摘要
以初步确定的试飞中滑油压力和滑油温度影响参数集合为基准输入参数,基于大量试飞数据,采用人工神经网络方法,获得滑油压力模型和滑油温度模型的基准结果。随后,采用不同的基准输入参数子集进行人工神经网络计算,以模型计算结果与试飞结果的最大偏差、偏差分布范围作为判据,与基准结果对比,确定滑油压力和滑油温度的最大影响参数。最后,建立发动机全包线试飞、全工作状态的滑油压力和滑油温度最大影响参数确定方法。该方法对滑油系统的试飞内容规划、状态预判和安全监控等具有重要的指导作用。
The reference models of aero-engine oil pressure and oil temperature were constructed on refer-ence input parameter sets preliminarily determined by using the ANN method for training and validation ofa large number of engine flight test data. Moreover, by applying all subsets of reference input parameter setsas inputs of ANN, the main influence parameters of oil pressure and oil temperature were determined. Thecriteria to determine the main influence parameters included the maximum errors between ANN calculationresults and flight-test results, and the ratios of error ranges, which were compared with the results of refer-ence models. By all means, a method to determine the main influence parameters of aero-engine oil pres-sure and oil temperature in entire flight envelope and whole engine state was developed, which could guidethe design of the flight test content, and the state prediction and monitoring of oil system.
出处
《燃气涡轮试验与研究》
北大核心
2017年第1期7-13,共7页
Gas Turbine Experiment and Research
关键词
航空发动机
飞行试验
全包线试飞
全工作状态
滑油系统
滑油压力
滑油温度
人工神经网络
aero-engine
flight test
entire flight envelope
whole engine state
oil system
oil pressure
oil temperature
ANN(Artificial Neural Network)