Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters
Xianda Cheng,
Haoran Zheng,
Wei Dong and
Xuesen Yang
Energy, 2023, vol. 265, issue C
Abstract:
The performance of marine intercooled cycle gas turbines (ICGTs) is affected by atmospheric and sea conditions. Gas turbine operators have to rely on complicated and unfriendly simulation models to predict the performance of ICGTs under different ambient conditions. Aiming at this problem, this paper introduces a novelty fast prediction method based on similarity theory, which can help gas turbine operators realize performance parameters prediction of ICGTs on the spot. For this purpose, the similarity theory is firstly extended to ICGTs. The similarity parameters corresponding to seawater flow rate, glycol solution flow rate, and seawater temperature are derived using Buckingham's Pi Theorem. On this basis, the performance prediction formula of ICGTs is developed. The second-order and dissimilar effects of ICGTs are fully considered in this formula to improve the prediction accuracy. The values of the unknown coefficients in the formula can be obtained by fitting from a small amount of test data. Finally, the high-fidelity ICGT simulation model and the actual ambient conditions verify the proposed method. The results show that the proposed method has good practicability and accuracy, which provides a new approach to predicting marine ICGT performance.
Keywords: Performance prediction; Gas turbine; Intercooled cycle; Buckingham's Pi theorem; Simulation model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222032881
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:265:y:2023:i:c:s0360544222032881
DOI: 10.1016/j.energy.2022.126402
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().