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Cycle Modeling and Optimization of an Integrally Geared sCO2 Compander

[+] Author Affiliations
Jeffrey Bennett, Jason Wilkes, Timothy Allison

Southwest Research Institute, San Antonio, TX

Robert Pelton, Karl Wygant

Hanwha Techwin, Houston, TX

Paper No. GT2017-63707, pp. V009T38A014; 8 pages
  • ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
  • Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5096-1
  • Copyright © 2017 by ASME


This paper presents the selection of a system configuration and off-design control method for an integrally geared compander based on cycle modeling and optimization. The goal of the cycle modeling was to determine a cycle configuration that would reach an efficiency of 50% at design conditions and to optimize off-design control to maintain high efficiencies. The compander is being developed for use in a concentrated solar power supercritical carbon dioxide power plant with expected turbine inlet temperatures of 705°C and utilizing dry cooling leading to compressor inlet temperature varying between 35°C and 55°C. The compander is unique as it consists of eight turbomachinery stages on four pinions all being driven by a single bull gear. The separate stages offer the opportunity to consider a variety of flow splits and cycle configurations including intercooling and multiple stages of reheat.

Cycle modeling was conducted in two stages: on-design and off-design. On-design modeling was simulated with all components operating at their design point. This was used to compare the performance of different cycle configurations and design temperatures. Off-design modeling was then performed to investigate the temperature dependence of the cycle efficiency and power output and to develop a control strategy. Strategies considered and discussed include: turbine bypass, compressor recycle, inlet guide vanes, and inventory control. To determine the best operating conditions for each configuration and control strategy, a genetic algorithm was implemented to optimize the cycle performance across the range of operating temperatures being considered. The final selection of cycle configuration, design temperature and control strategy is also presented.

Copyright © 2017 by ASME



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