下表面射流的超临界翼型气动性能分析

王若尘; 张国鑫; 王翔宇; 马晓平

doi:10.13700/j.bh.1001-5965.2021.0489

下表面射流的超临界翼型气动性能分析

doi: 10.13700/j.bh.1001-5965.2021.0489

王若尘^{1, 2},
张国鑫^1, ,,
王翔宇¹,
马晓平¹

1.
中国科学院工程热物理研究所，北京 100190
2.
中国科学院大学，北京 100049

基金项目: 国家自然科学基金(61901448)；中国科学院青年创新促进会(2020149)

详细信息

通讯作者:
E-mail：zhangguoxin@iet.cn

中图分类号: V211.3
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出版历程
- 收稿日期: 2021-08-25
- 录用日期: 2021-09-10
- 网络出版日期: 2021-09-24
- 整期出版日期: 2023-07-31

Aerodynamic performance analysis of supercritical airfoil with lower surface jet

1.
Institute of Engineering Thermophysics，Chinese Academy of Sciences，Beijing 100190，China
2.
University of Chinese Academy of Sciences，Beijing 100049，China

Funds: National Natural Science Foundation of China (61901448); Youth Innovation Promotion Association of Chinese Academy of Sciences (2020149)

More Information

Corresponding author: E-mail：zhangguoxin@iet.cn

摘要

摘要:
为探究下表面射流关键参数对超临界翼型气动性能的影响，采用雷诺平均Navier-Stokes （RANS）方程与Spalart-Allmaras （S-A）湍流模型进行数值模拟。通过比较基准RAE2822翼型与下表面射流翼型的流场，验证下表面射流能够在翼型后缘诱导产生逆时针分离涡，带动流线向下偏折，增加了翼型的等效弯度，同时加大前缘的吸力峰，从而提高翼型的气动性能。进一步探究射流位置、射流动量系数、射流角度、马赫数等关键参数对RAE2822翼型气动性能的影响规律。结果表明：给定状态下，下表面射流的位置越靠后，动量系数越大，翼型的气动性能越优。下表面射流在α=0°和2°时的最优射流角度为110°，在α=4°时的最优射流角度为160°，且在最优射流角度下能有效提高翼型马赫数在0.3~0.6范围内的气动性能。
- 主动流动控制 /
- 下表面射流 /
- 超临界翼型 /
- 气动性能 /
- 雷诺平均Navier-Stokes
Abstract:
To investigate the influence of key parameters of the lower surface jet on aerodynamic performance of supercritical airfoil, a numerical simulation is performed using Reynolds average Navier-Stokes (RANS) equation and Spalart-Allmaras (S-A) turbulence model. By comparing the flow field between the benchmark RAE2822 airfoil and the lower surface jet airfoil, the lower surface jet induces a counterclockwise separation vortex at the trailing edge of the airfoil, deflecting the streamlines downward. This deflection increases the equivalent camber of the airfoil and the suction peak of the leading edge, leading to the aerodynamic performance enhancement of the airfoil. The effects of key parameters such as the jet position, the jet momentum coefficient, the jet angle and the Mach number on the aerodynamic performance of RAE2822 airfoil are explored. The results show that under given conditions, the closer the lower surface jet is to the trailing edge and the greater the momentum coefficient is, the better the aerodynamic performance of the airfoil. The optimal angle of the lower surface jet is 110° at α=0° and 2°, and 160° at α=4°. The aerodynamic performance of the airfoil can be effectively improved at the optimal angles with the Mach number varied from 0.3 to 0.6.
- active flow control /
- lower surface jet /
- supercritical airfoil /
- aerodynamic performance /
- Reynolds average Navier-Stokes

