سازگاری حرارتی به عنوان شاخص نهایی سنجش کیفیت سایه اندازی سایبانهای خارجی؛ نمونه مورد مطالعه: ساختمان مسکونی در شیراز (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
سایبان های خارجی یکی از اجزای بسیار مهم و کاربردی در طراحی معماری پایدار به ویژه در اقلیم های گرم هستند که تاثیر زیادی بر کنترل مصرف منابع انرژی برای کاربردهایی چون بار حرارتی، نور روز و آسایش حرارتی دارند که به نظر می رسد کمتر تحقیقی در مورد تاثیر کیفیت سایه اندازی بر این عملکردها به ویژه بر سازگاری حرارتی انجام شده باشد. برای این منظور، این مقاله با کمک برداشت های میدانی از یک ساختمان مسکونی در شیراز و شبیه سازی و اعتبار سنجی آن و سپس تولید یک سایبان پارامتریک خلاقانه با قابلیت تولید سایه اندازی های متنوع و در نهایت با استفاده از تکنیک نمونه برداری LHS و همچنین استفاده از شبکه عصبی مصنوعی و بهینه سازی چند هدفه با الگوریتم NSGA III توانسته است نمونه های سایبانی بهینه ای را تولید کند که در نهایت بر مبنای میزان سازگاری حرارتی طبقه بندی شده اند. این طبقه بندی نشان می دهد که در سایبان های بهینه با وجود اینکه شاخص های نور روز در محدوده استاندارد هستند و می توانند 53% تا 73% در بار سرمایشی و 8% تا 10% نیز در بار گرمایشی صرفه جویی ایجاد کنند اما مدت زمان تامین آسایش حرارتی توسط آنها بین 4 تا 8 ماه متغیر است که این خود نشان دهنده اهمیت تاثیر کیفیت سایبان ها بر آسایش حرارتی است.Adaptive thermal comfort as the final metric for evaluating the quality of the exterior shading on building envelopes The case study: a residential building in Shiraz, Iran
One of the main problems in the world is global warming, which is caused by the building sector and carbon emissions. Researchers have come to the conclusion that managing and reducing building energy consumptions and promoting sustainable building practices are crucial. Particularly in hot areas, exterior shading devices are one of the most significant and useful criteria for sustainable passive architecture design. They can have an impact on managing the building's energy resources, including thermal load, daylight, and adaptive thermal comfort. The application of the shadings is highly effective when shading design parameters have carefully and accurately been studied and designed because an inefficient shading device can easily increase the thermal load and create glare or darkness at the same time, or while keeping daylight indicators within the standard range, increase energy consumptions. Although many studies have investigated the design parameters of exterior shading devices including dimensions, materials, and the location of installation through optimization methods, it seems that none of the researches have considered the effect of shade quality on building performances. To investigate the quality of shading, through field measurement in a residential building in Shiraz, Iran, a model was simulated and validated and then a novel parametric exterior fixed shading device added to the model was created in grasshopper plugin which was able to produce a variety range of shadings. A large dataset of 13600 samples of the parametric shading was produced by applying the LHS technique, which created an outspread community of shading samples and executed the energy simulation for each sample. Then this large data set was used to train and test an Artificial Neural Network (ANN). This ANN was applied as a fast emulator and the searching space for multi-objective optimization through NSGA_III algorithm. The major goals in the optimization process are considered to be the least cooling and heating load, the minimum annual sun exposure (ASE), the maximum spatial daylight autonomy (sDA), and the minimum useful daylight illumination (UDI). These five functions are considered as independent variables. Finally, using an adaptive thermal comfort model, the Pareto front solutions have been categorized based on the Percent of Time Comfortable (PTC). This classification clearly demonstrates that although the optimized shading devices can keep the daylight standard indicators (ASE, sDA, and UDI) within acceptable ranges and reduce the cooling load from 53% to 73% and the heating load by from 8% to 10%, the values of PTC could vary by as much as 33.3% (i.e. 4 months of the year) to 66.67% (i.e. 8 months of the year). This range in PTC value is significant because, although certain optimal shading devices can maintain the PTC in interior spaces at 33.3%, other cases with the same rate of energy saving and daylight standard indicators can raise the PTC value to 66.67%. Therefore, this paper introduces the PTC in adaptive thermal comfort model as a new metric for evaluating the quality of the shading produced by any shading device types.
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