دانلود مقاله ISI انگلیسی شماره 53036
عنوان فارسی مقاله

پیش بینی رفتار حرارتی گوشه های ساختمان با استفاده از سیستم استنتاج فازی همراه با دسته بندی و فیلتر کالمن

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
53036 2016 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Predicting building's corners hygrothermal behavior by using a Fuzzy inference system combined with clustering and Kalman filter
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Communications in Heat and Mass Transfer, Volume 71, February 2016, Pages 225–233

کلمات کلیدی
ساختمان؛ رشد قالب؛ بهره وری انرژی؛ رفتار حرارتی ؛ سیستم های فازی
پیش نمایش مقاله
پیش نمایش مقاله پیش بینی رفتار حرارتی گوشه های ساختمان با استفاده از سیستم استنتاج فازی همراه با دسته بندی و فیلتر کالمن

چکیده انگلیسی

The hygroscopic characteristics of building materials can affect thermal gain or losses that are directly associated to energy consumption due to the latent heat transport. Moreover, some specific regions can accumulate humidity on building structures, and some of this regions, known as building corners, are still barely explored due to modelling complexity, high computer run time, numerical divergence, and highly moisture-dependent properties. This article presents an alternative to predict temperature, vapor pressure, and moisture content profiles in specific points where moisture can be easily accumulated, increasing mould growth risks and/or causing structural damage to the building. In order to avoid time-consuming numerical models, this article uses a Takagi–Sugeno fuzzy inference system with a multiple-input, single-output (MISO) structure to predict building corners hygrothermal behavior. Due to the ability of nonlinearity detection, associated with a small number of “if–then” rules with fuzzy antecedents and crisp mathematical functions or linear functions in the resultant part, the fuzzy system was combined with subtractive clustering method and Kalman filter to enhance its performance. The results suggested that the developed Takagi–Sugeno fuzzy model has achieved good accuracy in terms of precision when the results were compared to the analytical model. Moreover, in terms of simulation time, after the tuning and optimization procedures, the prediction of temperature, relative humidity, and vapor pressure on specific nodes are faster than the numerical model.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.