کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4993022 | 1457472 | 2017 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
جریان سیال و فرایندهای انتقال
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and introduced. Optimal results showed that the optimum viscosity and thermal conductivity occurs at maximum temperature.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Communications in Heat and Mass Transfer - Volume 82, March 2017, Pages 154-160
Journal: International Communications in Heat and Mass Transfer - Volume 82, March 2017, Pages 154-160
نویسندگان
Mohammad Hemmat Esfe, Peyman Razi, Mohammad Hadi Hajmohammad, Seyed Hadi Rostamian, Wail Sami Sarsam, Ali Akbar Abbasian Arani, Mahidzal Dahari,