DOI QR코드

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Reynolds-averaged Navier-Stokes 해석과 기포동역학 모델을 이용한 날개 끝 와류 공동 소음의 수치적 고찰

Numerical investigation of blade tip vortex cavitation noise using Reynolds-averaged Navier-Stokes simulation and bubble dynamics model

  • 구가람 (부산대학교 기계공학부) ;
  • 정철웅 (부산대학교 기계공학부) ;
  • 설한신 (한국해양과학기술원 선박해양플랜트연구소)
  • 투고 : 2020.01.31
  • 심사 : 2020.03.11
  • 발행 : 2020.03.31

초록

본 연구에서는 날개 끝 와류 공동(Blade-Tip Vortex Cavitation, BTVC)과 이에 기인한 유동 소음을 예측하기 위하여 Eulerian/Lagrangian 연성 해석기법을 제안하였다. 제안한 방법은 크게 연속적인 4단계로 구성되며, 각각 전산유체역학을 이용한 유동장 모사, 와류모델을 이용한 날개 끝 와류의 재구성, 기포 동역학 모델을 이용한 BTVC의 생성, 그리고 음향상사법을 이용한 음향파 예측이다. 일반적으로 전산유체역학 자체가 지니는 고유한 수치감쇠와 과도한 난류 강도로 인해 와류 강도를 심각하게 작게 예측하므로, 유동방향의 날개 끝 와류는 와류모델을 사용하여 재생하였다. 다음으로 Reyleigh-Plesset 방정식에 기반한 기포 동역학 모델을 사용하여 BTVC의 발생과 변화를 모사하였다. 마지막으로 BTVC에 의한 유동소음을 각각의 구형 버블을 그 부피 시간변화율의 변화율에 크기가 비례하는 홀극원으로 모델링하여 예측하였다. 제안한 수치 방법의 유효성을 예측값과 측정값을 비교하여 검토하였다.

In this study, the Eulerian/Lagrangian one-way coupling method is proposed to predict flow noise due to Blade-Tip Vortex Cavitation (BTVC). The proposed method consists of four sequential steps: flow field simulation using Computational Fluid Dynamics (CFD) techniques, reconstruction of wing-tip vortex using vortex model, generation of BTVC using bubble dynamics model and acoustic wave prediction using the acoustic analogy. Because the CFD prediction of tip vortex structure generally suffers from severe under-prediction of its strength along the steamwise direction due to the intrinsic numerical damping of CFD schemes and excessive turbulence intensity, the wing-tip vortex along the freestream direction is regenerated by using the vortex modeling. Then, the bubble dynamics model based on the Rayleigh-Plesset equation was employed to simulate the generation and variation of BTVC. Finally, the flow noise due to BTVC is predicted by modeling each of spherical bubbles as a monople source whose strength is proportional to the rate of time-variation of bubble volume. The validity of the proposed numerical methods is confirmed by comparing the predicted results with the measured data.

키워드

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