Adaptation Method for Overall and Local Performances of Gas Turbine Engine Model

  • Sangjo Kim
  • Kuisoon Kim
  • Changmin Son
Original Paper


An adaptation method was proposed to improve the modeling accuracy of overall and local performances of gas turbine engine. The adaptation method was divided into two steps. First, the overall performance parameters such as engine thrust, thermal efficiency, and pressure ratio were adapted by calibrating compressor maps, and second, the local performance parameters such as temperature of component intersection and shaft speed were adjusted by additional adaptation factors. An optimization technique was used to find the correlation equation of adaptation factors for compressor performance maps. The multi-island genetic algorithm (MIGA) was employed in the present optimization. The correlations of local adaptation factors were generated based on the difference between the first adapted engine model and performance test data. The proposed adaptation method applied to a low-bypass ratio turbofan engine of 12,000 lb thrust. The gas turbine engine model was generated and validated based on the performance test data in the sea-level static condition. In flight condition at 20,000 ft and 0.9 Mach number, the result of adapted engine model showed improved prediction in engine thrust (overall performance parameter) by reducing the difference from 14.5 to 3.3%. Moreover, there was further improvement in the comparison of low-pressure turbine exit temperature (local performance parameter) as the difference is reduced from 3.2 to 0.4%.


Performance adaptation Gas turbine Engine model Adaptation factor 



Adaptation factor




High-pressure compressor


High-pressure turbine


Low-pressure turbine


Multi-island genetic algorithm


Corrected rotation speed


Total pressure ratio


Specific fuel consumption


High-pressure compressor exit total temperature


Low-pressure turbine exit total temperature


Engine nozzle exit total temperature


Corrected mass flow rate


Low spool speed

Station number


Engine inlet


Fan inlet


Splitter inlet


Inlet of duct in front of HPC for core side


HPC inlet


Combustor inlet


HPT inlet


Inlet of duct in front of LPT


LPT inlet


Inlet of duct in front of mixer


Mixer inlet


Nozzle inlet


Nozzle outlet


Outlet of bypass duct



This work was supported by a 2-Year Research Grant of Pusan National University


  1. 1.
    Colin JP, Brandon L, Mark LG, Danielle M, (2016) The future for industrial services: the digital twin.
  2. 2.
    General Electric (2016) Predix technology brief–digital twin.
  3. 3.
    General Electric (2017) Physical + digital = the new power couple.
  4. 4.
    Steinke RJ (1982) STGSTK: a computer code for predicting multistage axial flow compressor performance by a meanline stage stacking method. NASA TP-2020Google Scholar
  5. 5.
    Miller DC, Wasdell DL (1987) Off-design prediction of compressor blade losses. IMechE C279(87):249–260Google Scholar
  6. 6.
    Wright PI, Miller DC (1991) An improved compressor performance prediction model. IMechE C423(028):69–82Google Scholar
  7. 7.
    Schmidt JF (1995) Off-design computer code for calculating the aerodynamic performance of axial-flow fans and compressors. Report No. NASA CR-198362. NASA Lewis Research Center, Cleveland, OhioGoogle Scholar
  8. 8.
    Smith LH (1966) The radial-equilibrium equation of turbomachinery. J Eng Power 88(1):1–12CrossRefGoogle Scholar
  9. 9.
    Novak RA (1967) Streamline curvature computing procedures for fluid-flow problems. J Eng Power 89(4):478–490Google Scholar
  10. 10.
    Denton JD (1978) Throughflow calculations for transonic axial flow turbines. J Eng Power 100(2):212–218CrossRefGoogle Scholar
  11. 11.
    Petrovic MV, Wiedermann A (2015) Fully coupled through-flow method for industrial gas turbine analysis. In: Proceedings of ASME Turbo Expo 2015, Montréal, CanadaGoogle Scholar
  12. 12.
    Belamri T, Galpin P, Braune A, Cornelius C (2005) CFD analysis of a 15 stage axial compressor: Part I—Methods. In: Proceedings of ASME Turbo Expo 2005, Reno, Nevada, USAGoogle Scholar
