4.7 Article

Modeling and experimental testing of periodic flow regenerators for sCO2 cycles

期刊

APPLIED THERMAL ENGINEERING
卷 147, 期 -, 页码 789-803

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2018.10.110

关键词

Supercritical CO2; Regenerator; Periodic flow; Testing

资金

  1. U.S. Department of Energy [DE-EE0007120]
  2. agency of the United States Government

向作者/读者索取更多资源

Supercritical CO2 cycles have the ability to reach high efficiency because of their high turbine inlet temperatures. However, high effectiveness recuperators ( > 90%) are needed to achieve high efficiencies. Periodic flow regenerators have been proposed as an alternative to conventional heat exchanger designs such as Printed Circuit Heat Exchangers (PCHEs) or micro-tube heat exchangers. Regenerators use a packed bed of spheres to alternatively store and release heat. Since the hot and cold fluid streams are not in direct contact, the design of the regenerator is much simpler and less expensive than a recuperator. A model of the regenerator has been created that can predict the effectiveness, pressure drop, and carryover when given the regenerator size and operating conditions. To verify this model, an experimental test facility has been constructed that is capable of testing regenerators at temperatures up to 550 degrees C and pressures up to 2400 psi at a heat transfer rate of approximately 10 kW. Experimental data was collected and compared to the model predictions. The model was able to predict effectiveness to within approximately 2% and pressure drop to within approximately 20%; both results are acceptable. However, it was necessary to develop a correction for carryover to account for the differences between the model predictions and experimental data. The final result of this work is a verified model of the regenerator that can be used to quickly and accurately design and optimize a regenerator for a sCO(2) Brayton cycle.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据