Details

Autor(en) / Beteiligte

• Zhou, G.
• Ye, Y.
• Zuo, W.
• Zhou, X.
• Wu, X.

Titel

Fast and efficient prediction of finned-tube heat exchanger performance using wet-dry transformation method with nominal data

Ist Teil von

• Applied thermal engineering, 2018-12, Vol.145, p.133-146

Ort / Verlag

Oxford: Elsevier Ltd

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •A fast and efficient water-to-air finned-tube heat exchanger model is proposed.•The model uses wet-dry transformation method under wet-cooling conditions.•It is capable of predicting heat transfer performance with only nominal data.•It does not need geometric data or full performance data of the heat exchanger.•It is evaluated by comparing with experimental data and an existing model. Water-to-air finned-tube heat exchanger (FTHE) is a common component in air-conditioning systems. Mathematical models of water-to-air FTHE under the wet-cooling condition are necessary in evaluating the performance of air-conditioning system with water-to-air FTHEs during the system design phase. However, existing water-to-air FTHE models are computationally expensive and require detailed geometric data, which hinder the model applications during the system design phase. To address the above limitations, the current paper proposes a new water-to-air FTHE model which is computationally efficient, relatively accurate and only requires nominal data as inputs. The new water-to-air FTHE model is derived using wet-dry transformation method and the heat transfer process is calculated using the nominal data. Then the model is implemented in Modelica, which is an equation-based, object-oriented modeling language. In addition, experimental measurements of a water-to-air FTHE are conducted. The new model is then evaluated by experimental data and an existing model. The results show that the relative deviations of outlet temperatures and heat transfer rate between the modeled and experimental data are within 7% and 11%, respectively, which is much better than the existing model (19% and 13%). In addition, the new model is 1047 times faster than the existing model.