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RISS 인기검색어
- 검색키워드
- 저자 : Qingyuan Zhu (검색결과 3 건)
- 무료
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- 유료
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Strain energy-based fatigue life prediction under variable amplitude loadings
Zhu, Shun-Peng,Yue, Peng,Correia, Jose,Blason, Sergio,De Jesus, Abilio,Wang, Qingyuan Techno-Press 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.66 No.2
With the aim to evaluate the fatigue damage accumulation and predict the residual life of engineering components under variable amplitude loadings, this paper proposed a new strain energy-based damage accumulation model by considering both effects of mean stress and load interaction on fatigue life in a low cycle fatigue (LCF) regime. Moreover, an integrated procedure is elaborated for facilitating its application based on S-N curve and loading conditions. Eight experimental datasets of aluminum alloys and steels are utilized for model validation and comparison. Through comparing experimental results with model predictions by the proposed, Miner's rule, damaged stress model (DSM) and damaged energy model (DEM), results show that the proposed one provides more accurate predictions than others, which can be extended for further application under multi-level stress loadings.
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Strain energy-based fatigue life prediction under variable amplitude loadings
Shun-Peng Zhu,Peng Yue,José Correia,Sergio Blasón,Abílio De Jesus,Qingyuan Wang 국제구조공학회 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.66 No.2
With the aim to evaluate the fatigue damage accumulation and predict the residual life of engineering components under variable amplitude loadings, this paper proposed a new strain energy-based damage accumulation model by considering both effects of mean stress and load interaction on fatigue life in a low cycle fatigue (LCF) regime. Moreover, an integrated procedure is elaborated for facilitating its application based on S-N curve and loading conditions. Eight experimental datasets of aluminum alloys and steels are utilized for model validation and comparison. Through comparing experimental results with model predictions by the proposed, Miner’s rule, damaged stress model (DSM) and damaged energy model (DEM), results show that the proposed one provides more accurate predictions than others, which can be extended for further application under multi-level stress loadings.
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A novel data-driven rollover risk assessment for articulated steering vehicles using RNN
Xuanwei Chen,Wei Chen,Liang Hou,Huosheng Hu,Xiangjian Bu,Qingyuan Zhu 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.5
Articulated steering vehicles have outstanding capability operating but suffer from frequent rollover accidents due to their complicated structure. It is necessary to accurately detect their rollover risk for drivers to take action in time. Their variable structure and the variable center of mass exhibit nonlinear time-variant behavior and increase the difficulty of dynamic modelling and lateral stability description. This paper proposes a novel data-driven modelling methodology for lateral stability description of articulated steering vehicles. The running data is first collected based on the typical operations that prone to rollover and then classified into two types: Safety and danger. The data quality is further improved by wavelet transformation. Finally, an RNN model is built on the data. The experimental results show that the output of the RNN model can accurately quantify lateral stability of the vehicle, i.e., the risk of rollover, when it is turning and crossing uneven surfaces or obstacles.
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