飞行器乘波前体/Bump型面优化设计方法研究

doi:10.16182/j.issn1004731x.joss.23-0172

摘要/Abstract

摘要：

飞行器前体和Bump型面是乘波体思想在飞行器部件设计中的两大经典案例，可有效提升飞行器总体气动性能，已经成为飞行器总体设计的核心技术。为寻求乘波前体和Bump型面的最优设计以提升飞行器设计效率，提出了一种可应用于乘波前体和Bump型面的优化设计方法。采用密切锥理论和圆锥绕流流场生成初始的乘波前体和Bump型面，并通过面元法快速预估气动性能；结合BP神经网络建立的代理模型和遗传算法NSGA-II对乘波前体和Bump型面快速优化；利用数据挖掘方法分析乘波前体和Bump型面的流动机理。优化后的乘波前体升阻比提升了25.6%，体积提升41.4%。Bump型面阻力系数减少10.9%，横向压力梯度增加12.1%。研究结果表明，提出的优化方法能够有效应用于乘波前体和Bump气动型面的设计优化，对飞行器整体气动性能的优化具有指导意义，在工程应用中具有重大潜力。

关键词: 乘波前体, Bump型面, NSGA-II, 高超声速, 优化研究

Abstract:

The waverider forebody and Bump profile of aircraft are two classic cases reflecting the waverider idea in aircraft component design. They can effectively improve the overall aerodynamic performance of aircraft and have become the core technology of aircraft overall design. In order to seek the optimal design of the waverider forebody and Bump profile to improve the efficiency of aircraft design, an optimization design method for the waverider forebody and Bump profile is proposed in this paper. The initial waverider forebody and Bump profile are generated by the osculating cone theory and conical flow field, and the aerodynamic performance is quickly estimated by the panel method.The surrogate model established by the back-propagation (BP) neural network and genetic algorithm NSGA-II are used to optimize the waverider forebody and Bump profile quickly. The data mining method is used to analyze the flow mechanism of the waverider forebody and Bump profile. The lift-to-drag ratio and volume of the optimized waverider forebody are increased by 25.6% and 41.4%, respectively. The drag coefficient of the Bump profile is decreased by 10.9%, and the lateral pressure gradient is increased by 12.1%. The results show that the proposed optimization method can be applied to the design optimization of the waverider forebody and Bump aerodynamic profile, which has guiding significance for the optimization of the overall aerodynamic performance of the aircraft and has great potential in engineering applications.

Key words: waverider forebody, Bump profile, NSGA-II, hypersonic velocity, optimization research

中图分类号:

TP399

邱家林,黄俊,舒鹏等 . 飞行器乘波前体/Bump型面优化设计方法研究[J]. 系统仿真学报, 2024, 36(3): 686-699.

Qiu Jialin,Huang Jun,Shu Peng,et al . Research on Optimization Design Method of Waverider Forebody/Bump Profile of Aircraft[J]. Journal of System Simulation, 2024, 36(3): 686-699.

