مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

مدل‌های سیستم استنتاج عصبی- فازی تطبیقی برای پیش‌بینی روند جدایش انرژی در لوله‌های گردابه‌ای با استفاده از تابع مطلوبیت

نوع مقاله : پژوهشی اصیل

نویسندگان
گروه مهندسی مکانیک، دانشکده مهندسی مکانیک، دانشگاه صنعتی ارومیه، ارومیه، ایران
چکیده
لوله گردابه‌ای یکی از سیستم‌های سرمایشی بسیار پرکاربرد در صنعت است. بررسی تاثیر کلیه متغیرهای ورودی بر اختلاف دمای خروجی سرد در حالت آزمایشگاهی، زمان‌بر و پرهزینه است. به همین منظور در کار حاضر سعی شده تا با استفاده از روش سیستم استنتاج عصبی- فازی تطبیقی تاثیر کلیه متغیرهای ورودی بر اختلاف دمای هوای خروجی سرد و هوای ورودی، مدل‌سازی و پیش‌بینی شود. روش سیستم استنتاج عصبی- فازی تطبیقی، با سه ساختار سیستم استنتاج فازی به‌نام‌های الگوریتم خوشه‌بندی کاهشی، خوشه‌بندی اختیاری و منقطع‌سازی شبکه‌ای با چهار نوع تابع عضویت ورودی مثلثی، گاوسی، زنگوله‌ای و شبه‌پی طراحی شد. برای آموزش و آزمون مدل، از ۳۲۶ داده آزمایشگاهی استفاده شد. مقایسه مدلهای توسعه‌یافته با استفاده از پارامترهای آماری ضریب همبستگی، میانگین انحراف نسبی مطلق، انحراف استاندارد و خطای مربع میانگین ریشه همراه با تابع مطلوبیت کلی انجام شد. نتایج نشان داد که الگوریتم منقطع‌سازی شبکه‌ای با تابع عضویت ورودی نوع شبه‌پی با دارابودن بیشترین مقدار ضریب همبستگی و کمترین مقدار خطای مربع میانگین ریشه برای داده‌های آزمون با مقادیر ۰/۹۹۷۵ و ۰/۴۱۹۹ و مقدار تابع مطلوبیت کلی۰/۷۱ بهترین روش برای پیش‌بینی اختلاف دمای خروجی سرد است. با استفاده از روش فوق، بهینه‌ترین حالت عملکرد لوله گردابه‌ای جهت کاربردهای صنعتی استفاده از ۳ یا ۶ عدد نازل، محدوده فشار ۰/۵۵ تا ۰/۶ مگاپاسکال و زاویه نازل ۲۰ تا ۳۰درجه و جهت کاربردهای آزمایشگاهی تعداد ۶ نازل، محدوده فشار ۰/۵۵ تا ۰/۶مگاپاسکال و زاویه نازل ۲۵ تا ۳۵درجه به‌دست آمد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Adaptive Neuro-Fuzzy Inference System Models to Predict the Energy Separation Procedure in Vortex Tube Using Desirability Function

نویسندگان English

M.B. Mohamad Sadeghi Azad
S. Behzadipour
Department of Mechanical Engineering Urmia University of Technology, Urmia, IRAN
چکیده English

The vortex tube is one of the widely used cooling systems in the industry. Investigating the effect of all input variables on the outlet cold temperature difference in laboratory state is time-consuming and costly. To this purpose, in the current study, attempts were made to model and predict the effect of all input variables on the outlet cold temperature difference of air and inlet air using adaptive neuro-fuzzy inference system (ANFIS) method. The ANFIS method was designed with three structures of fuzzy inference systems called subtractive clustering (SC) algorithm, fuzzy c-means (FCM), and grid partition (GP) with four types of input membership functions including trimf, gaussmf, gbellmf, and pimf. For model training and testing, 326 laboratory data were used. The developed models were compared using statistical parameters of correlation coefficient, mean absolute relative deviation, standard deviation, and root mean square error (RMSD) together with general desirability function. The results showed that GP algorithm with input pimf membership function with the greatest value of correlation coefficient (0.9975) and lowest value of RMSD for test data (0.4199) and general desirability function value of 0.71 is the best method to predict outlet cold temperature difference. Using the above-mentioned method, the most optimum state of vortex tube performance for industrial applications was found to be the use of 3 or 6 nozzels, at the pressure range of 0.55 to 0.6MPa and the nozzle angle of 20 to30 degrees, and for laboratory applications was obtained to be the use of 6 nozzles, at the pressure range of 0.55 to 0.6MPa, and the nozzle angle of 25 to 35 degrees.

کلیدواژه‌ها English

ANFIS
artificial neural network
Vortex Tube
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