基于任务段的航空发动机载荷谱聚类方法
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摘要
针对航空发动机飞行任务剖面分类问题,对发动机31个飞行任务剖面进行了聚类分析。选取飞行高度和飞行马赫数作为划分飞行任务剖面的参数,依据其对应的飞行任务段均值生成聚类散点图,将剖面类型划分为5大类。结果表明:低空低速剖面在无量纲飞行高度为0～0.2、飞行马赫数为0.4～0.6区间,均值最低;高空高速剖面在无量纲飞行高度为1.2～2.2、飞行马赫数为1.0～1.8区间,均值最高;飞行任务剖面在无量纲飞行高度为0.2～1.2、飞行马赫数为0.6～1.0区间内最为集中;针对散点密集区域,可依据剖面特征进一步划分剖面类型;不同剖面间,飞行高度与飞行马赫数差异性强,利于剖面划分,而法向过载与转速差异性小,不利于剖面的划分。所提出的方法可以快速有效的对航空发动机飞行任务剖面进行聚类分析。
According to the classification problem of aeroengine mission profile,31 flight mission profiles of a fighter engine were clustered.The flight altitude and flight Mach number were selected as the parameters of the flight mission profile,and the cluster scatter plot was generated according to the corresponding flight mission segment average,and the profile types were divided into five categories.Results showed that the low-altitude lowspeed profile had the lowest mean value at the dimensionless flight altitude of 0-0.2 and the flight Mach number of 0.4-0.6.The high-altitude high-speed profile had the highest mean value at the dimensionless flight altitude of 1.2-2.2 and the flight Mach number of 1.0-1.8,with the flight mission profile.The dimensionless flight altitude was 0.2-1.2,and the flight Mach number was 0.6-1.0.The contour was further divided according to the profile characteristics;the difference between the flight height and the flight Mach number was different between different sections,which was beneficial to the section division.The difference between normal overload and speed was small,which was not conducive to the division of the profile.The proposed method can quickly and effectively cluster the aeronautical engine mission profile.
According to the classification problem of aeroengine mission profile,31 flight mission profiles of a fighter engine were clustered.The flight altitude and flight Mach number were selected as the parameters of the flight mission profile,and the cluster scatter plot was generated according to the corresponding flight mission segment average,and the profile types were divided into five categories.Results showed that the low-altitude lowspeed profile had the lowest mean value at the dimensionless flight altitude of 0-0.2 and the flight Mach number of 0.4-0.6.The high-altitude high-speed profile had the highest mean value at the dimensionless flight altitude of 1.2-2.2 and the flight Mach number of 1.0-1.8,with the flight mission profile.The dimensionless flight altitude was 0.2-1.2,and the flight Mach number was 0.6-1.0.The contour was further divided according to the profile characteristics;the difference between the flight height and the flight Mach number was different between different sections,which was beneficial to the section division.The difference between normal overload and speed was small,which was not conducive to the division of the profile.The proposed method can quickly and effectively cluster the aeronautical engine mission profile.
引文
[1]国防科学技术工业委员会.航空涡轮喷气和涡轮风扇发动机通用规范使用指南:GJB/Z216-2004[S].北京:国防科学技术工业委员会出版社,2004:1-170.
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[10]张利敏.基于模糊聚类算法的路面识别的研究[D].江苏镇江:江苏科技大学,2014.ZHANG Limin.Research of road recognition based on fuzzy clustering algorithm[D].Zhenjiang Jiangsu:Jiangsu University of Science and Technology,2014.(in Chinese)
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[14]孙中辉,刘宝印,郭彦颖.基于系统聚类的不同零部件的失效地区特征分析[R].上海:中国汽车工程学会年会,2016.
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[16]任冀宾,王斌团.基于飞行参数灵敏度的飞机疲劳载荷谱典型飞行任务剖面研究[R].江西井冈山:第14届全国疲劳与断裂学术会议,2008.
[17]付春梅,刘俊宁.Kmeans与系统聚类法结合在脑电图中的应用[J].科技信息,2007(29):191-192.FU Chunmei,LIU Junning.Application of Kmeans combined with system clustering in EEG[J].Science and Technology Information,2007(29):191-192.(in Chinese)
[18]韩雅雯.Kmeans聚类算法的改进及其在信息检索系统中的应用[D].昆明:云南大学,2016.HAN Yawen.Improvement of Kmeans clustering algorithm and its application in information retrieval system[D].Kunming:Yunnan University,2016.(in Chinese)
[19]李双虎,王铁洪.Kmeans聚类分析算法中一个新的确定聚类个数有效性的指标[J].河北省科学院学报,2003,20(4):199-202.LI Shuanghu,WANG Tiehong.New validity index for determining the number of clusters in Kmeans clustering[J].Journal of the Hebei Academy of Sciences,2003,20(4):199-202.(in Chinese)
[20]赵方伟,谢基龙.小应力循环对C70E型车体疲劳损伤的影响研究[J].机械工程学报,2014,50(10):121-126.ZHAO Fangwei,XIE Jilong.Influence of small stress cycles on the fatigue damage of C70Ecar body[J].Journal of Mechanical Engineering,2014,50(10):121-126.(in Chinese)
[2]国防科学技术工业委员会.航空燃气涡轮喷气、涡轮风扇发动机通用规范:GJB241-87[S].北京:航空标准化出版社,1976:1-166.
