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基于主要状态识别的涡轴发动机载荷谱研究 被引量:2

Research on Load Spectrum of Turboshaft Engine Based on Main State Recognition
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摘要 针对涡轴发动机实际使用载荷与设计状态存在差别的问题,对实际使用载荷状态下涡轴发动机的复杂载荷谱进行了研究。基于某型直升机实际飞行数据,使用雨流计数提取了转速参数的循环幅值、均值、时间等信息,并依据小幅值转速循环峰谷值的分布规律,利用k-means聚类方法获得了涡轴发动机的5个主要工作状态,进而提出将发动机的载荷状态分类为包含主、次循环的11个典型循环类型。依据疲劳理论推导出等寿命线的转速表达形式,利用距离判别法将造成较大疲劳损伤的状态众多的大幅值循环判别归类为11个典型循环类型,进而得到涡轴发动机的模拟任务载荷谱。所建立的分类方法和模拟载荷谱可以为涡轴发动机及其零部件可靠性、耐久性研究提供借鉴,为涡轴发动机加速寿命试车等试验提供贴合实际使用载荷状态的输入条件。 In view of the difference between the actual working load and the design state of the turboshaft engine,the complex load spectrum of the turboshaft engine under the actual working load state was studied.Based on the actual flight data of a certain type of helicopter,the rain-flow counting method was used to extract the cyclic amplitude,mean,time and other information of the speed parameter.And according to the distribution of the small-amplitude rotational speed cycle,five main working states of the turboshaft engine were obtained by k-means clustering method.And 11 typical cycle types including primary and secondary cycles were formed.Based on the fatigue theory,the speed expression of equal-life curve was deduced.By using the distance discriminant method,the large amplitude cycles which cause large fatigue damage were classified into 11 typical cycles.And the load spectrum of the engine flight mission was obtained.The classification method and simulated load spectrum established in this paper can provide a reference for the reliability and durability research of the turboshaft engine and its components,and provide input conditions that conform to the actual load conditions for tests,such as the accelerated life test of turboshaft engine.
作者 滕怀亮 李本威 韦祥 张赟 王永华 TENG Huailiang;LI Benwei;WEI Xiang;ZHANG Yun;WANG Yonghua(Aviation Foundation College, Naval Aviation University, Yantai 264001, China)
出处 《兵器装备工程学报》 CAS 北大核心 2020年第5期144-150,240,共8页 Journal of Ordnance Equipment Engineering
基金 国家自然科学基金项目(51505492) 泰山学者建设工程专项。
关键词 载荷谱 涡轴发动机 雨流计数 等寿命曲线 机器学习 load spectrum turboshaft engine rain-flow counting equal-life curve machine learning
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