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Similarity Based Performance Prediction of Organic Rankine Cycle Turbine Using Air Condition Data

[+] Author Affiliations
Tao Chen, Weilin Zhuge, Lei Zhang, Yangjun Zhang

Tsinghua University, Beijing, China

Paper No. AJKFluids2015-09097, pp. V01AT09A002; 9 pages
doi:10.1115/AJKFluids2015-09097
From:
  • ASME/JSME/KSME 2015 Joint Fluids Engineering Conference
  • Volume 1A: Symposia, Part 2
  • Seoul, South Korea, July 26–31, 2015
  • Conference Sponsors: Fluids Engineering Division
  • ISBN: 978-0-7918-5721-3
  • Copyright © 2015 by KSME

abstract

The Organic Rankine Cycle (ORC) is an effective technology to recover the waste heat of the internal combustion engine (ICE) exhaust gas and coolant water. Performance prediction and matching of the expander is a key issue, during designing the ORC system. Radial turbines have the advantage in the ORC system for the ICE waste heat recovery because of its small size and high efficiency compared with volumetric expanders. But the ORC turbines design database is insufficient and the organic gas test rig is difficult to be constructed. As a result, this paper, based on similarity theory, establishes a performance prediction method for fully using the mature design and experimental datum of air turbines. By this prediction method, turbine performance can be obtained when its working fluid is changed from the air to the organic gas. Computational fluid dynamics (CFD) solution has been obtained for a calculation example, and the validity of the turbine performance prediction method is demonstrated by comparing the ORC turbine’s predicted performance with the simulated performance. The results show that almost all of the performance parameter’s predictive deviation is less than 5%. This paper comes to a conclusion that designing an R123 gas turbine is similar with designing an air turbine whose rotation rate is twice that of the former, expansion ratio is twice that of the former, power is four times twice that of the former at the same mass flow rate.

Copyright © 2015 by KSME

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