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TECHNICAL PAPERS: Gas Turbines: CFD Modeling and Simulation

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

[+] Author and Article Information
Changduk Kong

Department of Aerospace Engineering, Chosun University, #375 Seosuk-dong, Dong-gu, Kwangju, Republic of Koreacdgong@mail.chosun.ac.kr

Jayoung Ki

Department of Aerospace Engineering, Chosun University, #375 Seosuk-dong, Dong-gu, Kwangju, Republic of Koreakjy2568110@nate.com

J. Eng. Gas Turbines Power 129(2), 312-317 (Jul 24, 2006) (6 pages) doi:10.1115/1.2436561 History: Received June 10, 2006; Revised July 24, 2006

In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be used. 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 a previous study. Among them the map generation method using experimental data and genetic algorithms had showed the possibility of composing the component maps from some random test data. However not only does this method need more experimental data to obtain more realistic component maps but it also requires some more calculation time to treat the additional random test data by the component map generation program. Moreover some unnecessary test data may introduced to generate inaccuracy in component maps. The map generation method called the system identification method using partially given data from the engine manufacturer (Kong and Ki, 2003, ASME J. Eng. Gas Turbines Power, 125, 958–979) 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 the component map conversely from some calculation results of a performance deck provided by the 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 unmanned aerial vehicle which has been developed by 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 the Estimated Engine Performance Program deck provided by the engine manufacturer. The performance results using the identified maps were also 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 above.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Engine schematic diagram of PW206C turbo shaft engine

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Figure 2

Flow chart of genetic algorithms applied to the present study

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Figure 3

MFP value and convergence of object function in growth of generation

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Figure 4

Compressor map generated by GAs and some data from EEPP

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Figure 5

Performance analysis results with varying gas generator rotational speed at sea level static standard atmospheric condition

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Figure 6

Performance analysis results with varying altitude at 100%rpm and static standard atmospheric condition

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Figure 7

Performance analysis results with varying flight Mach number at 100%rpm and 25,000ft standard atmospheric condition

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