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航空发动机涡轮榫接结构虚拟试验技术

黄宏扬 胡殿印 赵炎 陈高翔 鄢林 潘锦超

黄宏扬, 胡殿印, 赵炎, 等. 航空发动机涡轮榫接结构虚拟试验技术[J]. 航空动力学报, 2024, 39(9):20220620 doi: 10.13224/j.cnki.jasp.20220620
引用本文: 黄宏扬, 胡殿印, 赵炎, 等. 航空发动机涡轮榫接结构虚拟试验技术[J]. 航空动力学报, 2024, 39(9):20220620 doi: 10.13224/j.cnki.jasp.20220620
HUANG Hongyang, HU Dianyin, ZHAO Yan, et al. Virtual fatigue test technology of aero-engine turbine joint structure[J]. Journal of Aerospace Power, 2024, 39(9):20220620 doi: 10.13224/j.cnki.jasp.20220620
Citation: HUANG Hongyang, HU Dianyin, ZHAO Yan, et al. Virtual fatigue test technology of aero-engine turbine joint structure[J]. Journal of Aerospace Power, 2024, 39(9):20220620 doi: 10.13224/j.cnki.jasp.20220620

航空发动机涡轮榫接结构虚拟试验技术

doi: 10.13224/j.cnki.jasp.20220620
基金项目: 国家自然科学基金(51875023,52022007); 国家科技重大专项(2017-Ⅳ-0004-0041,J2019-Ⅳ-0009-0077)
详细信息
    作者简介:

    黄宏扬(2000-),男,博士生,主要从事航空发动机涡轮榫接结构疲劳虚拟试验技术研究

    通讯作者:

    陈高翔(1982-),男,副研究员,博士,主要从事航空发动机结构强度与可靠性、航空发动机数值仿真技术、航空发动机信息化技术方面的研究。E-mail:chengx@buaa.edu.cn

  • 中图分类号: V219

Virtual fatigue test technology of aero-engine turbine joint structure

  • 摘要:

    为解决航空发动机涡轮榫接结构疲劳试验成本高、周期长,且试验过程状态难以实时监控等问题,开展了涡轮榫接结构疲劳的虚拟试验技术研究。通过涡轮榫接结构模拟件的疲劳试验获取载荷-位移数据,构建NARX(nonlinear auto regressive model with exogenous inputs)神经网络模型,开展位移初步预测;在此基础上采用Kalman滤波引入实测数据对预测状态进行修正,实现疲劳虚拟试验位移的实时预测和更新且预测误差均小于5%;最后,基于3D MAX和Unity 3D平台,构建高度保真的涡轮榫接结构数字模型和虚拟环境,实现涡轮榫接结构疲劳虚拟试验过程的直观展示以及数据可视化。

     

  • 图 1  榫头模拟件工程图(单位:mm)

    Figure 1.  Engineering drawing of tenon simulated specimen (unit:mm)

    图 2  榫槽模拟件工程图(单位:mm)

    Figure 2.  Engineering drawing of mortise simulated specimen (unit:mm)

    图 3  涡轮榫接模拟件三维模型

    Figure 3.  3D model of turbine joint simulated specimen

    图 4  涡轮榫接模拟件低周疲劳试验装置

    Figure 4.  Low cycle fatigue test device of turbine joint simulated specimen

    图 5  NARX时间序列神经网络结构图

    Figure 5.  Structure of the NARX neural network

    图 6  闭环神经网络预测

    Figure 6.  Closed-loop neural network prediction

    图 7  Kalman滤波修正当前状态

    Figure 7.  Kalman filtering updated current state

    图 8  16 kN峰值载荷下虚拟试验位移预测结果

    Figure 8.  Predicted results of displacement in virtual test under 16 kN load

    图 9  16 kN峰值载荷下虚拟试验位移预测误差分析

    Figure 9.  Predicted error analysis of displacement in virtual test under 16 kN load

    图 10  13 kN峰值载荷下虚拟试验位移预测结果

    Figure 10.  Predicted results of displacement in virtual test under 13 kN load

    图 11  13 kN峰值载荷下虚拟试验位移预测误差分析

    Figure 11.  Predicted error analysis of displacement in virtual test under 13 kN load

    图 12  虚拟试验可视化实现方案

    Figure 12.  Visualization implementation scheme of virtual test

    图 13  疲劳机实物、三维模型及渲染图

    Figure 13.  Physical object, 3D model and rendering of fatigue machine

    图 14  试验件及夹具实物、三维模型及渲染图

    Figure 14.  Physical objects, 3D models and renderings of joint structure and their fixture

    图 15  涡轮榫接结构疲劳虚拟试验主界面

    Figure 15.  Main interface of virtual fatigue test of turbine joint structure

    图 16  试验过程人机交互界面

    Figure 16.  Man-machine interaction interface in test process

    图 17  虚拟试验过程及数据动态可视化

    Figure 17.  Virtual test process and data dynamic visualization

    表  1  材料参数

    Table  1.   Material parameters

    参数数值
    DD412GH4720Li
    温度$T$/℃2060080023600800
    密度$\rho $/(kg/m389308140
    弹性模量$E$/MPa129.9114.5101.4225190164
    泊松比$\nu $0.3650.4170.4350.3450.370.39
    下载: 导出CSV

    表  2  试验件低周疲劳寿命数据

    Table  2.   Low cycle fatigue life data of test pieces

    试验编号 疲劳寿命/循环 试验编号 疲劳寿命/循环
    1 43315 6 119041
    2 22301 7 76771
    3 35850 8 81621
    4 32721 9 146961
    5 52891 10 63812
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-25
  • 网络出版日期:  2024-03-15

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