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基于地面试车数据的高涵道比涡扇发动机飞行性能预估方法

Method of flight performance prediction of high bypass ratio turbofan engine based on ground test data
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摘要 为了预估高涵道比涡扇发动机飞行性能,使用GasTurb 11软件的试车数据分析功能计算出了某高涵道比涡扇发动机地面试车点与设计点各部件效率和流路损失的偏差.通过非设计点敏感性分析确定设计点与地面试车点的效率与损失偏差的相关性,最后预估得到高涵道比涡扇发动机的飞行性能.对某高涵道比涡扇发动机飞行性能预估研究表明:该方法切实可行,其中地面试车数据分析、地面和设计点偏差关系图、以及非设计敏感性分析是预估高涵道比涡扇发动机飞行性能的3个关键环节。 In order to predict the flight performance of a high bypass ratio turbofan engine. Errors of component efficiencies and pressure losses of a high bypass ratio turbofan engine at ground test point and design point were estimated by using GasTurb 11 software. And then the relationships of the errors between design point and ground test point were determined through the non-design point sensitivity analysis method. In terms of the above relationships determined, the flight performance of the high bypass ratio turbofan engine can be predicted at last. Prediction of the flight performance of a high bypass ratio engine shows that the method is feasible, analysis of ground test data, relationships of delta value of ground point and design point and the non-design point sensitivity analysis are three key links in predicting the flight performance of high bypass ratio turbofan engines.
出处 《航空动力学报》 EI CAS CSCD 北大核心 2012年第8期1773-1777,共5页 Journal of Aerospace Power
关键词 高涵道比涡扇发动机 飞行性能预估 试车数据分析 非设计点敏感性分析 设计点性能 high bypass ratio turbofan ground test data analysis design point performance engine flight performance prediction non-design point sensitivity analysis
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  • 1陈大光,韩凤学,唐耿林.多状态气路分析法诊断发动机故障的分析[J].航空动力学报,1994,9(4):349-352. 被引量:34
  • 2范作民,孙春林,林兆福.发动机故障方程的建立与故障因子的引入[J].中国民航学院学报,1994,12(1):1-14. 被引量:8
  • 3窦建平,黄金泉,周文祥.基于UML的航空发动机仿真建模研究[J].航空动力学报,2005,20(4):684-688. 被引量:20
  • 4范作民 孙春林.航空发动机状态诊断[M].天津:天津科技翻译出版公司,1997.. 被引量:1
  • 5朱之丽 孟凡涛.模型辨识法诊断发动机故障的分析[A]..温州:航空学会第十一届动力控制学术会议论文集[C].,2002.148-152. 被引量:2
  • 6[1]Duyar A, Eldem V, Merrill W, et al. Fault detection and diagnosis in propulsion systems: a fault parameter estimation approach [J].AIAA Journal of Guidance, Control and Dynamics, 1994, 17( 1 ):104 ~ 108 被引量:1
  • 7[5]Kassidas A,Taylor P A,MacGregor J F. Off-line diagnosis of deterministic faults in continuous dynamic multivariable processes using speech recognition methods[J]. Journal of Process Control, 1998,8(5/6) :381 ~ 393 被引量:1
  • 8[6]Keogh E J,Paazani M J. Scaling up dynamic time warping for data mining applications [A]. In: Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining[C]. Boston,2000. 285 ~ 289 被引量:1
  • 9严寒松.[D].南京:南京航空航天大学,1996. 被引量:2
  • 10Nowicki Robert.Rough Sets in the Neuro-Fuzzy Architectures Based on Non-Monotonic Fuzzy Implications[R].Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science),v 3070,Artificial Intelligent and Soft Computing-ICAISC 2004:518~525. 被引量:1

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