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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. [ABSTRACT FROM AUTHOR]

The exergetic performance of a roughened solar air heater has been predicted using an artificial neural network (ANN) model. The exergetic values have been derived from the data collected by conducting experiments on solar air heater using transverse wire ribs as a roughness element on the absorber plate with relative roughness heights 0.009571, 0.01400, 0.017142 and constant relative roughness pitch 10, at local weather condition of Jamshedpur (22.77° N and 86.14° E), India. The ANN model was structured with seven input parameters such as experimental time, solar radiation intensity, roughness size, atmospheric temperature, mean air temperature, absorber plate temperature and mass flow rate of air in input layer and five parameters such as exergy inlet, exergy outlet, exergy efficiency, exergy destruction and improvement potential in output layer. Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Polak-Ribiére conjugate gradient (CGP) learning algorithms have been used for training the proposed model. The LM learning algorithms with 7 neurons in the hidden layer are found to be optimal on the basis of statistical error analysis. The value of R2 for the predicted model of exergy outlet, exergy inlet, exergetic efficiency, exergy destruction and improvement potential were 0.99584, 0.99997, 0.99517, 0.99997 and 0.99983 respectively, which yields the satisfactory performance of the ANN model. The values of mean square error (MSE), the coefficient of variance (COV), the and mean relative error (MRE) are found to be very low for predicted values of exergetic performance parameters that are desirable. The statistical results exhibit that the proposed multi-layered perceptron (MLP) ANN model successfully predicts the exergetic performance of a roughened solar air heater. [ABSTRACT FROM AUTHOR]