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Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data

[+] Author Affiliations
Changduk Kong, Jayoung Ki

Chosun University, Kwangju, Republic of Korea

Changho Lee

Korea Aerospace Research Institute, Taejeon, Republic of Korea

Paper No. GT2006-90975, pp. 377-383; 7 pages
  • ASME Turbo Expo 2006: Power for Land, Sea, and Air
  • Volume 4: Cycle Innovations; Electric Power; Industrial and Cogeneration; Manufacturing Materials and Metallurgy
  • Barcelona, Spain, May 8–11, 2006
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4239-8
  • Copyright © 2006 by ASME


In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be needed. Because the components map is an engine manufacturer’s propriety obtained from many experimental tests with high cost, they are not provided to the customer generally. Some scaling methods for gas turbine component maps using experimental data or data partially given by engine manufacturers had been proposed in previous study. Among them the map generation method using experimental data and genetic algorithms (Kong et al., 2004) had showed a possibility composing the component maps from some random test data. However not only this method needs more experimental data to obtain the more realistic component maps but also it requires some more calculation time to treat the additional random test data by component map generation program. Moreover some unnecessary test data may introduce to generate inaccuracy in component maps. And the map generation method called as the system identification method using partially given data from engine manufacturer (Kong et al., 2003) can improve the traditional scaling methods by multiplying the scaling factors at design point to off-design point data of the original performance maps, but some reference map data at off-design points should be needed. In this study a component map generation method which may identify component map conversely from some calculation results of a performance deck provided by engine manufacturer using the Genetic Algorithms was newly proposed to overcome the previous difficulties. As a demonstration example for this study, the PW206C turbo shaft engine for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute) was used. In order to verify the proposed method, steady-state performance analysis results using the newly generated component maps were compared with them performed by EEPP (Estimated Engine Performance Program) deck provided by engine manufacturer. And also the performance results using the identified maps were compared with them using the traditional scaling method. In this investigation, it was found that the newly proposed map generation method would be more effective than the traditional scaling method and the methods explained at the above.

Copyright © 2006 by ASME



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