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Breathing characteristics of rotor cracks based on time-varying centroid position
Tingqiong Cui,Yinong Li,Jicheng Ma,Yanlin Jin,Cheng Wang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.2
On the basis of material and fracture mechanics theory, the flexible matrix and stiffness matrix of crack elements are derived using the strain energy release rate and the Timoshenko beam element model of six degrees of freedom. In accordance with the geometric relationship between the opening and closing of crack, time-varying centroid coordinates of the crack are fixed by neutral axis theory, and the position of the crack closing line is obtained depending on the time-varying centroid coordinates, which describe the crack's breathing characteristics. On this basis, a time-varying centroid coordinate model is proposed and compared with the sinusoidal model, the Gaozhu model, and the zero-stress intensity factor model. Results show that the proposed model has more coupling characteristics. Finally, the flexibility matrix of oblique, straight, and semielliptical crack elements is derived, and the breathing mechanism of three kinds of cracks is studied.
Xuqiang Qiao,Ling Zheng,Yinong Li,Ziwei Zhang,Jie Zeng,Hao Zheng 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.2
A novel stochastic model predictive control (SMPC) scheme is proposed for automotive scenes based on high-performance and practical motion state prediction method. The significant properties of the proposed scheme are that: 1) it can accurately predict disturbances within the prediction horizon, and 2) the prediction results can be considered into the optimizing process to obtain a more efficient and accurate controller. As a result, the proposed adaptive cruise control (ACC) system can ensure driving safety and improve tracking accuracy and comfort performance while satisfying different driving styles. In detail, a large amount of naturalistic driving data is collected based on a real vehicle test platform at first. Then an adaptive optimization Gaussian process regression (AOGPR) is developed and trained with real measurements to predict the motion states of the preceding vehicle. The prediction module is embedded in SMPC to bind the collision conditions, tighten the states and finally construct a novel controller, i.e., AOGPR-SMPC controller. A bidirectional LSTM (BiLSTM) network is trained and tested for online recognizing driving styles to satisfy personalized car-following needs. The simulation and field tests verify and evaluate the proposed controller. The results demonstrate that the ACC system could realize personalized carfollowing according to the driver’s driving style, and the proposed controller can obtain better tracking accuracy and comfort performance compared with the GPR-SMPC controller and MPC controller.