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Application of Artificial Neural Networks to Predict Heat Transfer From Buried Pipe for Ground Source Heat Pump Applications

[+] Author Affiliations
Hakan Demir, Ş. Özgür Atayılmaz, Özden Agra, Ahmet Selim Dalkılıç

Yıldız Technical University, İstanbul, Turkey

Paper No. HT2013-17729, pp. V004T14A030; 8 pages
doi:10.1115/HT2013-17729
From:
  • ASME 2013 Heat Transfer Summer Conference collocated with the ASME 2013 7th International Conference on Energy Sustainability and the ASME 2013 11th International Conference on Fuel Cell Science, Engineering and Technology
  • Volume 4: Heat and Mass Transfer Under Extreme Conditions; Environmental Heat Transfer; Computational Heat Transfer; Visualization of Heat Transfer; Heat Transfer Education and Future Directions in Heat Transfer; Nuclear Energy
  • Minneapolis, Minnesota, USA, July 14–19, 2013
  • Conference Sponsors: Heat Transfer Division
  • ISBN: 978-0-7918-5550-8
  • Copyright © 2013 by ASME

abstract

The earth is an energy resource which has more suitable and stable temperatures than air. Ground Source Heat Pumps (GSHPs) were developed to use ground energy for residential heating. The most important part of a GSHP is the Ground Heat Exchanger (GHE) that consists of pipes buried in the soil and is used for transferring heat between the soil and the heat exchanger of the GSHP. Soil composition, density, moisture and burial depth of pipes affect the size of a GHE. Design of GSHP systems in different regions of US and Europe is performed using data from an experimental model. However, there are many more techniques including some complex calculations for sizing GHEs. An experimental study was carried out to investigate heat transfer in soil. A three-layer network is used for predicting heat transfer from a buried pipe. Measured fluid inlet temperatures were used in the artificial neural network model and the fluid outlet temperatures were obtained. The number of the neurons in the hidden layer was determined by a trial and error process together with cross-validation of the experimental data taken from literature evaluating the performance of the network and standard sensitivity analysis. Also, the results of the trained network were compared with the numerical study.

Copyright © 2013 by ASME

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