A New Windage Loss Model for S-CO2 Turbomachinery Design

A supercritical CO2 (S-CO2) Brayton cycle is a compact and simple power conversion system with competitive efficiency. However, the strong real gas effects of S-CO2 pose challenges to the design of a cycle and its components. In particular, designing turbomachinery for expansion and compression proc...

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Bibliographic Details
Published in:Applied sciences 2023-07, Vol.13 (13), p.7463
Main Authors: Kim, Dokyu, Jeong, Yongju, Son, In Woo, Lee, Jeong Ik
Format: Article
Language:English
Subjects:
Brayton cycle
Carbon dioxide
Cooling
Design
Efficiency
Energy conversion
Heat
Heat generation
Real gases
Reynolds number
supercritical carbon dioxide Brayton cycle (S-CO2 Brayton cycle)
Turbomachinery
windage loss
Windage losses
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Summary:A supercritical CO2 (S-CO2) Brayton cycle is a compact and simple power conversion system with competitive efficiency. However, the strong real gas effects of S-CO2 pose challenges to the design of a cycle and its components. In particular, designing turbomachinery for expansion and compression processes has to accurately reflect real gas effects. Windage loss is one of the major losses that affects the motor load and heat generation in turbomachinery. The windage loss has a substantial impact on the overall turbomachinery efficiency especially in an S-CO2 power cycle since the windage loss is reported to be the dominant loss mechanism due to high fluid density and high rotational speed. Therefore, an accurate windage loss model reflecting the real gas effect of S-CO2 is essential to obtaining an optimal design of turbomachinery as well as maximizing the performance of an S-CO2 power cycle. In this study, existing windage loss models are first compared to the recently obtained data from S-CO2 windage loss experiments conducted by the KAIST research team under S-CO2 conditions in order to understand the turbomachinery performance uncertainty caused by the windage loss models. This is followed by proposing a new windage model which explains data better.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13137463