Based on the finite volume method, three methods for rotational region treatment were presented and validated by simulating two-dimensional accelerating rotational flows. Separate transient incompressible flows induced by crossshaped blades during starting process were simulated using the dynamic mesh, sliding mesh and dynamic reference frame methods. The computing performance and stability of the three methods were evaluated by comparing numerical results, and the transient characteristics of the accelerating rotational flow were analysed numerically. Results showed that the starting process affected the flow structure and transient characteristics of the accelerating rotational flows. The sliding mesh method showed higher computational efficiency and accuracy compared with other methods, and could easily be extended to solve three-dimensional transient flows in hydraulic machineries under transient operations, such as start-up and shutdown.

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In order to investigate the mechanism of the low‐density solid particle and liquid mixing process, a specialised agitator structure was used. Both computational fluid dynamics simulation and experiments were carried out to study the two‐phase mixing characteristics in the stirred vessel. The mixing process was captured by snapshots. The flow field and solid phase volume fraction evolution were analysed. Experimental and numerical results agreed well with each other. Solid particles floating on the liquid surface were gradually transported to the bottom through the centre of the vessel and the mixing time was predicted and tested. Results indicate that the agitator structure used in this study is able to form an obvious axial circulation in the vessel and then achieve a good performance in low‐density solid and liquid mixing operations. The study provides a valuable reference for the design and optimisation of solid–liquid mixing equipment. © 2011 Canadian Society for Chemical Engineering

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In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.