航空发动机涡轮榫接结构虚拟试验技术
Virtual fatigue test technology of aero-engine turbine joint structure
黄宏扬 1胡殿印 2赵炎 1陈高翔 1鄢林 3潘锦超3
作者信息
- 1. 北京航空航天大学航空发动机研究院,北京 100191
- 2. 北京航空航天大学航空发动机研究院,北京 100191;北京航空航天大学航空发动机结构强度北京市重点实验室,北京 100191;中小型航空发动机联合研究中心,北京 100191
- 3. 北京航空航天大学能源与动力工程学院,北京 100191
- 折叠
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.
关键词
涡轮榫接/虚拟试验/神经网络/Kalman滤波/可视化Key words
turbine joint/virtual test/neural network/Kalman filtering/visualization引用本文复制引用
基金项目
国家自然科学基金(51875023)
国家自然科学基金(52022007)
国家科技重大专项(2017-Ⅳ-0004-0041)
国家科技重大专项(J2019-Ⅳ-0009-0077)
出版年
2024
NETL
NSTL
万方数据