Search

• Analytical investigation has been studied of parallel flow solar air heater. • Thermal Efficiency of double flow conventional SAH and roughened SAH are compared. • It has been found that thermal efficiency of PFRSAH is nearly 8–10% more than the PFSSAH. • Optimum thermohydraulic efficiency nearly Reynolds number 29,000. • The parameters influencing the Nusselt number and friction factor are discussed. This paper deals with the theoretical analysis of a parallel flow solar air heater having an absorber plate roughened with arc shaped wires. The absorber plate that forms the upper and lower ducts is roughened on its upper side only. A mathematical model describing the fluid flow and heat transfer characteristics of such a parallel flow roughened absorber plate solar air heater has been developed to study the effects of various operating and systems parameters on thermal performance of the solar air heater. The governing energy equations have been solved by iterative method using computer codes in MATLAB. A comparison between the theoretical results obtained and experimental results available in the literature has been made which shows a good agreement between the results. The thermal efficiency of parallel flow roughened solar air heater has been found nearly 8–10% higher than the smooth parallel flow solar air heater operating under similar conditions. It has been found that based on the thermohydraulic efficiency criteria, parallel flow roughened solar air heater is not beneficial for higher Reynolds number (Re > 29000) as compared to solar air heater having smooth absorber plate and this fact can be considered for designing roughened parallel flow solar air heaters. [ABSTRACT FROM AUTHOR]

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 coeffi- cient 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. [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]

In the present paper, a comprehensive investigation on thermal and thermohydraulic performance of solar collector for heating air having circular wire rib roughness in the form of arc shape on the back side of absorber plate, has been carried out. A mathematical model incorporating the operating and system parameters has been developed and the results have been computed by using MATLAB for specified range of these parameters. A conventional solar air heater working under similar conditions has also been considered for the purpose of comparison. At rib height-to-duct hydraulic diameter ratio = 0.0422 and flow-attack-angle = 0.3333 the values of maximum thermal and effective efficiencies were found to be 79.84% and 75.24% respectively for the range of parameters used in the investigation. Further, the thermal efficiency, obtained in the present work has been compared with those obtained for other roughness geometries available in the literature for common roughness parameters and operating parameters to validate the results. Optimization of different parameters of wire roughness for optimum thermohydraulic (effective) efficiency of solar air heater duct has also been investigated. [ABSTRACT FROM AUTHOR]