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基于改进粒子群的航空发动机部件特性修正
李明洲,嵇润民,黄向华
南京航空航天大学 能源与动力学院,江苏省航空动力系统重点实验室,江苏 南京 210016
摘要:
涡桨发动机采用定转速工作方式,试车数据集中分布在几个不同转速工作状态附近。为利用大量分布不均匀的试车数据进行部件特性修正,提出一种改进参数的模拟退火粒子群算法,解决多工作点性能匹配时易陷入局部最优的问题,提高涡桨发动机部件特性修正精度。针对以往依靠经验和试错确定修正系数定义域时效率低下,且限制了算法搜索能力的问题,提出一种根据相邻等转速线确定非设计点区域修正系数上下限的方法。模型计算结果与实验数据对比表明,修正后模型各参数平均误差从3.95%降低到0.89%,最大误差从11.32%降低到2.37%,精度明显提高。
关键词:  涡桨发动机  部件特性  修正  试车数据  数学模型  粒子群优化
DOI:10.13675/j.cnki.tjjs.210662
分类号:V233.7
基金项目:国家自然科学基金(51576097;51976089);国防科技委员会基础研究强化项目(2017-JCJQ-ZD-047-21)。
Aeroengine Component Characteristic Correction Based on Improved Particle Swarm Optimization
LI Ming-zhou, JI Run-min, HUANG Xiang-hua
Jiangsu Province Key Laboratory of Aerospace Power System,College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
Abstract:
Turboprop engine adopts constant speed working mode, and the test data are distributed centrally around several different speed working states. In order to make use of a large number of unevenly distributed test data for component characteristic correction, a simulated annealing particle swarm optimization algorithm with improved parameters is proposed to solve the problem of easily falling into local optimum when performance matching at multiple working points, and improve the characteristic correction accuracy of turboprop engine components. In order to solve the problem of low efficiency and limited searching ability of the algorithm when determining the correction coefficient domain by experience and trial and error in the past, a method is proposed to determine the upper and lower limits of the correction coefficient in the region of non-design points according to adjacent equal speed lines. The comparison between the calculated results and the experimental data shows that the average error of each parameter of the modified model decreases from 3.95% to 0.89%, and the maximum error decreases from 11.32% to 2.37%. The accuracy is obviously improved.
Key words:  Turboprop engine  Component characteristics  Correction  Test data  Mathematical model  Particle swarm optimization