面向声爆/气动力的飞行器布局设计知识挖掘

马创; 舒博文; 黄江涛; 刘刚; 钟世东

doi:10.13700/j.bh.1001-5965.2022.0310

面向声爆/气动力的飞行器布局设计知识挖掘

doi: 10.13700/j.bh.1001-5965.2022.0310

马创¹,
舒博文^{1, 2},
黄江涛^1, ,,
刘刚¹,
钟世东¹

1.
中国空气动力研究与发展中心，绵阳 621000
2.
西北工业大学航空学院，西安 710072

基金项目: 国家重大科技专项(MJZ5-(N2))；基础与前沿技术基金重点项目(PJD20190095)

详细信息

通讯作者:
E-mail：f_yforever@126.com

中图分类号: V221⁺.3；TB553
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出版历程
- 收稿日期: 2022-05-01
- 录用日期: 2022-07-08
- 网络出版日期: 2022-09-06
- 整期出版日期: 2024-03-27

Knowledge mining of aircraft configuration design for sonic boom/aerodynamics

1.
China Aerodynamics Research and Development Center，Mianyang 621000，China
2.
School of Astronautics，Northwestern Polytechnical University，Xi’an 710072，China

Funds: National Major Science and Technology Projects (MJZ5-(N2)); Key Projects of Basic and Frontier Technology Fund (PJD20190095)

More Information

Corresponding author: E-mail：f_yforever@126.com

摘要

摘要:
声爆抑制是发展新一代超声速民机必须突破的关键技术。飞行器总体布局参数对其声爆特性有重要影响。数据挖掘（DM）可以从大量的数据中通过算法搜索隐藏的信息，是飞行器设计知识提取的有力工具。选取后掠角、展弦比、梢根比、上反角、机身长细比5个总体布局参数作为设计变量，目标函数定义为远场感知噪声级，同时计算升、阻力系数作为气动力衡量指标。基于总变差分析(ANOVA)、决策树算法、自组织映射（SOM）网络组成的数据挖掘体系提取低声爆设计知识库。获得所选设计变量与声爆/气动力的相关度，并实现对设计变量的分层与降维。后掠角有最高设计优先级，长细比、上反角重要程度次之，展弦比与梢根比为低敏感变量。对于算例中的飞行器，在合理区间内选择较大的后掠角、上反角能够使声爆最小化的同时，设计适于超声速巡航的小展弦比布局。
- 超声速民机 /
- 声爆抑制 /
- 布局设计 /
- 数据挖掘 /
- 知识库
Abstract:
Sonic boom suppression is a key technology in the development of a new generation of supersonic civil aircraft. The configuration parameters of aircraft have an important influence on its sonic boom characteristics. Data mining (DM) can search hidden information from a large amount of data through algorithms, thus becoming a powerful tool to extract aircraft design knowledge. Five configuration parameters, including the sweep angle, aspect ratio, taper ratio, dihedral angle and fuselage slenderness ratio, are selected as design variables. The objective function is defined as the perceived noise level, and the lift and drag coefficients are calculated as aerodynamic measurement indexes. The knowledge base of low sonic boom design is extracted based on the data mining system composed of analysis of variance (ANOVA), decision tree and self-organizing feature map (SOM). The method obtains the correlation between the selected design variables and sonic boom/aerodynamic force, realizing the stratification and dimensionality reduction of design variables. The sweep angle has the first design priority, the slenderness ratio and the dihedral angle the second, and the aspect ratio and taper ratio are low sensitive variables. For the aircraft of the example in this paper, choosing a larger sweep angle and dihedral angle in a reasonable range can minimize the sonic boom and design a small aspect ratio configuration suitable for supersonic cruises.
- supersonic civil aircraft /
- sonic boom suppression /
- configuration design /
- data mining /
- knowledge base

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图 1 本文方法流程

Figure 1. Flow chart of proposed method

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图 2 SOM网络结构

Figure 2. Structure of SOM network

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图 3 典型决策树结构

Figure 3. Structure of typical decision tree

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图 4 某型超声速飞行器外形

Figure 4. Shape of a certain supersonic aircraft

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图 5 近场计算网格

Figure 5. Calculation grid of near field

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图 6 SOM训练结果

Figure 6. Training results of SOM

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图 7 远场声爆信号波形参数

Figure 7. Waveform parameters of far field sonic boom signal

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图 8 设计变量Sobol指标分析

Figure 8. Sobol index analysis of design variables

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图 9 决策树生成结果

Figure 9. Result of decision tree

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图 10 优化结果

Figure 10. Results of optimization

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表 1 设计变量取值区间

Table 1. Value ranges of design variables

后掠角X₁/(°)	展弦比X₂	梢根比X₃	上反角X₄/(°)	机身长细比X₅
60～70	0.7 ～ 1.2	0.02 ～ 0.2	−3～8	12 ～ 17

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表 2 决策树样本标签

Table 2. Labels of decision tree samples

标签	X₁/(°)	X₂	X₃	X₄/(°)	X₅	目标函数
1	[60,62)	[0.7,0.8)	[0.02,0.06)	[−3.0,−0.8)	[12,13)	(−$ \mathrm{\infty } $,99.67]
2	[62,64)	[0.8,0.9)	[0.06,0.09)	[−0.8,1.4)	[13,14)	(99.67,+$ \mathrm{\infty } $)
3	[64,66)	[0.9,1.0)	[0.09,0.13)	[1.4,3.6)	[14,15)
4	[66,68)	[1.0,1.1)	[0.13,0.16)	[3.6,5.8)	[15,16)
5	[68,70)	[1.1,1.2)	[0.16,0.20)	[5.8,8.0)	[16,17)

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表 3 决策树设计知识提取

Table 3. Design knowledge extracted from decision tree

序号	样本数	设计知识
1	107	X₁≥66°， X₅≥13。
2	13	X₁≥66°，X₃≥0.09，X₅<13。
3	21	64°≤X₁<66°，X₄≥3.6°，X₅≥13。
4	13	64°≤X₁<66°，X₂≥1.0， X₃<0.16。 −0.8°≤X₄<3.6°， X₅≥13。

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表 4 设计变量分层信息

Table 4. Hierarchical information of design variables

分层情况	变量	原区间	新空间
第1层	X₁/(°)	60～70	64～70
第2层	X₄/(°)	−3～8	3.6～8
第2层	X₅	12 ～ 17	13 ～ 17
第3层	X₂	0.7 ～ 1.2	0.7 ～ 1.2
第3层	X₃	0.02 ～ 0.20	0.02 ～ 0.20

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