Numerical Investigation of Heat Transfer Characteristics of Supercritical CO2 Tube in Combustion Chamber of Coal-Fired Boiler View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-08

AUTHORS

Jimin Wang, Xue Chen, Chao Zhang, Mingyan Gu, Huaqiang Chu

ABSTRACT

To achieve compact structure, light weight, and high thermal efficiency for the coal-fired boiler, the supercritical CO2 power cycle has been considered as one of the promising alternatives in the coal-fired power conversion system. One of the major problems concerning fossil fuel powered plants is the safety of the water wall in boiler design. In this work, the heat transfer characteristics of the supercritical CO2 tube in the combustion chamber were determined through the low Reynolds number k-ε model, the gas real model and the P-1 radiation model. The study covered the supercritical CO2 tube and the fins, and the annulus flue gas passage was also included. The wall temperature and the heat transfer coefficient were compared against those obtained from the experiments. Based on the examinations of the calculated flow and turbulence fields, the distributions of the velocity and the temperature inside the supercritical CO2 tube in the combustion chamber were resolved numerically. Moreover, the effects of the heat transfer coefficient on the heat transfer characteristics were also discussed. And it was numerically focused on the influence of the inclined angle on the flow and the heat transfer of the supercritical CO2 tube. The results show that the heat transfer coefficient keeps namely constant as the increasing inclined angle. It would help to better understand the heat transfer mechanism of unique characteristics of supercritical CO2 above the pseudo-critical temperature, which may provide the corresponding theoretical basis on the optimization design of the coal-fired boiler. More... »

PAGES

1-12

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11630-019-1106-8

DOI

http://dx.doi.org/10.1007/s11630-019-1106-8

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https://app.dimensions.ai/details/publication/pub.1112634342


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