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همت اسفه, محمد, مطلبی, سیدمجید. (1402). بررسی ضریب هدایت حرارتی نانوسیال هیبریدی سه‌گانه پایه آبی حاوی MWCNTs به روش شبکه عصبی مصنوعی. پدافند الکترونیکی و سایبری, 12(2), 155-168.
محمد همت اسفه; سیدمجید مطلبی. "بررسی ضریب هدایت حرارتی نانوسیال هیبریدی سه‌گانه پایه آبی حاوی MWCNTs به روش شبکه عصبی مصنوعی". پدافند الکترونیکی و سایبری, 12, 2, 1402, 155-168.
همت اسفه, محمد, مطلبی, سیدمجید. (1402). 'بررسی ضریب هدایت حرارتی نانوسیال هیبریدی سه‌گانه پایه آبی حاوی MWCNTs به روش شبکه عصبی مصنوعی', پدافند الکترونیکی و سایبری, 12(2), pp. 155-168.
همت اسفه, محمد, مطلبی, سیدمجید. بررسی ضریب هدایت حرارتی نانوسیال هیبریدی سه‌گانه پایه آبی حاوی MWCNTs به روش شبکه عصبی مصنوعی. پدافند الکترونیکی و سایبری, 1402; 12(2): 155-168.

بررسی ضریب هدایت حرارتی نانوسیال هیبریدی سه‌گانه پایه آبی حاوی MWCNTs به روش شبکه عصبی مصنوعی

مکانیک سیالات و آیرودینامیک
مقاله 11، دوره 12، شماره 2 - شماره پیاپی 32، اسفند 1402، صفحه 155-168    اصل مقاله (1014.44 K)
نوع مقاله: مقاله پژوهشی
نویسندگان
محمد همت اسفه* 1؛ سیدمجید مطلبی2
1استادیار، دانشگاه جامع امام حسین(علیه السلام)، تهران، ایران
2دانشجوی دکتری، دانشگاه جامع امام حسین(ع)، تهران، ایران
تاریخ دریافت: 23 مهر 1402،  تاریخ بازنگری: 01 دی 1402،  تاریخ پذیرش: 25 دی 1402 
چکیده
هدایت حرارتی نانوسیال MWCNT(40%)-CuO(30%)-TiO2(30%) /Water در کسر حجمی‌ها و دماهای مختلف با روش شبکه عصبی مصنوعی، مدل‌سازی و تحلیل می‌شود. شبکه عصبی مصنوعی از نوع MLP هست. 48 سری داده تجربی مورداستفاده قرار گرفت که به ترتیب %70، %15 و %15 برای آموزش، اعتبارسنجی و آزمون به کار رفت. ساختار عصبی بهینه دارای دو لایه پنهان است که در لایه اول 4 نورون و در لایه دوم 5 نورون به ترتیب با تابع انتقال logsig و tansig قرار دارد. آموزش شبکه عصبی با الگوریتم لونبرگ-مارکوارت (ML) انجام می‌شود. مقادیر پارامترهای ضریب رگرسیون R و میانگین خطا MSE برای ساختار بهینه به ترتیب برابر با 9995753/0 و 2.8734E-06 به دست آمدند. رابطه همگرایی نیز برای پیش‌بینی هدایت حرارتی نانوسیال ارائه می‌شود. مقایسه بین مدل همگرایی و شبکه عصبی مصنوعی، نشان از برتری شبکه عصبی مصنوعی دارد. مقادیر MOD برای شبکه عصبی مصنوعی نیز در محدوده %3- تا %7+ قرار گرفت.
کلیدواژه‌ها
نانوسیال؛ شبکه عصبی مصنوعی؛ هدایت حرارتی؛ لونبرگ- مارکوارت
عنوان مقاله [English]
Investigating the Thermal Conductivity Coefficient of Water-based Ternary Hybrid Nanofluid Containing Mwcnts by Artificial Neural Network Method
نویسندگان [English]
Mohammad Hemmat Esfe1؛ Sayyid majid Motallebi2
1Assistant Professor, Imam Hossein University, Tehran, Iran
2PhD student, Imam Hossein University, Tehran, Iran
چکیده [English]
Thermal conductivity of MWCNT(40%)-CuO(30%)-TiO2(30%) /Water nanofluid in different volume fractions and temperatures is modeled and analyzed by artificial neural network method. The type of artificial neural network is MLP. 48 experimental data series are used, 70%, 15%, and 15% are used for training, validation, and testing, respectively. The optimal neural structure has two hidden layers, in which there are 4 neurons in the first layer and 5 neurons in the second layer, with the transfer functions of logsig and tansig, respectively. Neural network training is done with Levenberg-Marquardt (ML) algorithm. The values of regression coefficient, R, and mean squared error, MSE, for the optimal structure are obtained as 0.9995753 and 2.8734E-06, respectively. The correlation equation is also presented to predict the thermal conductivity of nanofluid. The comparison between the correlation equation and the artificial neural network shows the superiority of the artificial neural network. MOD values for artificial neural network are also in the range of -3% to +7%.
کلیدواژه‌ها [English]
Nanofluid Levenberg-Marquardt, Thermal Conductivity Artificial Neural Network
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