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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

马创,舒博文,黄江涛,等. 面向声爆/气动力的飞行器布局设计知识挖掘[J]. 北京航空航天大学学报,2024,50(3):975-984 doi: 10.13700/j.bh.1001-5965.2022.0310
引用本文: 马创,舒博文,黄江涛,等. 面向声爆/气动力的飞行器布局设计知识挖掘[J]. 北京航空航天大学学报,2024,50(3):975-984 doi: 10.13700/j.bh.1001-5965.2022.0310
MA C,SHU B W,HUANG J T,et al. Knowledge mining of aircraft configuration design for sonic boom/aerodynamics[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):975-984 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0310
Citation: MA C,SHU B W,HUANG J T,et al. Knowledge mining of aircraft configuration design for sonic boom/aerodynamics[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):975-984 (in Chinese) 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
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

Knowledge mining of aircraft configuration design for sonic boom/aerodynamics

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

     

  • 图 1  本文方法流程

    Figure 1.  Flow chart of proposed method

    图 2  SOM网络结构

    Figure 2.  Structure of SOM network

    图 3  典型决策树结构

    Figure 3.  Structure of typical decision tree

    图 4  某型超声速飞行器外形

    Figure 4.  Shape of a certain supersonic aircraft

    图 5  近场计算网格

    Figure 5.  Calculation grid of near field

    图 6  SOM训练结果

    Figure 6.  Training results of SOM

    图 7  远场声爆信号波形参数

    Figure 7.  Waveform parameters of far field sonic boom signal

    图 8  设计变量Sobol指标分析

    Figure 8.  Sobol index analysis of design variables

    图 9  决策树生成结果

    Figure 9.  Result of decision tree

    图 10  优化结果

    Figure 10.  Results of optimization

    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
    下载: 导出CSV

    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)
    下载: 导出CSV

    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。
    下载: 导出CSV

    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
    X5 12 ~ 17 13 ~ 17
    第3层 X2 0.7 ~ 1.2 0.7 ~ 1.2
    X3 0.02 ~ 0.20 0.02 ~ 0.20
    下载: 导出CSV
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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

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