energy consumption in the buildings of the area of interest, was achieved with help of the EnergyPlus simulation and a genetic algorithm developed in MATLAB. The roof variables were defined in three categories of the passive energy system, physical roof specifications, and positioning and the optimization was carried out using the genetic algorithm and the EnergyPlus software. The outputs obtained from the simulation software and the objective function of the genetic algorithm showed that the roof optimization models could reduce the energy consumption of the building by 39-50% | ||
کلیدواژهها [English] | ||
energy saving, roof, optimization, genetic algorithm, energy simulator | ||
مراجع | ||
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