图/表 28

图1

图2

图3

图4

图5

表1

图6

图7

表2

图8

图9

图10

图11

图12

图13

图14

图15

图16

图17

图18

图19

图20

图21

图22

图23

图24

图25

图26

参考文献 21

1	Nonweiler T R F. Aerodynamic Problems of Manned Space Vehicles[J]. The Journal of the Royal Aeronautical Society, 1959, 63(585): 521-528.
2	Simon P C, Brown D W, Huff R G. Performance of External-compression Bump Inlet at Mach Numbers of 1.5 and 2.0[EB/OL]. (1957-04-24) [2022-11-15]. .
3	O'Neill M K L. Optimized Scramjet Engine Integration on a Waverider Airframe[M]. [S.l.]: [s.n.], 1992.
4	乔文友, 黄国平, 夏晨, 等. 基于渗透边界的Bump型面反设计方法[J]. 工程热物理学报, 2014, 35(12): 2387-2392.
	Qiao Wenyou, Huang Guoping, Xia Chen, et al. Bump Surface Design Based on Permeable Boundary Inverse Method[J]. Journal of Engineering Thermophysics, 2014, 35(12): 2387-2392.
5	Simpson T W, Booker A J, Ghosh D, et al. Approximation Methods in Multidisciplinary Analysis and Optimization: A Panel Discussion[J]. Structural and Multidisciplinary Optimization, 2004, 27(5): 302-313.
6	Schmit L A Jr, Farshi B. Some Approximation Concepts for Structural Synthesis[J]. AIAA Journal, 1974, 12(5): 692-699.
7	崔玺康, 叶正寅. 高超音速飞行器前体优化设计方法研究[J]. 航空计算技术, 2006, 36(2): 77-81.
	Cui Xikang, Ye Zhengyin. Opgitimization Method Research about the Forebody and Afterbody of Hypersonic Aircraft[J]. Aeronautical Computing Technique, 2006, 36(2): 77-81.
8	张锋涛, 崔凯, 杨国伟, 等. 基于神经网络技术的乘波体优化设计[J]. 力学学报, 2009, 41(3): 418-424.
	Zhang Fengtao, Cui Kai, Yang Guowei, et al. Optimization Design of Waverider Based on the Artificial Neural Networks[J]. Chinese Journal of Theoretical and Applied Mechanics, 2009, 41(3): 418-424.
9	张竞, 邵雪明, 曾丽芳, 等. S弯进气道全自动优化设计[J]. 工业控制计算机, 2019, 32(6): 78-80.
	Zhang Jing, Shao Xueming, Zeng Lifang, et al. Design of S-shaped Inlet Fully Automatic Optimization[J]. Industrial Control Computer, 2019, 32(6): 78-80.
10	Srinivas N, Deb K. Multiobjective Function Optimization Using Nondominated Sorting Genetic Algorithms[J]. Evolutionary Computation, 1994, 2(3): 1301-1308.
11	陈小庆, 侯中喜, 何烈堂, 等. 吻切锥乘波构型优化设计与分析[J]. 国防科技大学学报, 2007, 29(4): 12-16.
	Chen Xiaoqing, Hou Zhongxi, He Lietang, et al. Optimized Design and Analyze of Osculating Cone Waverider[J]. Journal of National University of Defense Technology, 2007, 29(4): 12-16.
12	Piegl L A, Tiller W. The NURBS Book[M]. New York: Springer, 1995.
13	贺旭照, 乐嘉陵. 曲外锥乘波体进气道实用构型设计和性能分析[J]. 航空学报, 2017, 38(6): 9-19.
	He Xuzhao, Yue Jialing. Design and Performance Analysis of Practical Curved Cone Waverider Inlet[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(6): 9-19.
14	乔文友, 黄国平, 夏晨, 等. 发展用于高速飞行器前体/进气道匹配设计的逆特征线法[J]. 航空动力学报, 2014, 29(6): 1444-1452.
	Qiao Wenyou, Huang Guoping, Xia Chen, et al. Development of Inverse Characteristic Method for Matching Design of High-speed Aircraft Forebody/Inlet[J]. Journal of Aerospace Power, 2014, 29(6): 1444-1452.
15	Taylor G I, Maccoll J W. The Air Pressure on a Cone Moving at High Speeds.-II[J]. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 1933, 139(838): 298-311.
16	王磊, 何国毅, 王娜, 等. 高超声速飞行器的气动力工程计算[J]. 南昌航空大学学报(自然科学版), 2020, 34(1): 1-6.
	Wang Lei, He Guoyi, Wang Na, et al. Engineering Method for Computing the Aerodynamics Performance of Hypersonic Vehicle[J]. Journal of Nanchang Hangkong University(Natural Sciences), 2020, 34(1): 1-6.
17	Mckay M D, Beckman R J, Conover W J. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code[J]. Technometrics, 2000, 42(1): 55-61.
18	Chiba Zouhair, Abghour Noureddine, Moussaid Khalid, et al. A Novel Architecture Combined with Optimal Parameters for Back Propagation Neural Networks Applied to Anomaly Network Intrusion Detection[J]. Computers & Security, 2018, 75: 36-58.
19	张连文, 夏人伟. Pareto 最优解的充分及必要条件[J]. 北京航空航天大学学报, 1997, 23(2): 64-69.
	Zhang Lianwen, Xia Renwei. On Necessary and Sufficient Pareto Optimality Conditions in Vector Optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 1997, 23(2): 64-69.
20	弭宝福. 遗传算法进化策略的改进研究[D]. 哈尔滨: 东北农业大学, 2014.
	Mi Baofu. The Improvement Research on Evolution Strategy of Genetic Algorithm[D]. Harbin: Northeast Agricultural University, 2014.
21	Lloyd S P. Least Squares Quantization in PCM[J]. IEEE Transactions on Information Theory, 1982, 28(2): 129-137.

