Performance Assessment of a Triangular Integrated Collector Using Neural Networks

Abstract

A numerical study is achieved on a new shape of temperature saver solar collector using an artificial neural network. The storage collector is a triangle face and a right triangle pyramid for the volumetric shape. It is obtained by cutting a cube from one upper corner at 45°, down to the opposite hypotenuse of the base of the cube. The numerical study was carried out using the computational fluid dynamics code (ANSYS-Fluent) software with natural convection phenomenon in the pyramid enclosure. Elman backpropagation network is used for his ability to find the nearest solution with the smallest error rate. The network consists of three layers, each of different corresponding weights. The results show that the temperature and velocity distributions throughout the operating period were obtained. The influence of introducing an internal partition inside the triangular storage collector was investigated. Also the optimum geometry and location for this partition were obtained. The enhancement was best at y = 0.25 m, whereas the height of triangular collector was 0.5 m. The hourly system performance was evaluated for all test conditions. The performance of the NN to train a model for this work was 0.000207, while the error of the calculation was 1×10-2 as average.