DDPG方法在抖振约束下变弯度翼型/机翼设计的应用研究

周思历; 孙刚; 王聪

doi:10.21656/1000-0887.440204

DDPG方法在抖振约束下变弯度翼型/机翼设计的应用研究

doi: 10.21656/1000-0887.440204

复旦大学航空航天系，上海 200433

(我刊编委孙刚来稿)

详细信息

作者简介:
周思历(1999—)，男，硕士生(E-mail: zhousl21@m.fudan.edu.cn)

通讯作者:
孙刚(1964—)，男，教授，博士，博士生导师(通讯作者. E-mail: gang_sun@fudan.edu.cn)

中图分类号: TP18;V221.3
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- 文章访问数: 352
- HTML全文浏览量: 91
- PDF下载量: 59
- 被引次数: 0
出版历程
- 收稿日期: 2023-07-04
- 修回日期: 2023-09-27
- 刊出日期: 2024-01-01

Application Study on the DDPG Method for Designing Variable Camber Airfoils/Wings Under Buffeting Constraints

Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, P.R.China

(Contributed by SUN Gang, M. AMM Editorial Board)

摘要

摘要: 变弯度技术可以提升巡航多升力系数工况下的升阻性能，对于提高整段巡航的经济效益具有重要意义. 构造了光滑连续的流动分离函数约束翼型抖振性能，结合变弯度技术与人工神经网络代理模型搭建了某机翼截面翼型的巡航多升力系数工况优化模型. 应用深度确定性策略梯度(DDPG)方法优化此模型，实现了抖振约束下6.8%的巡航平均升阻比提升，优于粒子群和改进灰狼算法对此模型的优化结果. 以优化前后翼型分别生成锥形后掠翼，验证了二维翼型变弯度优化对三维机翼的贡献.
- 气动外形优化 /
- DDPG /
- 变弯度 /
- 抖振
Abstract: The application of the variable camber technology has promising results in improving the lift-to-drag performance during the cruise phase, particularly under multi-lift conditions. This improvement is crucial for enhancing the economic benefits of the entire flight. A smooth and continuous flow separation function was developed to constrain the buffeting performance. An optimization model for cruise performances under multi-lift conditions of wing cross sections was constructed through combination of this function with the variable camber technology and an artificial neural network surrogate model. The deep deterministic policy gradient (DDPG) method was used to optimize this model, resulting in a cruise average lift-to-drag ratio improvement of 6.8% under buffeting constraints. This improvement surpasses the results obtained by other optimization algorithms, such as the particle swarm optimization (PSO) and the improved gray wolf optimization (GWO). The results of the generation and analysis of 2 conical swept wings with the unoptimized and optimized airfoils, show the contribution of the 2D variable camber airfoil optimization to 3D wings.
- aerodynamic optimization /
- DDPG /
- variable camber /
- buffet
edited-by

edited-by
注释:

1) (我刊编委孙刚来稿)

HTML全文

图 1 变弯度翼型优化设计框架

注为了解释图中的颜色，读者可以参考本文的电子网页版本，后同.

Figure 1. The optimization design framework for variable curvature airfoils

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图 2 超临界翼型不同后缘偏转角度的升阻比曲线

Figure 2. Lift-drag ratio curves of supercritical airfoils under different trailing edge deflection angles

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图 3 Δα=0.1方法在不同Mach数下的抖振边界判定

Figure 3. Buffet onset determination of the Δα=0.1 method for different Mach numbers

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图 4 分离函数方法在不同Mach数下的抖振边界判定

Figure 4. Buffet onset determination of the separation function method for different Mach numbers

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图 5 Δα=0.1方法和分离函数方法的对比

Figure 5. Comparison between the Δα=0.1method and the separation function method

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图 6 DDPG算法框架

Figure 6. The framework of DDPG

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图 7 机翼表面压力云图

Figure 7. Wing surface pressure contours

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图 8 不同网格精度下机翼截面压力分布

Figure 8. Wing section pressure distributions with different grid finesses

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图 9 RAE2822翼型网格收敛性验证

Figure 9. Mesh convergence study for the RAE2822 airfoil

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图 10 2.5D、2.75D和3D计算的压力分布对比

Figure 10. Comparison of pressure distributions for 2.5D, 2.75D and 3D calculations

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图 11 优化模型

Figure 11. The optimization model

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图 12 训练集和验证集的损失函数收敛曲线

Figure 12. Loss function convergence curves for the training set and the verification set

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图 13 整体数据集预测的C_l, C_d, S_sep的线性回归图

Figure 13. Linear regression plots of C_l, C_d and S_sep predicted by the overall data set

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图 14 初始翼型不同弯度下的抖振边界

Figure 14. Buffet onsets of initial airfoil shapes with different cambers

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图 15 DDPG收敛曲线

Figure 15. Convergence curves of DDPG

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图 16 群智能算法流程

Figure 16. The flow chart of the swarm intelligence algorithm

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图 17 群智能算法收敛曲线

Figure 17. Convergence curves of the swarm intelligence algorithm

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图 18 不同方法优化的升阻比最大曲线

Figure 18. Lift to drag ratio maximum curves optimized with different methods

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图 19 不同方法优化的几何外形

Figure 19. Geometric profiles optimized with different methods

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图 20 锥形后掠翼

Figure 20. The conical swept wing

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图 21 机翼升阻比曲线

Figure 21. Lift-drag ratio curves of the wing

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表 1 CRM机翼网格收敛性验证

Table 1. Mesh convergence study for the CRM wing

	mesh size	α/(°)	Ma	C_l	C_d
coarse	1 089 048	2.47	0.85	0.500	0.022 1
medium	5 083 584	2.43	0.85	0.500	0.021 5
fine	21 028 625	2.42	0.85	0.500	0.021 4

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表 2 2.5D方法工况转换结果

Table 2. Case conversion results of the 2.5D method

	Ma	C_l	Re
3D	0.85	0.5	5×10⁶
2D	0.737	0.798	5×10⁶

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表 3 优化结果比较

Table 3. Comparison of optimization results

	base 0	base	PSO	GWO	DDPG-5	DDPG-1
C_l/C_d	-	62.37	64.67	65.00	65.12	65.04
t/s	-	-	861.48	824.86	235.24	48.33
C_l/C_d(CFD)	61.15	62.80	64.52	65.28	65.18	65.29
buffet onset	1.075	1.075	1.103	1.085	1.121	1.076

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