- Tytuł:
- Modelling of back propagation neural network to predict the thermal performance of porous bed solar air heater
- Autorzy:
-
Ghritlahre, Harish Kumar
Prasad, Radha Krishna - Powiązania:
- https://bibliotekanauki.pl/articles/240570.pdf
- Data publikacji:
- 2019
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
solar air heater
porous bed
thermal performance
artificial neural network
Levenberg-Marquardt algorithm - Opis:
- 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.
- Źródło:
-
Archives of Thermodynamics; 2019, 40, 4; 103-128
1231-0956
2083-6023 - Pojawia się w:
- Archives of Thermodynamics
- Dostawca treści:
- Biblioteka Nauki
Artykuł