  13. 13.
    Ikeguchi T, Matsuoka A, Sakai Y, Sakano Y, Yoshiura K (2012) Design and development of a 14-stage axial compressor for industrial gas turbine. In: Proceedings of ASME Turbo Expo 2012, Copenhagen, Denmark, USAGoogle Scholar
  14. 14.
    Cornelius C, Biesinger T, Galpin P, Braune A (2013) Experimental and computational analysis of a multistage axial compressor including stall prediction by steady and transient CFD methods. J Turbomach 136(6):061013CrossRefGoogle Scholar
  15. 15.
    Ghorbanian K, Gholamrezaei M (2007) Axial compressor performance map prediction using artificial neural network. In: Proceedings of ASME Turbo Expo 2007, Montreal, CanadaGoogle Scholar
  16. 16.
    Yu Y, Chen L, Sun F, Wu C (2007) Neural-network based analysis and prediction of a compressor’s characteristic performance map. Appl Energy 84(1):48–55CrossRefGoogle Scholar
  17. 17.
    Kong C, Ki J, Kang M (2003) A new scaling method for component maps of gas turbine using system identification. J Eng Gas Turbines Power 125(4):979–985CrossRefGoogle Scholar
  18. 18.
    Kong C, Ki J (2007) Study on component map identification from gas turbine performance deck data using hybrid method. Int J Turbo Jet Engines 24(3–4):171–182Google Scholar
  19. 19.
    Li YG, Ghafir MA, Wang L, Singh R, Huang K, Feng X (2011) Nonlinear multiple points gas turbine off-design performance adaptation using a genetic algorithm. J Eng Gas Turbines Power 133(7):071701CrossRefGoogle Scholar
  20. 20.
    Li YG, Ghafir MA, Wang L, Singh R, Huang K, Feng X, Zhang W (2012) Improved multiple point nonlinear genetic algorithm based performance adaptation using least square method. J Eng Gas Turbines Power 134(3):031701CrossRefGoogle Scholar
  21. 21.
    Tsoutsanis E, Meskin N, Benammar M, Khorasani, K (2014) An efficient component map generation method for prediction of gas turbine performance. In: Proceedings of ASME Turbo Expo 2014, Düsseldorf, GermanyGoogle Scholar
  22. 22.
    Tsoutsanis E, Meskin N, Benammar M, Khorasani K (2014) A component map tuning method for performance prediction and diagnostics of gas turbine compressors. Appl Energy 135(1):572–585CrossRefGoogle Scholar
  23. 23.
    Osterbeck P, Butzin EL, Johnson P (2009) Air vehicle sizing and performance modeling in NPSS. In: AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Denver, ColoradoGoogle Scholar
  24. 24.
    Kim S (2016) Performance evaluation of a turbofan engine considering the optimum scheduling of compressor variable guide vanes and bleed air (Ph. D thesis). Pusan National University, Pusan, Korea,Google Scholar
  25. 25.
    Koff BL (1989) F100-PW-229 higher thrust in same frame size. J Eng Gas Turbines Power 111(2):187–192CrossRefGoogle Scholar
  26. 26.
    Team NPSS (2012) NPSS user guide software release: NPSS 2.4.1. Ohio Aerospace Institute, ClevelandGoogle Scholar
  27. 27.
    Jones SM (2007) An introduction to thermodynamic performance analysis of aircraft gas turbine engine cycles using the numerical propulsion system simulation code. NASA TP-2007-214690Google Scholar
  28. 28.
    Miki M, Hiroyasu T, Kaneko M, Hatanaka K (1999) A parallel genetic algorithm with distributed environment scheme. In: Systems, Man, and Cybernetics, 1999. IEEE SMC’99 Conference Proceedings. 1999 IEEE International Conference on, vol 1, no 1, pp 695–700Google Scholar
  29. 29.
    Gallar L, Arias M, Pachidis V, Singh R (2011) Stochastic axial compressor variable geometry schedule optimization. Aerosp Sci Technol 15(5):366–374CrossRefGoogle Scholar
  30. 30.
    Kim S, Son C, Kim K (2017) Combining effect of optimized axial compressor variable guide vanes and bleed air on the thermodynamic performance of aircraft engine system. Energy 119(1):199–210CrossRefGoogle Scholar

Copyright information

© The Korean Society for Aeronautical & Space Sciences and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Aerospace EngineeringPusan National UniversityBusanRepublic of Korea
  2. 2.School of Mechanical EngineeringPusan National UniversityBusanRepublic of Korea

Personalised recommendations