基于QPSO混合算法的变循环发动机建模方法

肖红亮; 李华聪; 李嘉; 王淑红; 彭凯

doi:10.13700/j.bh.1001-5965.2017.0078

基于QPSO混合算法的变循环发动机建模方法

doi: 10.13700/j.bh.1001-5965.2017.0078

1.
西北工业大学动力与能源学院, 西安 710072
2.
中国航发西安动力控制科技有限公司, 西安 700077

基金项目:

国家自然科学基金 51506176

航空科学基金 6141B090302

中央高校基本科研业务费专项资金 G2017KY0003

详细信息

作者简介:
肖红亮男, 博士研究生。主要研究方向:航空发动机建模及控制

李华聪男, 博士, 教授, 博士生导师。主要研究方向:航空发动机控制、先进控制理论

通讯作者:
李华聪, E-mail:lihuacong@nwpu.edu.cn

中图分类号: V235.16;TP183
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出版历程
- 收稿日期: 2017-02-21
- 录用日期: 2017-06-05
- 网络出版日期: 2018-02-20

Modeling method of variable cycle engine based on QPSO hybrid algorithm

1.
School of Power and Energy, Northwestern Polytechnical University, Xi'an 710072
2.
AVIC Xi'an Aero-engine Controls Technology Co., Ltd., Xi'an 700077, China

Funds:

National Natural Science Foundation of China 51506176

Aeronautical Science Foundation of China 6141B090302

the Fundamental Research Funds for the Central Universities G2017KY0003

More Information

Corresponding author: LI Huacong, E-mail:lihuacong@nwpu.edu.cn

摘要

摘要:
针对变循环发动机非线性部件模型共同工作方程组求解时初值选取对收敛速度和精度的影响问题，提出一种基于量子粒子群优化（QPSO）算法与Broyden拟牛顿法混合的求解思路。首先，对变循环发动机（VCE）进行变几何特性分析以及反向传播（BP）神经网络下的外涵道稳态特性分析基础上，建立反映变几何特性以及模式切换等全状态部件模型。其次，以该模型性能计算为基准，提出了一种基于QPSO的Broyden拟牛顿混合算法来达到发动机共同工作平衡要求，通过发散系数实现混合算法的切换，以改善单一Broyden拟牛顿法对初值选取的依赖性同时提高QPSO算法的求解效率。通过高阶非线性方程组的仿真验证了算法的有效性、求解效率以及精度。最后，进行VCE部件模型稳态、动态仿真计算，结果表明：与GasTurb性能计算结果对比可以看出发动机速度特性、高度特性等变化趋势与GasTurb基本一致，且误差均小于2%；基于QPSO的Broyden拟牛顿混合算法可有效快速地完成VCE部件模型的求解；所建VCE部件模型能够有效实现该新型发动机的性能模拟分析。
- 变循环发动机(VCE) /
- 变几何特性 /
- 外涵道 /
- 非线性方程组求解 /
- 量子粒子群优化(QPSO) /
- Broyden拟牛顿法
Abstract:
A new hybrid algorithm which is based on quantum particle swarm optimization (QPSO) algorithm and Broyden quasi-Newton algorithm was proposed to reduce the effect of initial value selection on convergence speed and accuracy in solving the variable cycle engine (VCE) model. Firstly, based on the analysis of the VCE geometrical characteristics and the analysis of the steady-state characteristics of the external duct through backpropagation(BP) neural network method, a component model was established which can reflect variable geometry property and mode switching and other states of the VCE. Secondly, based on the model performance calculation, a QPSO based Broyden quasi-Newton hybrid algorithm was used to solve the VCE model cooperating equations, which improved the convergence and calculation efficiency of the hybrid algorithm by introducing the divergence coefficient to combine the two single algorithms. The effectiveness, efficiency and accuracy of the algorithm were verified by the simulation of high-order nonlinear equations. Finally, the steady state and dynamic simulation of VCE component model were carried out. The results of VCE model show that, compared with the results of GasTurb performance calculation, the trends of velocity characteristics and altitude characteristics are basically the same with those of GasTurb, the error between VCE model and GasTurb is less than 2%. The hybrid algorithm based on QPSO and Broyden quasi-Newton algorithm can solve the VCE model efficiently and quickly. The established VCE model can be used for performance simulation and analysis.
- variable cycle engine (VCE) /
- variable geometry characteristics /
- external duct /
- solving nonlinear equations /
- quantum particle swarm optimization (QPSO) /
- Broyden quasi-Newton method

