29 تیر 1403
امين شهسوارگلدانلو

امین شهسوارگلدانلو

مرتبه علمی: استادیار
نشانی: دانشگاه صنعتی کرمانشاه - دانشکده مهندسی - گروه مهندسی مکانیک
تحصیلات: دکترای تخصصی / مهندسی مکانیک
تلفن:
دانشکده: دانشکده مهندسی

مشخصات پژوهش

عنوان
A parametric assessing and intelligent forecasting of the energy and exergy performances of a dish concentrating photovoltaic/thermal collector considering six different nanofluids and applying two meticulous soft computing paradigms
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Dish concentrating photovoltaic thermal system Exergy Multi-gene genetic optimization Nanofluid Thermodynamic analysis
پژوهشگران اقبال بنی اسد عسگری (نفر اول)، امین شهسوارگلدانلو (نفر دوم)، مهدی جامعی (نفر سوم)، فرانچسکو سالیس (نفر چهارم)، مسعود کرباسی (نفر پنجم)

چکیده

In the present study, the application of six engine oil-based Nano fluids (NFs) in a solar concentrating photovoltaic thermal (CPVT) collector is investigated. The calculations were performed for different values of nanoparticle volume concentration, receiver tube diameter, concentrator surface area, receiver length, receiver actual to the maximum number of channels ratio, beam radiation, and a constant volumetric flow rate. Besides, two novel soft computing paradigms namely, the cascaded forward neural network (CFNN) and Multi-gene genetic programming (MGGP) were adopted to predict the first law efficiency () and second law efficiency () of the system based on the influential parameters, as the input features. It was found that the increase of nanoparticle concentration leads to an increase in and a decrease in . Moreover, the rise of both the concentrator surface area (from 5 m2 to 20 m2) and beam irradiance (from 150 W/m2 to 1000 W/m2) entails an increase in both the (by 39% and 261%) and (by 55% and 438%). Furthermore, it was reported that the pattern of changes in both and with serpentine tube diameter, receiver plate length, and absorber tube length is increasing-decreasing. The results of modeling demonstrated that the CFNN had superior performance than the MGGP model.