| Abstract: | Abstract In the last decade, Artificial Neural Networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, speed and nonlinearity. This article describes an alternative approach to assess two types of hybrid solar collector/heat pipe systems (plate heat pipe type and tube heat pipe type) using ANNs. Multiple Layer Perceptrons (MLPs) and Radial Basis Networks (RBFs) were considered. The networks were trained using results from mathematical models generated by Monte Carlo simulation. The mathematical models were based on energy balances and resulted in a system of nonlinear equations. The solution of the models was very sensitive to initial estimates, and convergence was not obtained under certain conditions. Between the two neural models, MLPs performed slightly better than RBFs. It can be concluded that similar configurations were adequate for both collector systems. It was found that ANNs simulated both collector efficiency and heat output with high accuracy when “unseen” data were presented to the networks. An important advantage of a trained ANN over the mathematical models is that convergence is not an issue and the result is obtained almost instantaneously. |