HTML全文

图 1 初值选取对Broyden拟牛顿法求解收敛性的影响

Figure 1. Influence of initial value selection on solving convergence of Broyden quasi-Newton method

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图 2 初值选取对Broyden拟牛顿法收敛一致性的影响

Figure 2. Influence of initial value selection on convergence consistency of Broyden quasi-Newton method

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图 3 可调静子叶片角度为10°时，变几何特性修正计算与传统方法结果对比

Figure 3. Comparison of variable geometry characteristic correcting calculation results with results of traditional method when adjustable stator blade angle is 10°

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图 4 变循环航空发动机外涵道结构示意图

Figure 4. Schematic diagram of external duct structure of variable cycle aero-engine

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图 5 模式选择活门为5°时，进口总压分别为356.24和351.091 kPa工况时，副外涵道与CDFS涵道流场计算结果

Figure 5. Flow field calculation results of vice external duct and CDFS duct at total import pressure of 356.24 and 351.091 kPa when mode of valve 5° is selected

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图 6 神经网络训练结果

Figure 6. Neural network training results

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图 7 基于QPSO的混合算法求解方案

Figure 7. Hybrid algorithm solution based on QPSO

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图 8 高阶非线性方程组求解结果对比

Figure 8. Comparison of solving results of high-order nonlinear equations

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图 9 双涵及单涵模式下不同高度及马赫数的速度及高度特性仿真结果对比

Figure 9. Comparison of simulation results of velocity and height characteristics at different height and Mach numbers in double-duct and single-duct modes

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图 10 单、双涵模式工作VCE稳态特性对比

Figure 10. Comparison of steady-state characteristics of variable-cycle engines between single-duct and double-duct working modes

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图 11 H=8 km，Ma=0.9的双涵工作模式动态仿真结果

Figure 11. Dynamic simulation results of double-duct working mode when H=8 km and Ma=0.9

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图 12 H=12 km，Ma=1.5的单涵工作模式动态仿真结果

Figure 12. Dynamic simulation results of single-duct working mode when H=12 km and Ma=1.5

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表 1 设计点参数

Table 1. Design point parameters

参数	参数值
参数	双涵模式	单涵模式
低压转子转速PCNF	100	100
高压转子转速PCNC	100	100
风扇叶尖压比PRF_tip	3.5	3.5
风扇叶根压比PRF_root	3.2	3.2
CDFS压比PRCDFS	1.3	1.3
压气机压比PRC	6	6
燃烧室总温T4/K	1850	1850
高压涡轮落压比PRHT	2.763489	2.795642
低压涡轮落压比PRLT	1.915404	1.703737
前涵道比BYPASS1	0.3	0
后涵道比BYPASS2	0.3	0.4

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参考文献(18)

