基于协同智能移动Kriging模型的襟翼偏转角可靠性分析

Q3 Engineering 西北工业大学学报 Pub Date : 2023-04-01 DOI:10.1051/jnwpu/20234120253
Lei Liu, D. Teng, Yun Feng
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引用次数: 0

摘要

为了有效监测民用飞机襟翼偏转角的可靠性,结合快速存取记录仪(QAR),利用Kriging模型、分解与协调策略、平衡优化器(EO)和移动最小二乘(MLS),提出了协同智能移动克里格(CIMK)方法。其中,采用分解协调策略处理襟翼左右偏角之间的关系。采用MLS选择有效的建模样本,求解Kriging模型的待定系数。采用EO法确定最优MLS局部紧凑支撑区域半径。首先分析了皮瓣左右不对称的故障原因,明确了QAR数据中的主要特征参数;其次,结合相关影响参数的QAR数据,利用CIMK构建民机襟翼偏转模型(极限状态函数);然后,采用蒙特卡罗(MC)采样方法分析了民用飞机襟翼偏转角的可靠性及其对可靠性的影响。结果表明,当襟翼偏转角为3°时,可靠性为0.450 2,影响襟翼偏转角的重要因素有马赫数、左攻角、右攻角等。与响应面法(RSM)、Kriging、支持向量机(SVM)和BP-ANN相比,该方法的平均绝对误差精度相对提高了53.02%、51.43%、49.03%和44.04%,平均相对误差精度相对提高了68.36%、66.76%、64.41%和62.64%,建模效率相对提高了50.62%、26.35%和43.01%。当模拟次数为103次时,分析精度分别相对提高8.82%、7.25%、6.22%和3.98%。
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Reliability analysis of flap deflection angle based on collaborative intelligent moving Kriging model
To effectively monitor the reliability of civil aircraft flap deflection angle, combined with the quick access recorder(QAR), the collaborative intelligent moving Kriging(CIMK) method is proposed by absorbing the Kriging model, decomposition and co-ordination strategy, equilibrium optimizer(EO), and moving least square(MLS). Among them, the decomposition coordination strategy is used to deal with the relationship between the flaps left and right deflection angles. MLS is employed to select effective modeling samples and solve the undetermined coefficients of Kriging model. EO method is applied to determine optimizing the local compact support region radius of MLS. Firstly, the fault reason for flap left-right asymmetry is analyzed to clarify the main characteristic parameters in QAR data. Secondly, combined with the QAR data of relevant influencing parameters, the civil aircraft flap deflection model(limit state function) is constructed by using CIMK. Then, the reliability and influence of civil aircraft flap deflection angle are analyzed by Monte Carlo(MC) sampling method. The results show that when the flap deflection angle is 3°, the reliability is 0.450 2, and the important factors affecting the flap deflection angle are Mach number, left angle of attack, right angle of attack, etc. Compared with the response surface method(RSM), Kriging, support vector machine(SVM), and back-propagation-artificial neural network(BP-ANN), the average absolute error accuracy of the proposed method is relative improved by 53.02%, 51.43%, 49.03%, and 44.04%, the average relative error accuracy is relative improved by 68.36%, 66.76%, 64.41%, and 62.64%, and the modeling efficiency is relative improved by 50.62%, 26.35%, and 43.01% respectively compared with Kriging, SVM and BP-ANN. When the number of simulations is 103, the analysis accuracy is relative improved by 8.82%, 7.25%, 6.22%, and 3.98% respectively.
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来源期刊
西北工业大学学报
西北工业大学学报 Engineering-Engineering (all)
CiteScore
1.30
自引率
0.00%
发文量
6201
审稿时长
12 weeks
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