Based on the Reynolds equation with Reynolds boundary conditions, the Castelli method was employed to solve the Reynolds equation for oil lubrication upon bearings. By doing so, a profile of nonlinear oil film force o...Based on the Reynolds equation with Reynolds boundary conditions, the Castelli method was employed to solve the Reynolds equation for oil lubrication upon bearings. By doing so, a profile of nonlinear oil film force of single-pad journal bearings is established. According to the structure of combination journal bearings, nonlinear oil film force of combination journal bearing is obtained by retrieval, interpolation and assembly techniques. As for symmetrical flexible Jeffcott rotor systems supported by combination journal bearings, the nonlinear motions of the center of the rotor are calculated by the self-adaptive Runge-Kutta method and Poincar6 mapping with different rotational speeds. The numerical results show that the system performance is slightly better when the pivot ratio changes from 0.5 to 0.6, and reveals nonlinear phenomena of periodic, period-doubing, quasi-periodic motion, etc.展开更多
A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar f...A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar friction. A steady-state Kalman filter is built based on the model of pipeline dynamics. Pressure signals at both ends of a target section of a pipe are input to the model of pipeline dynamics, and as an output of the model an estimated pressure signal at a mid-point of the pipe is obtained. Difference between measured and estimated pressure signals at the mid-point is fed back to the model of pipeline dynamics to modify state variables of the model. According to the Kalman filter principle, the state variables of the model are adjusted so that they converge to real values. It is demonstrated that real-time implementation of the Kalman filter is possible with the sampling time of 0.1 ms.展开更多
基金Project(2007CB707706) supported by the National Basic Research Program of China Projects(51075327,10972179) supported by the National Natural Science Foundation of China+2 种基金 Project(SKLMT-KFKT-201011) supported by Open Foundation of State Key Laboratory of Mechanical Transmission,China Projects(2009JQ7006,2007E203) supported by the Natural Science Foundation of Shaanxi Province of China Projects(09JK680,07JK340) supported by the Natural Science Foundation of Department of Education of Shaanxi Province of China
文摘Based on the Reynolds equation with Reynolds boundary conditions, the Castelli method was employed to solve the Reynolds equation for oil lubrication upon bearings. By doing so, a profile of nonlinear oil film force of single-pad journal bearings is established. According to the structure of combination journal bearings, nonlinear oil film force of combination journal bearing is obtained by retrieval, interpolation and assembly techniques. As for symmetrical flexible Jeffcott rotor systems supported by combination journal bearings, the nonlinear motions of the center of the rotor are calculated by the self-adaptive Runge-Kutta method and Poincar6 mapping with different rotational speeds. The numerical results show that the system performance is slightly better when the pivot ratio changes from 0.5 to 0.6, and reveals nonlinear phenomena of periodic, period-doubing, quasi-periodic motion, etc.
文摘A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar friction. A steady-state Kalman filter is built based on the model of pipeline dynamics. Pressure signals at both ends of a target section of a pipe are input to the model of pipeline dynamics, and as an output of the model an estimated pressure signal at a mid-point of the pipe is obtained. Difference between measured and estimated pressure signals at the mid-point is fed back to the model of pipeline dynamics to modify state variables of the model. According to the Kalman filter principle, the state variables of the model are adjusted so that they converge to real values. It is demonstrated that real-time implementation of the Kalman filter is possible with the sampling time of 0.1 ms.
