The settling of particles in fluid flows is a common occurrence in various industrial processes. Investigating the interactions between particles and fluid during settling holds significant importance. This article pr...The settling of particles in fluid flows is a common occurrence in various industrial processes. Investigating the interactions between particles and fluid during settling holds significant importance. This article presents a numerical study of the settling process involving two parallel particles in upward flow, employing the immersed boundary method (IBM). The simulation data were validated using experimental results for single spherical particle settlement, two parallel spherical particles settlement, and the settlement of two series of spherical particles. A comparative analysis was conducted between particle settling in upward flow and static fluid. The study explores the impact of different upward velocities and initial particle spacing on particle settling. Results indicate that the wake generated by the two parallel particles in upward flow forms a distinct boundary with the surrounding fluid. As the upward velocity increases, this boundary becomes increasingly observable. In comparison to settling in static flow, the repulsive effect between two parallel particles in upward flow is stronger, and the settling velocity of particles is smaller. Furthermore, the study reveals that the repulsion between two particles diminishes rapidly with an increase in the initial spacing, but the final settling velocity of particles remains nearly constant.展开更多
A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network(RNN) ...A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network(RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative(PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization(PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.展开更多
基金supported by the National Natural Science Foundation of China(grant No.52222601).
文摘The settling of particles in fluid flows is a common occurrence in various industrial processes. Investigating the interactions between particles and fluid during settling holds significant importance. This article presents a numerical study of the settling process involving two parallel particles in upward flow, employing the immersed boundary method (IBM). The simulation data were validated using experimental results for single spherical particle settlement, two parallel spherical particles settlement, and the settlement of two series of spherical particles. A comparative analysis was conducted between particle settling in upward flow and static fluid. The study explores the impact of different upward velocities and initial particle spacing on particle settling. Results indicate that the wake generated by the two parallel particles in upward flow forms a distinct boundary with the surrounding fluid. As the upward velocity increases, this boundary becomes increasingly observable. In comparison to settling in static flow, the repulsive effect between two parallel particles in upward flow is stronger, and the settling velocity of particles is smaller. Furthermore, the study reveals that the repulsion between two particles diminishes rapidly with an increase in the initial spacing, but the final settling velocity of particles remains nearly constant.
基金supported by MOST under Grants No.104-2632-B-468-001,No.103-2221-E-468-009-MY2,No.104-2221-E-182-008-MY2,No.105-2221-E-468-009,No.106-2221-E-468-023,and No.106-2221-E-182-033Chang Gung Memorial Hospital,under Grants No.CMRPD2C0052 and No.CMRPD2C0053
文摘A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network(RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative(PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization(PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.
