﻿ RANS方法预报对转桨非定常水动力性能研究
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 应用科技  2019, Vol. 46 Issue (3): 7-12  DOI: 10.11991/yykj.201808001 0

### 引用本文

YAO Guoying, KE Yongsheng. Using RANS method to predict the unsteady hydrodynamic performance of contra-rotating propellers[J]. Applied Science and Technology, 2019, 46(3), 7-12. DOI: 10.11991/yykj.201808001.

### 文章历史

RANS方法预报对转桨非定常水动力性能研究

Using RANS method to predict the unsteady hydrodynamic performance of contra-rotating propellers
YAO Guoying , KE Yongsheng
Naval Research Academy, Beijing 100161, China
Abstract: The unsteady exciting force of contra-rotating propellers (CRPs) can generate line-spectrum noise and vibration. Predicting unsteady hydrodynamic performance of CRPs precisely is important for the design of CRPs. A numerical method to predict CRPs unsteady hydrodynamic performance is developed based on the solution of RANS equations with the SST kω turbulence model, using the sliding mesh model to simulate the unsteady interaction between the forward and aft propeller. To improve the prediction accuracy, meshes around the interface of the forward and aft propeller are generated with a refined strategy, combining the sliding mesh’s characteristics, besides, the calculation is set up reasonably by considering the correspondence of rotating angle in every calculating time-step. Numerical predictions were carried out for two sets of CRPs, and results were compared with model test data. Comparison and analysis indicate that the prediction method developed in this article yields more accurate results and this method is effective.
Keywords: contra-rotating propellers    unsteady    exciting force    hydrodynamic performance    prediction    mesh    computational fluid dynamics    RANS method

1 数值预报模型 1.1 控制方程

 $\frac{{\partial \rho }}{{\partial t}} + \frac{\partial }{{\partial {x_i}}}(\rho {u_i}) = 0\\$
 $\frac{\partial }{{\partial t}}(\rho {u_i}) + \frac{\partial }{{\partial {x_j}}}(\rho {u_i}{u_j}) = - \frac{{\partial p}}{{\partial {x_i}}} \!+\! \frac{{\partial {\tau _{ij}}}}{{\partial {x_j}}} \!+ \!{F_i} \!+\! \frac{\partial }{{\partial {x_j}}}( - \rho \overline {{u_i}^\prime {u_j}^\prime } )$

1.2 模型离散化与边界条件

2 结果与讨论

2.1 方案A

 $f = {f_n} \times \left( {{m_F}{Z_F} + {m_A}{Z_A}} \right)$

 ${{m_F}{Z_F} = {m_A}{Z_A}}$

2.2 方案B