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科研机构
北京航空航天大学 [41]
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期刊论文 [24]
会议论文 [17]
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2019 [6]
2018 [5]
2017 [8]
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2014 [8]
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专题:北京航空航天大学
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Fatigue life prediction method based on the features of surface defects [基于表面缺陷特征的疲劳寿命预测方法]
期刊论文
Hangkong Dongli Xuebao/Journal of Aerospace Power, 2019, 卷号: 34, 页码: 92-98
作者:
Wei, D.
;
Chen, Y.
;
Wang, Y.
;
Gao, J.
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2019/12/30
Forecasting
Powder metallurgy
Superalloys
Surface defects
Turbines
Calculation results
Fatigue life prediction
Life prediction methods
Life predictions
Powder metallurgy turbine disk
Stress characteristics
Stress gradient
Turbine disks
Fatigue of materials
A novel model modification method for support vector regression based on radial basis functions
期刊论文
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2019, 卷号: 60, 页码: 983-997
作者:
Yan, Cheng
;
Shen, Xiuli
;
Guo, Fushui
;
Zhao, Shiqi
;
Zhang, Lizhang
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2019/12/30
Support vector regression
Model modification
Radial basis functions
Turbine disk
Experimental study of thermal loading management strategy for the transient process of a rotating turbine disk
期刊论文
EXPERIMENTAL THERMAL AND FLUID SCIENCE, 2019, 卷号: 103, 页码: 234-246
作者:
Li, Guo
;
Li, Ye
;
Bao, Mengyao
;
Ding, Shuiting
收藏
  |  
浏览/下载:5/0
  |  
提交时间:2019/12/30
Thermal management strategy
Turbine disk
Transient process
Thermal stress
Temperature gradient
Tensile Behavior and Microstructural Evolution of the Polycrystalline Nickel-Based Superalloy Applied in Turbine Disk
期刊论文
INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES, 2019, 卷号: 36, 页码: 339-346
作者:
Shen, Xiuli
;
Dong, Shaojing
;
Chen, Zhiying
收藏
  |  
浏览/下载:52/0
  |  
提交时间:2019/12/30
tensile behavior
microstructural evolution
nickel-based superalloy
high temperature
uniaxial tension
Multi-failure probabilistic design for turbine bladed disks using neural network regression with distributed collaborative strategy
期刊论文
AEROSPACE SCIENCE AND TECHNOLOGY, 2019, 卷号: 92, 页码: 464-477
作者:
Song, Lu-Kai
;
Bai, Guang-Chen
;
Fei, Cheng-Wei
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/12/30
Multi-failure probabilistic analysis
Surrogate model
Turbine bladed disk
Neural network regression
Distributed collaborative strategy
变温热处理过程的相场模拟及其参数标定
期刊论文
航空工程进展, 2019, 卷号: 10, 页码: 255-262
作者:
王婵
;
石磊
;
石多奇
;
杨晓光
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/12/30
航空发动机涡轮盘 晶粒演化 相场模型 Arrhenius关系 晶界迁移率 热处理 aeroengine turbine disk grain evolution phase field model Arrhenius relationship grain boundary mobility heat treatment
Local maximum-entropy based surrogate model and its application to structural reliability analysis
期刊论文
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2018, 卷号: 57, 页码: 373-392
作者:
Fan, Jiang
;
Liao, Huming
;
Wang, Hao
;
Hu, Junheng
;
Chen, Zhiying
收藏
  |  
浏览/下载:8/0
  |  
提交时间:2019/12/30
Local Maximum-Entropy
Surrogate model
Structural reliability analysis
Turbine disk
Uncertainty quantification
Fatigue life of turbine disk based on actively managed thermal loading method [基于主动热载荷管理技术的涡轮盘疲劳寿命]
期刊论文
Hangkong Dongli Xuebao/Journal of Aerospace Power, 2018, 卷号: 33, 页码: 1896-1904
作者:
Ding, S.
;
Chu, J.
;
Li, G.
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2019/12/30
Cracks
Fatigue of materials
Thermal fatigue
Thermal load
Turbines
Change trends
Energy distributions
Failure risk
Finite element numerical simulation
General weights
Thermal boundary
Thermal loadings
Turbine disks
Stress analysis
Substructure-based distributed collaborative probabilistic analysis method for low-cycle fatigue damage assessment of turbine blade-disk
期刊论文
AEROSPACE SCIENCE AND TECHNOLOGY, 2018, 卷号: 79, 页码: 636-646
作者:
Gao, Haifeng
;
Wang, Anjenq
;
Bai, Guangchen
;
Wei, Chunmei
;
Fei, Chengwei
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/12/30
Reliability analysis
Fatigue damage
Turbine blade-disk system
Random variable
Substructure method
SDCPAM
EVALUATION METHOD OF UNCERTAINTY OF RELIABILITY CALCULATION FOR TURBINE DISK LIFE
会议论文
PROCEEDINGS OF THE ASME TURBO EXPO: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, 2018, VOL 7A, 2018-01-01
作者:
Fan, Jiang
;
Chen, Gaoxiang
;
Shen, Xiuli
;
Fan, Tianyi
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2019/12/30
turbine disk
reliability
geometry variation
surrogate model
uncertainty quantification
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