مشخصات پژوهش

عنوان	GMDH-type neural network modeling and genetic algorithm-based multi-objective optimization of thermal and friction characteristics in heat exchanger tubes with wire-rod bundles
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	GMDH-type neural network; genetic algorithm; heat exchanger; optimization
چکیده	The group method of data handling (GMDH) technique was used to predict heat transfer and friction characteristics in heat exchanger tubes equipped with wire-rod bundles. Nusselt number and friction factor were determined as functions of wire-rod bundle geometric parameters and Reynolds number. The performance of the developed GMDH-type neural networks was found to be superior in comparison with the proposed empirical correlations. For optimization, the genetic algorithm-based multi-objective optimization was applied.
پژوهشگران	اسمیت یامساآرد (نفر چهارم)، مهدی پرویزی (نفر سوم)، رضا بیگزاده (نفر دوم)، مسعود رحیمی (نفر اول)