항공우주기술 (Aerospace Engineering and Technology)

한국항공우주연구원 (Korea Aerospace Research Institute)
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아음속 항공기 날개 최적 설계 기술 개발

Development of Technology for Optimized Wing Design of Subsonic Aircraft

  • 투고 : 2010.12.21
  • 심사 : 2011.07.01
  • 발행 : 2011.07.01
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초록

100인승 이하의 쌍발 터보프롭 항공기의 날개 형상에 대한 최적 설계를 수행하였다. 최적설계는 2단계로 이뤄져 있는데 먼저 꼬리날개의 높이에 대한 방향안정성을 분석하였고 방향 안정성을 갖는 높이에 대해 순항조건에 대해 항력을 최소로 하는 날개의 최적형상을 결정하였다. 방향안정성 분석은 Vorstab를 통해 이뤄졌고, 최적형상은 Piano를 활용하여 결정하였으며 공력해석은 점성을 고려한 Fluent 코드를 활용하였다. 최적설계 결과 약 10 count의 항력을 감소하였다.

Optimized design was performed for a subsonic aircraft wing. The subsonic aircraft is dual turbo-prop and carrying less than 100 passengers. The cruise speed is Mach 0.6. The design was performed by two stages. The first stage is to decide the height of horizontal tail by analyzing the directional stability with Vorstab and then, the optimized wing configuration was selected with Piano, a optimizer commercially available. Fluent, a commercial CFD software was utilized to predict the aerodynamic performance of the aircraft. Drag of the aircraft was minimized with maintaining constant lift for cruise. The optimization reduced 10 counts from the initial wing configuration.

키워드

참고문헌

  1. PIAnO (Process Integration, Automation and Optimization) User's Manual. Version 2.4, 2008, FRAMAX Inc.
  2. Y.B.Lee, K.J.Hong and D.H.Choi, "An Efficient Robust Optimal Design Method for Engineering Systems with Numerical Noise", 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
  3. K.J.Hong, M.S.Kim and D.H.Choi, "Efficient Approximation Method for Constructing Quadratic Response Surface Model", 2001, KSME international Journal 15(7). 876-888
  4. N.A. Butler, "Optimal and Orthogonal Latin Hypercube Designs for Computer Experiments", 2001, Biometrika, 88(3). 847-857 https://doi.org/10.1093/biomet/88.3.847
  5. L. Wang, "Valuable Theoretical Lessons Learned From the Application of Metamodels to a Variety of Industrial Problems", IDETC/CIE2009. 789-804
  6. R. H. Myers, and D. C. Montgomery, Response Surface Methodology-Process and Product Optimization Using Designed Experiments, 1995, John Wiley & Sons. New York. USA.
  7. M. Orr, Matlab functions for Radial Basis Functions Networks. Institute for Adaptive and Neural Computation. Division of Informatics, 1999, Scotland. UK, Edinburgh University. Edinburgh EH8 9LW
  8. J. Sacks, S.B. Schiller, and W.J. Welch , Designs for Computer Experiments, 1989, Technometrics.31(1).41-47 https://doi.org/10.1080/00401706.1989.10488474
  9. M. Powell, Radial Basis Functions for Multivariable Interpolation : A review. 1987, Oxford University Press.143-167
  10. T. Back, Evolutionary algorithms in theory and practice-Evolution strategies, Evolutionary programming, Genetic algorithms. 1996, Oxford University Press. New York, USA.