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图 1 RAE2822翼型下表面射流参数示意图

Figure 1. Diagram of jet parameters of RAE2822 airfoil lower surface

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图 2 RAE2822翼型计算网格

Figure 2. Computational grids of RAE2822 airfoil

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图 3 RAE2822压力系数分布对比

Figure 3. Comparison of pressure coefficient distribution of RAE2822

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图 4 CC020-010EJ翼型计算网格

Figure 4. Computational grids of CC020-010EJ airfoil

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图 5 CC020-010EJ压力系数分布对比

Figure 5. Comparison of pressure coefficient distribution of CC020-010EJ

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图 6 RAE2822下表面射流翼型计算网格

Figure 6. Computational grids of jet airfoil of RAE2822 lower surface

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图 7 马赫云图与流线图对比

Figure 7. Comparison of Mach contours and streamlines

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图 8 有/无射流压力系数分布对比

Figure 8. Comparison of pressure coefficient distribution with/without jet

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图 9 不同射流位置气动力系数对比

Figure 9. Comparison of aerodynamic coefficients at different jet positions

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图 10 不同射流位置马赫云图与流线图对比

Figure 10. Comparison of Mach contours and streamlines at different jet positions

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图 11 不同射流动量系数下气动力系数对比图

Figure 11. Comparison of aerodynamic coefficients at different jet momentum coefficients

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图 12 不同射流角度下各个气动力系数对比

Figure 12. Comparison of aerodynamic coefficients at different jet angles

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图 13 不同马赫数下各个气动力系数对比

Figure 13. Comparison of aerodynamic coefficients at different Mach numbers

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表 1 RAE2822翼型网格参数与气动力系数的对比

Table 1. Comparison of grid parameters and aerodynamic coefficients of RAE2822 airfoil

网格	网格数量	C_L	C_D
实验值^[21]		0.803	0.0168
粗网格	23324	0.76031	0.016989
中等网格	40964	0.76652	0.017031
细网格	69188	0.76928	0.017094
注：3套网格与实验值相比，C_L误差5.3%，4.5%，4.2%，C_D误差为1.1%，1.3%，1.8%。

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表 2 CC020-010EJ翼型气动力系数的对比

Table 2. Comparison of aerodynamic coefficients of CC020-010EJ airfoil

结果	C_L	C_D
计算值	1.543	0.0348
文献[22]数值结果	1.533	0.0367
实验值^[22]	1.363	0.0312
注：本文，文献[22]计算值与实验值相比，C_L误差为13.2%，12.4%，C_D误差为11.5%，17.6%。

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表 3 下表面射流计算参数

Table 3. Computational parameters of lower surface jet

Ma	Re	α/(°)	c_μ	x_j	θ/(°)
0.6	5.35×10⁶	4	0.005	0.98c	90

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表 4 不同射流位置下的计算参数

Table 4. Computational parameters of different jet positions

Ma	Re	α/(°)	c_μ	x_j	θ/(°)
0.6	5.35×10⁶	−4~16	0.005	0.8c，0.9c，0.98c	90

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表 5 不同射流动量系数下的计算参数

Table 5. Computational parameters of different jet momentum coefficients

Ma	Re	α/(°)	c_μ	x_j	θ/(°)
0.6	5.35×10⁶	−4~16	0.0005，0.001，0.005	0.98c	90

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表 6 4°迎角下不同射流动量系数翼型的气动力系数

Table 6. Aerodynamic coefficients of airfoil with different jet momentum coefficients at angle of attack 4°

c_μ	C_L	C_D	C_L /C_D
0	0.77982	0.013095	59.55
0.0005	0.93005	0.014121	65.86
0.001	0.98081	0.014366	68.27
0.005	1.1701	0.015337	76.29

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表 7 不同射流角度计算参数

Table 7. Computational parameters of different jet angles

Ma	Re	α/（°）	c_μ	x_j	θ/（°）
0.6	5.35×10⁶	0，2，4	0.005	0.98c	10~170

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表 8 不同马赫数下的计算参数

Table 8. Computational parameters of different Mach numbers

Ma	Re	α/（°）	c_μ	x_j	θ/（°）
0.3~0.6	2.68×10⁶~5.35×10⁶	0、2、4	0.005	0.98c	110，160

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