Dynamics and vibration of control valves under flow-induced vibration are analyzed. Hydrodynamic load characteristics and structural response under flow-induced vibration are mainly influenced by inertia, damping, ela...Dynamics and vibration of control valves under flow-induced vibration are analyzed. Hydrodynamic load characteristics and structural response under flow-induced vibration are mainly influenced by inertia, damping, elastic, geometric characteristics and hydraulic parameters. The purpose of this work is to investigate the dynamic behavior of control valves in the response to self-excited fluid flow. An analytical and numerical method is developed to simulate the dynamic and vibrational behavior of sliding dam valves, in response to flow excitation. In order to demonstrate the effectiveness of proposed model, the simulation results are validated with experimental ones. Finally, to achieve the optimal valve geometry, numerical results for various shapes of valves are compared. Rounded valve with the least amount of flow turbulence obtains lower fluctuations and vibration amplitude compared with the flat and steep valves. Simulation results demonstrate that with the optimal design requirements of valves, vibration amplitude can be reduced by an average to 30%.展开更多
A new human action recognition approach was presented based on chaotic invariants and relevance vector machines(RVM).The trajectories of reference joints estimated by skeleton graph matching were adopted for represent...A new human action recognition approach was presented based on chaotic invariants and relevance vector machines(RVM).The trajectories of reference joints estimated by skeleton graph matching were adopted for representing the nonlinear dynamical system of human action.The C-C method was used for estimating delay time and embedding dimension of a phase space which was reconstructed by each trajectory.Then,some chaotic invariants representing action can be captured in the reconstructed phase space.Finally,RVM was used to recognize action.Experiments were performed on the KTH,Weizmann and Ballet human action datasets to test and evaluate the proposed method.The experiment results show that the average recognition accuracy is over91.2%,which validates its effectiveness.展开更多
文摘Dynamics and vibration of control valves under flow-induced vibration are analyzed. Hydrodynamic load characteristics and structural response under flow-induced vibration are mainly influenced by inertia, damping, elastic, geometric characteristics and hydraulic parameters. The purpose of this work is to investigate the dynamic behavior of control valves in the response to self-excited fluid flow. An analytical and numerical method is developed to simulate the dynamic and vibrational behavior of sliding dam valves, in response to flow excitation. In order to demonstrate the effectiveness of proposed model, the simulation results are validated with experimental ones. Finally, to achieve the optimal valve geometry, numerical results for various shapes of valves are compared. Rounded valve with the least amount of flow turbulence obtains lower fluctuations and vibration amplitude compared with the flat and steep valves. Simulation results demonstrate that with the optimal design requirements of valves, vibration amplitude can be reduced by an average to 30%.
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education,China
文摘A new human action recognition approach was presented based on chaotic invariants and relevance vector machines(RVM).The trajectories of reference joints estimated by skeleton graph matching were adopted for representing the nonlinear dynamical system of human action.The C-C method was used for estimating delay time and embedding dimension of a phase space which was reconstructed by each trajectory.Then,some chaotic invariants representing action can be captured in the reconstructed phase space.Finally,RVM was used to recognize action.Experiments were performed on the KTH,Weizmann and Ballet human action datasets to test and evaluate the proposed method.The experiment results show that the average recognition accuracy is over91.2%,which validates its effectiveness.
