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Study on Estimations of Initial Mass Fractions of CH4/O2 in Diffusion-Controlled Turbulent Combustion Using Inverse Analysis

확산지배 난류 연소현상에서 역해석을 이용한 CH4/O2의 초기 질량분율 추정에 관한 연구

  • Lee, Kyun-Ho (Korea Aerospace Research Institute) ;
  • Baek, Seung-Wook (Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2009.03.18
  • Accepted : 2010.03.22
  • Published : 2010.07.01

Abstract

The major objective of the present study is to extend the applications of inverse analysis to more realistic engineering fields with a complex combustion process rather than the traditional simple heat-transfer problems. In order to do this, the unknown initial mass fractions of $CH_4/O_2$ are estimated from the temperature measurement data by inverse analysis in the practical diffusion-controlled turbulent combustion problem. In order to ensure efficient inverse analysis, the repulsive particle swarm optimization (RPSO) method, which belongs to the class of stochastic evolutionary global optimization methods, is implemented as an inverse solver. Based on this study, it is expected that useful information can be obtained when inverse analysis is used in the diagnosis, design, or optimization of real combustion systems involving unknown parameters.

본 연구에서는 기존의 역열전달 문제(inverse heat transfer problem)와 같이 역해석(inverse analysis)을 통해 미지의 파라미터를 추정(estimation)하는 개념을 복잡한 연소문제에 도입하였다. 기존의 연구에서는 역해석 기법을 연소문제 자체에 보다는 대부분 연소현상을 동반한 복사열전달과 같은 역열전달 문제에 국한해서 적용하고 있기 때문에, 열전달 문제에 한정되어 사용되고 있는 기존의 역해석을 새로운 공학문제에 확장하여 적용함과 동시에 효율적인 연소기 설계 및 최적화 개념을 제시하는데 본 연구의 의의가 있다고 할 수 있다. 이를 위해 실제적으로 많이 사용하고 있는 축대칭 원통형 연소기 내부로 주입되는 메탄($CH_4$)과 산소($O_2$) 성분의 초기 질량분율 값을 연소기 입구 근방에서 측정한 개스의 온도 데이터를 이용하여 역추정하였다. 이때, 복잡한 확산지배 연소 현상을 효율적으로 역해석하기 위해 최적화 방법 중의 하나인 반발 입자 군집 최적화 방법을 역해석 기법으로 적용하였다.

Keywords

References

  1. Lewis, M. H. and Smoot, L. D., 1981,“Turbulent Gaseous Combustion Part I : LocalSpecies Concentration Measurements," Combust.Flame, Vol. 42, No. 2, pp. 183-196. https://doi.org/10.1016/0010-2180(81)90157-7
  2. Gran, I. R., Mellen, M. C., and Magnussen, B.F., 1994, "Numerical Simulation of Local ExtinctionEffects in Turbulent Combustor Flows of Methaneand Air," 25th Symposium (International) onCombustion, pp. 1283-1291.
  3. Aslanyan, G. S. and Maikov, I. L., 1998,"Numerical Simulation of Turbulent GaseousCombustion in Axially Symmetric CombustionChambers," Combustion, Explosion, and ShockWaves, Vol. 34, pp. 369-377. https://doi.org/10.1007/BF02675601
  4. Richards, R. F., Ribail, R. T., Bakkom, A. W.,and Plumb. O. A., 1997, “Fire Detection,Location and Heat Release Rate Through InverseProblem Solution," Fire Safety Journal, Vol. 28,No. 4, pp. 323-350. https://doi.org/10.1016/S0379-7112(97)00005-2
  5. Fan, H., Li, B., Yang, L., and Wang, R., 2002,“Simultaneous Estimation of the Temperature andHeat Rate Distributions within the CombustionRegion by a New Inverse Radiation Analysis," J.Quant. Spectrosc. Ra., Vol. 74, pp. 75-83. https://doi.org/10.1016/S0022-4073(01)00253-9
  6. Zheng, Y. and Gore, J. P., 2005, “Measurementsand Inverse Calculations of Spectral RadiationIntensities of a Turbulent Ethylene/Air Jet Flame,"Proc. of the Combustion Institute, Vol. 30, pp.727-734. https://doi.org/10.1016/j.proci.2004.08.255
  7. Han, S. H., Kim, D. M., Baek, S. W., andKim, C. Y., 2006, "3D Unsteady NumericalAnalysis of Slab Heating Characteristics in aReheating Furnace for Steel Mill Company," J. ofthe KSC, Vol. 11, No. 1, pp. 34-42.
  8. Oh, C. B. and Lee, C. E., 2001, "NumericalSimulation of Unsteady CH4/Air Jet DiffusionFlame," Trans. of the KSME(B), Vol 25, No. 8,pp.1087-1096.
  9. Magnussen, B. F. and Hjertager, B. H., 1976, "OnMathematical Modeling of Turbulent Combustionwith Emphasis on Soot Formation and Combustion,"16th Symposium (International) on Combustion, pp.719-729.
  10. Byggstoyl, S. and Magnussen, B. F., 1983, "AModel for Flame Extinction in Turbulent Flow,"4th Symposium (International) on Turbulent ShearFlows, pp. 1032-1038.
  11. Ferziger, J. H. and Peric, M., 2001,Computational Methods for Fluid Dynamics,Springer.
  12. Kim, K. W., Baek, S. W., Kim, M. Y., andRyou. H. S., 2003, “A Study on a HybridGenetic Algorithm for the Analysis of InverseRadiation," Trans. of the KSME(B), Vol, 10,pp.1516-1523. https://doi.org/10.3795/KSME-B.2003.27.10.1516
  13. Lee, K. H., Baek, S. W., Kim, K. W., and Kim,M. Y., 2007, "A Study on Inverse RadiationAnalysis using RPSO Algorithm," Trans. of theKSME(B), Vol. 31, No. 7, pp.635-643. https://doi.org/10.3795/KSME-B.2007.31.7.635
  14. Lee, K. H., Baek, S. W., and Kim, K. W., 2008,“Inverse Radiation Analysis Using Repulsive ParticleSwarm Optimization Algorithm," Int. J. Heat MassTran., Vol. 51, pp. 2772-2783. https://doi.org/10.1016/j.ijheatmasstransfer.2007.09.037
  15. Kennedy, J. and Eberhart, R., 1995, "Particle Swarm Optimization," Proc. of the IEEE Int. Conf. Neural Networks, Perth, Australia, pp. 1942-1945.