Virtual fatigue test technology of aero-engine turbine joint structure
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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。
Abstract:To solve the problems of high cost, long cycle and difficulty in monitoring in real time the test status of the fatigue test of aero-engine turbine joint structure, the virtual fatigue test technology was studied. The fatigue test of turbine joint structure was carried out to obtain load-displacement data, which were used to construct the NARX (nonlinear auto regressive model with exogenous inputs) neural network to carry out preliminary displacement prediction. On that basis, Kalman filtering was used to correct the predicted state with the measured data, and real-time prediction and updating of virtual fatigue test displacement were realized with the prediction error less than 5%. Finally, based on 3D MAX and Unity 3D, a high-fidelity digital model and virtual environment of the turbine joint structure were constructed to realize the visual display and data visualization of virtual fatigue test process of turbine joint structure.
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Key words:
- turbine joint /
- virtual test /
- neural network /
- Kalman filtering /
- visualization
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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 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 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 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
表 1 材料参数
Table 1. Material parameters
参数 数值 DD412 GH4720Li 温度$T$/℃ 20 600 800 23 600 800 密度$\rho $/（kg/m3） 8930 8140 弹性模量$E$/MPa 129.9 114.5 101.4 225 190 164 泊松比$\nu $ 0.365 0.417 0.435 0.345 0.37 0.39 
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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
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