Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Study on the Optimal Selection of Rotor Track and Balance Parameters using Non-linear Response Models and Genetic Algorithm

로터 트랙 발란스(RTB) 파라미터 최적화를 위한 비선형 모델링 및 GA 기법 적용 연구

  • Received : 2016.09.19
  • Accepted : 2016.10.13
  • Published : 2016.11.01
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Abstract

This paper intends to develop the rotor track and balance (RTB) algorithm using the nonlinear RTB models and a real-coded hybrid genetic algorithm. The RTB response data computed using the trim solutions with variation of the adjustment parameters have been used to build nonlinear RTB models based on the quadratic interpolation functions. Nonlinear programming problems to minimize the track deviations and the airframe vibration responses have been formulated to find optimum settings of balance weights, trim-tab deflections, and pitch-link lengths of each blade. The results are efficiently resolved using the real-coded genetic algorithm hybridized with the particle swarm optimization techniques for convergence acceleration. The nonlinear RTB models and the optimized RTB parameters have been compared with those computed using the linear models to validate the proposed techniques. The results showed that the nonlinear models lead to more accurate models and reduced RTB responses than the linear counterpart.

본 연구는 비선형 응답모델과 실수기반의 혼합형 유전자 알고리즘을 적용하여 로터의 트?-발란스(RTB) 기법을 개발하는 데 목적이 있다. 트?-발란스 조절 파라미터의 변화에 따른 트림해석 결과를 이용하여 2차의 근사함수를 이용하는 비선형 응답모델을 개발하였다. 트?편차와 기체의 진동응답을 최소화하기 위해 균형추 무게, 트림 탭(Trim Tab) 및 피치링크 길이를 최적화하기 위한 비선형계획 문제를 정식화하였다. 정식화 결과는 수렴성 향상을 위해 군집최적화 기법을 실변수기반의 유전자 알고리즘에 통합한 혼합형 유전자 기법을 사용함으로써 효율적인 해석이 가능하였다. 비선형 모델을 이용한 본 연구의 방법을 선형모델의 결과와 비교하여 본 연구의 방법을 검증하였으며 비선형모델을 사용하는 경우 선형모델의 결과보다 향상된 응답특성을 계산할 수 있음을 밝혔다.

Keywords

References

  1. Rosen, A. and Ben-Ari, R., "Mathematical Modelling of a Helicopter Rotor Track and Balance: Theory", Journal of Sound and Vibration, Vol. 200, No.5, 1997, pp. 589-603 https://doi.org/10.1006/jsvi.1996.0669
  2. Rosen, A. and Ben-Ari, R., "Mathematical Modelling of a Helicopter Rotor Track and Balance: Result", Journal of Sound and Vibration, Vol. 200, No.5, 1997, pp. 605-620 https://doi.org/10.1006/jsvi.1996.0670
  3. Miller, N. A. and Kunz, D. L., "A comparison of main rotor smoothing adjustments using linear and neural network algorithms", Journal of Sound and Vibration, Vol. 311, 2008, pp. 991-1003 https://doi.org/10.1016/j.jsv.2007.09.041
  4. Kwon, H. J., Yu, Y. H., Jung, S. N., and Yun, Chul. Y.," Development of Dynamic Balancing Techniques of a Rotor System Using Genetic Algorithm", Journal of the Korean Society for aeronautical & space sciences, Vol. 38, No. 12, 2010, pp. 1162-1169 https://doi.org/10.5139/JKSAS.2010.38.12.1162
  5. Liu, H., Cai, Y., Lu, C., and Luan, J., "Helicopter Rotor Balance Adjustment Using GRNN Neural Network and Genetic Algorithm", Intelligent Systems, 2009. GCIS'09. WRI Global Congress on. Vol. 4. IEEE, 2009.
  6. Yu, Y. H., Kim, C. J., Jung, S. N., and Kim, O. C.,"Rotor Track and Balance of a Helicopter Rotor System Using Modern Global Optimization Schemes" Journal of the Korean Society for aeronautical & space sciences, Vol. 41, No. 7, 2013, pp. 524-531 https://doi.org/10.5139/JKSAS.2013.41.7.524
  7. Bechhoefer, E., and Power, D., "IMD HUMS Rotor Track and Balance Techniques", Aerospace Conference, 2003. IEEE, Vol. 7 pp. 3205-3211
  8. Kim, C. J., Shin, K. C., Cho, I. J., Kim, C. S., and Ahn, S. J.," Development and Application of High-Fidelity Helicopter Flight Dynamic Analysis Program", Proceeding of the 2011 KSAS Fall Conference, pp. 660-666
  9. Lee, S. H., Kim, C. J., Jung, S. N., Yu, Y. H., and Kim, O. C., "Development and Validation of Helicopter Non-linear RTB(Rotor Track and Balance) Model", Proceeding of the KSAS 2015 Fall Conference, 2015
  10. You, Y. H., Lee, S. H., Jung, S. N., Kim, C. J., and Kim, O. C.,"Optimization of RTB Parameters Using a Nonlinear Helicopter Model," Proceeding of the 2015 KSAS Fall Conference, 2015.
  11. Dhadwal, M. K., Jung, S. N., Kim, C. J., "Advanced particle swarm assisted genetic algorithm for constrained optimization problems", Computational Optimization and Applicaions, vol. 58, No. 3, 2014, pp. 781-806 https://doi.org/10.1007/s10589-014-9637-0