In present article a mathematical model of arc shape wire roughened solar air heater, on the basis of energy and exergy output rates, entropy generation rate and augmentation entropy generation number, has been developed. A parametric study leading to entropy generation minimization has also been performed. In the analysis the geometric and operating parameters which have been considered as variable are: inlet air temperature, duct depth, collector width to duct depth ratio, mass flow rate per unit collector area, and temperature rise parameter . Results have been presented to see the effects of these values on the energy and exergy output rates of the roughened solar air heater. Effect of different values of wire rib roughness parameters on entropy generation has also been presented. Finally, design curves and optimization for different rib roughness parameters on the basis of minimum entropy generation number with temperature rise parameter, have been presented and optimum values also have been found out 0.004 to 0.010 (Km2)/W. The entropy generation rate obtained for the system, in the present work has been compared with those obtained for solar air heater with different roughness geometries on absorber plates available in the literature for common roughness parameters and operating parameters which validate the present results.

The objective of present work is to predict the thermal performance of wire screen porous bed solar air heater using artificial neural network (ANN) technique. This paper also describes the experimental study of porous bed solar air heaters (SAH). Analysis has been performed for two types of porous bed solar air heaters: unidirectional flow and cross flow. The actual experimental data for thermal efficiency of these solar air heaters have been used for developing ANN model and trained with Levenberg-Marquardt (LM) learning algorithm. For an optimal topology the number of neurons in hidden layer is found thirteen (LM-13).The actual experimental values of thermal efficiency of porous bed solar air heaters have been compared with the ANN predicted values. The value of coefficient of determination of proposed network is found as 0.9994 and 0.9964 for unidirectional flow and cross flow types of collector respectively at LM-13. For unidirectional flow SAH, the values of root mean square error, mean absolute error and mean relative percentage error are found to be 0.16359, 0.104235 and 0.24676 respectively, whereas, for cross flow SAH, these values are 0.27693, 0.03428, and 0.36213 respectively. It is concluded that the ANN can be used as an appropriate method for the prediction of thermal performance of porous bed solar air heaters.