مشخصات پژوهش

عنوان	Computational fluid dynamic assists artificial neural network and genetic algorithm approaches for modeling the thermal and flow characteristics of air forced convection on interrupted plate fins
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Computational fluid dynamic; artificial neural network; genetic algorithm; interrupted plate fins
چکیده	This study reports the application of Computational Fluid Dynamics (CFD) as a data provider for Artificial Neural Networks (ANNs). Four interrupted plate fins with different geometric parameters were studied experimentally. The CFD modeling was undertaken and the simulation results were verified using the experimental data. After validating the model, more fins with various geometries were modeled. The numerically validated data were used for developing two ANNs. Reynolds number and geometric parameters were determined as ANN inputs, and Nusselt number (Nu) and friction factor (f) were outputs. Moreover, the ANNs were compared to genetic algorithm-based correlations and the ANNs appeared more accurate than the correlations.
پژوهشگران	عمار السرافی (نفر چهارم)، امید جعفری (نفر سوم)، مسعود رحیمی (نفر دوم)، رضا بیگزاده (نفر اول)