Thermodynamic analysis and design optimisation of a cross flow air to air membrane enthalpy exchanger
This paper presents a thermodynamic analysis and design optimisation of a cross flow air to air membrane enthalpy exchanger (MEE). The entropy generation rate for simultaneous heat and moisture transfer was first derived based on the second law of thermodynamics and the NTU-effectiveness method. Two...
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Veröffentlicht in: | Energy (Oxford) 2020-07, Vol.202, p.117691, Article 117691 |
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Sprache: | eng |
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Zusammenfassung: | This paper presents a thermodynamic analysis and design optimisation of a cross flow air to air membrane enthalpy exchanger (MEE). The entropy generation rate for simultaneous heat and moisture transfer was first derived based on the second law of thermodynamics and the NTU-effectiveness method. Two potential objective functions based on the dimensionless entropy generation rates were used in this study. A parametric analysis was carried out to explore the effects of the effectiveness and operating conditions on the dimensionless entropy generation rates of the MEE and identify the appropriate objective function to be used in the optimisation. Global sensitivity analysis and a genetic algorithm were used to determine the key design parameters and obtain their optimal values, respectively. An illustrative example was lastly used to demonstrate the benefit of the optimisation. It was found that the operating conditions showed significant impacts on the entropy generation rates inside MEEs. Using the optimal values identified can reduce the entropy generation by 19.8% and 29.7% for the cooling and heating modes respectively, as compared to a baseline design. The findings obtained can be used to generate a deep understanding of the relationship between entropy generation rate and the operating conditions/design parameters of MEEs.
•Thermodynamic performance of a cross flow membrane enthalpy exchanger was evaluated.•Effect of operating conditions on entropy generation of the exchanger was examined.•A design strategy was developed to minimise entropy generation of the exchanger.•The optimisation problem was solved using a genetic algorithm. |
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ISSN: | 0360-5442 1873-6785 |
DOI: | 10.1016/j.energy.2020.117691 |