Transactions of the Korean Society of Mechanical Engineers B (대한기계학회논문집B)

The Korean Society of Mechanical Engineers (대한기계학회)
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Inverse Estimation of Surface Radiation Properties Using Repulsive Particle Swarm Optimization Algorithm

반발 입자 군집 최적화 알고리즘을 이용한 표면복사 물성치의 역추정에 관한 연구

  • Lee, Kyun Ho (Dept. of Aerospace Engineering, School of Mechanical and Aerospace Engineering, Sejong Univ.) ;
  • Kim, Ki Wan (Agency for Defense Development)
  • 이균호 (세종대학교 기계항공우주공학부 항공우주공학전공) ;
  • 김기완 (국방과학연구소)
  • Received : 2014.04.17
  • Accepted : 2014.07.14
  • Published : 2014.09.01
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Abstract

The heat transfer mechanism for radiation is directly related to the emission of photons and electromagnetic waves. Depending on the participation of the medium, the radiation can be classified into two forms: surface and gas radiation. In the present study, unknown radiation properties were estimated using an inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure. For efficiency, a repulsive particle swarm optimization (RPSO) algorithm, which is a relatively recent heuristic search method, was used as inverse solver. By comparing the convergence rates and accuracies with the results of a genetic algorithm (GA), the performances of the proposed RPSO algorithm as an inverse solver was verified when applied to the inverse analysis of the surface radiation problem.

광자(Photon)이나 전자기파(Electromagnetic Wave) 등의 형태로 직접 열을 전달하는 특징을 가지고 있는 복사열전달은 중간 매질의 열전달 관여여부에 따라 표면복사(Surface Radiation)와 기체복사(Gas Radiation)의 형태로 구분될 수 있다. 본 연구에서는 원통 형상에서의 표면복사에 대해 미지의 복사물성치들을 역해석 방법을 이용해 역추정하였다. 이때, 효율적인 역해석을 위해 반발 입자 군집 최적화(Repulsive Particle Swarm Optimization, RPSO) 알고리즘을 역해석 기법으로 채택하였다. 이로부터 얻은 해의 수렴성과 정확도 등을 기존의 유전알고리즘(GA) 결과와 비교해 봄으로써, 표면복사 현상에 대한 역해석의 적용 가능성을 고찰하고자 하였다.

Keywords

References

  1. Modest, M. F., 2003, Radiative Heat Transfer, Academic Press, Massachusetts.
  2. Ozisik, M. N. and Orlande, H. R. B., 2000, Inverse Heat Transfer, Taylor & Francis, New York.
  3. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer, New York, 1999.
  4. Kennedy, J. and Eberhart, R., 1995, "Particle Swarm Optimization," Proc. of the IEEE Int. Conf. Neural Networks, Perth, Australia, pp. 1942-1945.
  5. Laarhoven, P. J. M. and Aarts, E. H. L., Simulated Annealing: Theory and Applications, Springer, New York, 1987.
  6. Kim, K. W., Baek, S. W. and Ryou, H. S., 2004, "A Study on the Inverse Analysis of Surface Radiation in a Cylindrical Enclosure," Trans. Korean Soc. Mech. Eng. B, Vol. 28, No. 6, pp. 705-712. https://doi.org/10.3795/KSME-B.2004.28.6.705
  7. Chang, H. C., Park, W. H., Lee, D. H., Son B. S. and Kim, T. K., 2010, "Estimation of Fire Dynamics Properties for Charring Material Using a Genetic Algorithm," J. of Korean Institute of Fire Sci. & Eng., Vol. 24, No. 2, pp. 106-113.
  8. Becceneri, J. C., Stephany, S., de Campos Velho, H. F. and Silva Neto, A. J., 2006, "Solution of the Inverse Problem of Radiative Properties Estimation with Particle Swarm Optimization Techniques," IPES, Iowa, USA.
  9. Lee, K. H., Baek, S. W., Kim, K. W. and Kim, M. Y., 2007, " A Study on Inverse Radiation Analysis Using RPSO Algorithm," Trans. Korean Soc. Mech. Eng. B, Vol. 31, No. 7, pp. 635-643. https://doi.org/10.3795/KSME-B.2007.31.7.635