[1]	姚艳玲, 黄春峰.先进变循环发动机技术研究[J].航空制造技术, 2012(23):106-109. http://d.wanfangdata.com.cn/Periodical_hkdlxb201304033.aspx YAO Y L, HUANG C F.Research on advanced variable cycle engine[J].Aeronautical Manufacturing Technology, 2012(23):106-109(in Chinese). http://d.wanfangdata.com.cn/Periodical_hkdlxb201304033.aspx
[2]	樊思齐.航空发动机控制[M].西安:西北工业大学出版社, 2008:65-68. FAN S Q.Aeroengine control[M].Xi'an:Northwesten Polytechnical University Press, 2008:65-68(in Chinese).
[3]	KREBS J N, ALLAN R D. Supersonic propulsion-1970 to 1977: AIAA-1977-0832[R]. Reston: AIAA, 1977.
[4]	ALLAN R D. General electric company variable cycle engine technology demonstrator program: AIAA-1979-1311[R]. Reston: AIAA, 1979.
[5]	FRENCH M W, ALLEN G L. NASA VCE test bed engine aerodynamic performance characteristics and test results: AIAA-1981-1594[R]. Reston: AIAA, 1981.
[6]	US Navy instigates variable-cycle engine programme[EB/OL]. [2017-02-21]. Jane's Defence Weekly, 2011.
[7]	SIMMONS R J. Design and control of a variable geometry turbofan with an independently modulated third stream[D]. Columbus: The Ohio State University, 2009.
[8]	JOHN R. Real-time simulation of F100-PW-100 turbofan engine using the hybrid computer: NASA TMX-3261[R]. Washington, D. C. : NASA, 1975.
[9]	刘增文, 王占学, 黄红超, 等.变循环发动机性能数值模拟[J].航空动力学报, 2010, 25(6):1310-1315. http://epub.cqvip.com/pay.aspx?id=1000000246015 LIU Z W, WANG Z X, HUANG H C, et al.Numerical simulation on performance of variable cycle engines[J].Journal of Aerospace Power, 2010, 25(6):1310-1315(in Chinese). http://epub.cqvip.com/pay.aspx?id=1000000246015
[10]	苟学中, 周文祥, 黄金泉.变循环发动机部件级建模技术[J].航空动力学报, 2013, 28(1):104-111. http://www.wenkuxiazai.com/doc/85f4b00ab52acfc789ebc938-2.html GOU X Z, ZHOU W X, HUANG J Q.Component-level modeling technology for variable cycle engine[J].Journal of Aerospace Power, 2013, 28(1):104-111(in Chinese). http://www.wenkuxiazai.com/doc/85f4b00ab52acfc789ebc938-2.html
[11]	王元, 李秋红, 黄向华.变循环发动机建模技术研究[J].航空动力学报, 2013, 28(4):954-960. http://d.wanfangdata.com.cn/Periodical_hkdlxb201304033.aspx WANG Y, LI Q H, HUANG X H.Research of variable cycle engine modeling techniques[J].Journal of Aerospace Power, 2013, 28(4):954-960(in Chinese). http://d.wanfangdata.com.cn/Periodical_hkdlxb201304033.aspx
[12]	苏三买, 廉小纯.遗传算法在航空发动机非线性数学模型中的应用[J].推进技术, 2004, 25(3):237-240. http://industry.wanfangdata.com.cn/dl/Detail/Thesis?id=Thesis_Y480404 SU S M, LIAN X C.Application of genetic algorithm in aero-engine nonlinear mathematical models[J].Journal of Propulsion Technology, 2004, 25(3):237-240(in Chinese). http://industry.wanfangdata.com.cn/dl/Detail/Thesis?id=Thesis_Y480404
[13]	苏三买, 陈永琴.基于混合遗传算法的航空发动机数学模型解法[J].推进技术, 2007, 28(6):661-664. http://www.cqvip.com/QK/97609X/200706/26071467.html SU S M, CHEN Y Q.Hybrid genetic algorithm in solving aero-engine nonlinear mathematical model[J].Journal of Propulsion Technology, 2007, 28(6):661-664(in Chinese). http://www.cqvip.com/QK/97609X/200706/26071467.html
[14]	杨伟, 冯雷星, 彭靖波, 等.求解航空发动机数学模型的混合智能方法[J].推进技术, 2008, 29(5):614-616. http://d.old.wanfangdata.com.cn/Periodical/tjjs200805019 YANG W, FENG L X, PENG J B, et al.An intelligent algorithm for solution of nonlinear mathematical model for aeroengine[J].Journal of Propulsion Technology, 2008, 29(5):614-616(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/tjjs200805019
[15]	骆广琦, 刘波, 宋頔源.基于混合粒子群算法的航空发动机数学模型解法[J].燃气涡轮试验与研究, 2011, 24(2):5-8. http://d.wanfangdata.com.cn/Periodical_rqwlsyyyj201102002.aspx LUO G Q, LIU B, SONG D Y.Hybrid particle swarm optimization in solving aero-engine nonlinear mathematical model[J].Gas Turbine Experiment and Research, 2011, 24(2):5-8(in Chinese). http://d.wanfangdata.com.cn/Periodical_rqwlsyyyj201102002.aspx
[16]	尹大伟. 航空发动机模型求解算法及性能寻优控制中的参数估计研究[D]. 长沙: 国防科技大学, 2011. YIN D W. Algorithms for solving aero-engine nonlinear mathematical model and parameter estimation in performance-seeking control[D]. Changsha: National University of Defense Technology, 2011(in Chinese).
[17]	白洋, 段黎明, 柳林, 等.基于改进的混合粒子群算法的变循环发动机模型求解[J].推进技术, 2014, 35(12):1694-1700. http://www.cqvip.com/QK/97609X/201412/84747483504849524950484953.html BAI Y, DUAN L M, LIU L, at al.Solving variable cycle engine model based on improved hybrid particle swarm optimization[J].Journal of Propulsion Technology, 2014, 35(12):1694-1700(in Chinese). http://www.cqvip.com/QK/97609X/201412/84747483504849524950484953.html
[18]	KURZKE J.GasTurb 12 user's manual:Design and off-design performance of gas turbines[M].Friedrichshafen:MTU Company, 2012:79-81.