Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10415918 | Energy Conversion and Management | 2005 | 10 Pages |
Abstract
This study presents an application of the artificial neural network (ANN) model using the back propagation (BP) learning algorithm to predict the performance (suction line outlet temperature and mass flow rate) of a non-adiabatic capillary tube suction line heat exchanger, basically used as a throttling device in small household refrigeration systems. Comparative studies were made by using an ANN model, experimental results and correlations to predict the performance. These studies showed that the proposed approach could successfully be used for performance prediction for the exchanger.
Related Topics
Physical Sciences and Engineering
Energy
Energy (General)
Authors
Yasar Islamoglu, Akif Kurt, Cem Parmaksizoglu,