Regular Article

ANN model to predict the performance of parabolic dish collector with tubular cavity receiver

¹ Department of Mechanics of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
² Department of Renewable Energies, Faculty of New Sciences & Technologies, University of Tehran, Tehran, Iran
³ Department of Water Structures Engineering, Tarbiat Modares University, Tehran, Iran
⁴ Department of Mechanics of Biosystem Engineering, Tarbiat Modares University, Tehran, Iran
⁵ Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

^* e-mail: mohammadhosein.ahmadi@gmail.com

Received: 13 October 2016
Accepted: 16 March 2017

Abstract

In this study, the thermal performance of a parabolic dish concentrator with a rectangular-tubular cavity receiver was investigated. The thermal oil was used as the working fluid in the solar collector system. The performance of the cavity receiver was studied in two ways as a numerical modeling method and the artificial neural networks (ANNs) methodology. In this study, three variable parameters including the different tube diameters equal to 5, 10, 22, and 35 mm, and different cavity depths equal to 0.5a, 0.75a, 1a, 1.5a, and 2a were considered. The purpose of this study is the prediction of the thermal performance of the cavity receiver in different amounts of solar irradiance, the cavity depth, and the diameter of tube by the ANN methodology. The main benefit of the ANN method, in comparison with the numerical modeling method, is the calculation time and cost saving. The results reveal that the ANN method can accurately predict the thermal performance of the cavity receiver at different variable parameters of the cavity depth, and tube diameter with R² = 0.99 for each prediction.