[3]United States Air Force.Engine structural integrity program(ENSIP):MIL-STD-1783[S].Washington:United States Department of Defense,1984:1-59.
[4]United States Department of Defense.General specification for aircraft turbojet,turbofan,turboshaft and turboprop engine:JSSG-87231A[S].Washington:United States Department of Defense,1995:1-370.
[5]程礼,冯伟,陈卫.航空发动机飞行任务剖面统计规律研究[J].航空动力学报,2003,18(6):749-752.CHENG Li,FENG Wei,CHEN Wei.Study of the statistical rules for flight mission profiles of aero-engine[J].Journal of Aerospace Power,2003,18(6):749-752.(in Chinese)
[6]宋迎东,高德平.发动机机动飞行类综合载荷谱研究[J].航空动力学报,2002,17(2):212-216.SONG Yingdong,GAO Deping.Aeroengine composite maneuver loading spectrum derivation[J].Journas of Aerospace Power,2002,17(2):212-216.(in Chinese)
[7]苏清友,孔瑞莲,陈筱雄.航空涡喷、涡扇发动机主要零部件定寿指南[M].北京:航空工业出版社,2004.
[8]航空工业部发动机管理局.WP-6发动机寿命研究报告[R].沈阳:沈阳发动机厂,1988.
[9]程德金.模糊数学在飞行试验数据处理中的应用[J].飞行力学,1994,12(2):52-59.CHENG Dejin.The applications of fuzzy math in data processing[J].Flight Dynamics,1994,12(2):52-59.(in Chinese)
[10]张利敏.基于模糊聚类算法的路面识别的研究[D].江苏镇江:江苏科技大学,2014.ZHANG Limin.Research of road recognition based on fuzzy clustering algorithm[D].Zhenjiang Jiangsu:Jiangsu University of Science and Technology,2014.(in Chinese)
[11]WANG H M,KIM J H,JUNG D Y.Power Interconnected system clustering with advanced fuzzy C-mean algorithm[J].Journal of Central South University of Technology,2011,18(1):190-195.
[12]WANG Decai,ZHANG Ganlin,PAN Xianzhang.Mapping soil texture of a plain area using fuzzy-C-means clustering method based on land surface diurnal temperature difference[J].Pedosphere,2012,22(3):394-403.
[13]赵章焰,夏文俊,李爱华,等.基于层次聚类桥架起重机整机载荷谱编制方法[J].武汉理工大学学报,2015,37(12):68-72.ZHAO Zhangyan,XIA Wenjun,LI Aihua,et al.Method of compiling whole machine load spectrum of bridge crane based on clustering algorithm[J].Journal of Wuhan University of Technology,2015,37(12):68-72.(in Chinese)
[14]孙中辉,刘宝印,郭彦颖.基于系统聚类的不同零部件的失效地区特征分析[R].上海:中国汽车工程学会年会,2016.
[15]宋迎东,高德平.发动机飞行任务剖面的主成份聚类法[J].航空动力学报,2002,17(2):196-200.SONG Yingdong,GAO Deping.The principal component analysis method for engine flight mission profiles categorization[J].Journal of Aerospace Power,2002,17(2):196-200.(in Chinese).
[16]任冀宾,王斌团.基于飞行参数灵敏度的飞机疲劳载荷谱典型飞行任务剖面研究[R].江西井冈山:第14届全国疲劳与断裂学术会议,2008.
[17]付春梅,刘俊宁.Kmeans与系统聚类法结合在脑电图中的应用[J].科技信息,2007(29):191-192.FU Chunmei,LIU Junning.Application of Kmeans combined with system clustering in EEG[J].Science and Technology Information,2007(29):191-192.(in Chinese)
[18]韩雅雯.Kmeans聚类算法的改进及其在信息检索系统中的应用[D].昆明:云南大学,2016.HAN Yawen.Improvement of Kmeans clustering algorithm and its application in information retrieval system[D].Kunming:Yunnan University,2016.(in Chinese)
[19]李双虎,王铁洪.Kmeans聚类分析算法中一个新的确定聚类个数有效性的指标[J].河北省科学院学报,2003,20(4):199-202.LI Shuanghu,WANG Tiehong.New validity index for determining the number of clusters in Kmeans clustering[J].Journal of the Hebei Academy of Sciences,2003,20(4):199-202.(in Chinese)
[20]赵方伟,谢基龙.小应力循环对C70E型车体疲劳损伤的影响研究[J].机械工程学报,2014,50(10):121-126.ZHAO Fangwei,XIE Jilong.Influence of small stress cycles on the fatigue damage of C70Ecar body[J].Journal of Mechanical Engineering,2014,50(10):121-126.(in Chinese)
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