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Design Parameters Prediction of New Type Gas Turbine Based on a Hybrid GRA-SVM Prediction Model

[+] Author Affiliations
Tingting Wei, Dengji Zhou, Jinwei Chen, Huisheng Zhang

Shanghai Jiao Tong University, Shanghai, China

Yaoxin Cui

Shanghai Turbine Company, Shanghai, China

Paper No. GT2017-63905, pp. V003T06A014; 7 pages
  • ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
  • Volume 3: Coal, Biomass and Alternative Fuels; Cycle Innovations; Electric Power; Industrial and Cogeneration Applications; Organic Rankine Cycle Power Systems
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5083-1
  • Copyright © 2017 by ASME


Since the late 1930s, gas turbine has begun to develop rapidly. To improve the economic and safety of gas turbine, new types were generated frequently by Original Equipment Manufacture (OEM). In this paper, a hybrid GRA-SVM prediction model is established to predict the main design parameters of new type gas turbines, based on the combination of Grey Relational Analysis (GRA) and Support Vector Machine (SVM). The parameters are classified into two types, system performance parameters reflecting market demands and technology development, and component performance parameters reflecting technology development and coupling connections. The regularity based on GRA determines the prediction order, then new type gas turbine parameters can be predicted with known system parameters. The model is verified by the application to SGT600. In this way, the evolution rule can be obtained with the development of gas turbine technology, and the improvement potential of several components can be predicted which will provide supports for overall performance design.

Copyright © 2017 by ASME



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