序号	主要参数	量值
1	β_cu	4.5°
2	β_cd	8°
3	β_bm	10°
4	B_X₁	0.8
5	B_X₂	0.6
6	β_C1	0.8
7	β_C2	0.6

序号	主要参数	量值
1	k	1.4
2	M_∞	5.5 Ma
3	T₀	300 k
4	P₀	101 325 pa
5	R	287.04

[1]	张洪亮, 徐静茹, 谈波, 徐公杰. 考虑交货期的双资源柔性作业车间节能调度[J]. 系统仿真学报, 2023, 35(4): 734-746.
[2]	刘云钦, 李妮, 赵路明, 白金鹏, 李婷珽, 王晨光. 高超声速飞行器流热固耦合实时仿真技术[J]. 系统仿真学报, 2021, 33(12): 2820-2827.
[3]	凡永华, 皇甫逸伦, 郭晓雯, 李辰璐, 李国飞. 拦截临近空间滑翔目标的衰减记忆滤波中制导方法[J]. 系统仿真学报, 2021, 33(11): 2742-2752.
[4]	刘晓岑, 吴云洁, 徐鹏. 考虑输入饱和的高超声速飞行器姿态控制[J]. 系统仿真学报, 2019, 31(11): 2553-2561.
[5]	王维平, 王涛, 李小波, 刘佳杰. 一种基于模体的无人机集群智能任务规划方法[J]. 系统仿真学报, 2018, 30(4): 1211-1220.
[6]	王旭, 刘士新, 张瑞友, 王佳. 求解集装箱码头泊位-岸桥分配多目标算法[J]. 系统仿真学报, 2018, 30(3): 1178-1189.
[7]	李海林, 张斌, 吴德伟, 卢虎. 一种高超声速飞行器组合导航故障检测方法[J]. 系统仿真学报, 2017, 29(8): 1809-1814.
[8]	陶超. 基于高斯伪谱法的高超声速飞行器轨迹优化与跟踪控制[J]. 系统仿真学报, 2017, 29(4): 865-872.
[9]	陈红永, 范宣华, 王柯颖, 方叶, 肖世富. 基于大规模并行的高超声速飞行器动力学特性仿真[J]. 系统仿真学报, 2015, 27(8): 1715-1720.
[10]	孙学功, 刘璟. 多物理场耦合仿真简化方法研究[J]. 系统仿真学报, 2015, 27(6): 1165-1174.
[11]	孙学功, 龚春叶. 高超声速飞行器并行仿真方法研究[J]. 系统仿真学报, 2015, 27(5): 948-958.
[12]	陶超, 艾剑良. 基于卡尔曼滤波的高超声速飞行器控制器设计[J]. 系统仿真学报, 2015, 27(2): 376-383.
[13]	蒋金, 陈长兴, 汪成, 陈婷, 周天翔, 凌云飞. 太赫兹波在非均匀等离子体鞘套中的传播特性[J]. 系统仿真学报, 2015, 27(12): 3109-3115.