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
Experimental exploration of rheological behavior of polyethylene glycol-carbon dot nanofluid: Introducing a robust artificial intelligence paradigm optimized with unscented Kalman filter technique
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Carbon dot nanofluid Polyethylene glycol Viscosity Unscented Kalman filter Artificial neural network Response surface methodology
پژوهشگران امین شهسوارگلدانلو (نفر اول)، محمد امین میرزایی (نفر دوم)، ایدین شهام (نفر سوم)، مهدی جامعی (نفر چهارم)، مسعود کرباسی (نفر پنجم)، فاطمه صیفکار (نفر ششم به بعد)، سعید عزیزان (نفر ششم به بعد)

چکیده

In the present study, the polyethylene glycol 200 (PEG200)-based nanofluid containing carbon dot (CD) nanoparticles was synthesized, and its rheological behavior at different temperatures and nanoparticle concentrations () was investigated. The values considered for were 0%, 1% and 3% and 7% the values considered for temperature were 20, 30, 40, 50 and 60 °C. It was observed that the PEG200 has a Newtonian behavior, and the nanofluid has a non-Newtonian behavior which is amplified with increasing temperature. Also, a decreasing and increasing trend of viscosity was observed with temperature and . As another novelty of this research, a robust novel artificial neural network (ANN) model integrated with an unscented Kalman filter (UKF-ANN) was presented for accurate estimation of the viscosity of the PEG-CD nanofluid based on , temperature, and shear rate as the input features. Besides, two efficient data-driven approaches, including classical perceptron ANN (MLP) and response surface methodology (RSM) were developed to examine and evaluate the robustness of UKF-ANN model. The statistical and infographic assessment indicated that the UKF-ANN outperformed the MLP and RSM, respectively.