A thermodynamically equivalent transformation method for the design and performance analysis of absorption cycles
File(s)
Accepted version
Author(s)
Zhang, Fan
Yin, Yonggao
Markides, Christos N
Wang, Xiaolin
Type
Journal Article
Abstract
Absorption cycles are a very promising technology for the provision of heating or cooling. Systems based on such cycles are capable of utilizing environmentally-friendly thermal energy sources such as low-grade solar or waste heat. Increasingly advanced absorption cycles with complex configurations are being proposed to meet the diversified demands of modern energy systems, however, a convenient, rapid yet accurate method for the design and performance analysis of these complex cycles is lacking. In this paper, a thermodynamically equivalent transformation method is proposed which decomposes complex cycles into mutually coupled basic single-stage cycles. Based on the decomposition transformation, a generalized method for the fast calculation of the COP of complex cycles under both ideal and practical conditions was also established and verified. Two case studies on the configuration design, performance analysis and optimization of complex absorption cooling cycles are performed to demonstrate the applicability of the proposed method. The results show that the proposed thermodynamically equivalent transformation method can make the decomposition of complex cycles convenient and effective. Although the present paper focuses on absorption cooling cycles, the method is equally applicable to absorption heat pump cycles. The established fast COP prediction method is computationally efficient and accurate for the performance analysis of absorption cycles with complex configurations. This study provides a powerful tool for the design, performance analysis and optimization of next-generation advanced absorption cycles.
Date Issued
2025-03-01
Date Acceptance
2025-01-25
Citation
Energy Conversion and Management, 2025, 327
ISSN
0196-8904
Publisher
Elsevier
Journal / Book Title
Energy Conversion and Management
Volume
327
Copyright Statement
Copyright © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Article Number
119587
Date Publish Online
2025-02-01