Modelling and simulation of regeneration in AC traction propulsion system of electrified railway

This paper presents a robust model and its simulation to investigate the performance of an AC propulsion system in a rail vehicle for directly returning the regenerative braking power to the feeder substation of an AC traction network. This direct returning method can be an efficient approach for energy recovery if the regenerative braking is reliably applied. However, it is shown that this method can cause undesired voltage fluctuations if the regenerative braking regime or braking location of the rail vehicle change. The load torque on the traction motor (TM) is precisely modelled when pure electrical braking is applied. Different states of the direct torque controlled inverter are modelled when the TM regenerates. A circuit model for the utility grid, load impedances and the traction network is developed to evaluate the network receptivity against the regenerated power. The dynamics of the electromagnetic torque and the fluctuations of the DC-link voltage are investigated for two operational conditions: changes on the regenerative braking regime and changes on the rail vehicle braking location. The results justify how the DC-link voltage dramatically fluctuates with variations of the rail vehicle's operation conditions, whereas the electromagnetic torque is maintained on optimum rates.

Flow analysis based on a pulsed flow of solution: theory, instrumentation and applications

The increased demands placed on solution propulsion by programmed flow systems, such as sequential injection analysis, lab-on-value technology, bead injection and multi-commutation, has highlighted the inability of many conventional pumps to generate a smooth, consistent flow. A number of researchers have examined ways to overcome the inadvertent, uncontrolled pulsation caused by the mechanical action of peristaltic pumps. In contrast, we have developed instruments that exploit the characteristics of a reproducible pulsed flow of solution. In this paper, we discuss our instrumental approaches and some applications that have benefited from the use of a reproducible pulsed flow rather than the traditional linear flow approach. To place our approach in the context of the continuously developing field of flow analysis, an overview of other programmed flow systems is also presented.

Analysis and implementation of swimming backward for biomimetic carangiform robot fish

The swimming backward for biomimetic carangiform robot fish is analyzed and implemented in this paper. The swimming law of the carangiform robot fish is modified according to the European Eel swimming mode based on the multiple-link structure to implement the backward motion. The motion mode difference between the eel and carangiform fish is discussed, and a qualitative kinematic analysis of the carangiform swimming in water is given to analyze the propulsion produced by the undulation of the multi-links tail. The experiments conducted demonstrate the good performance of the proposed method, and the results are given.

The design and implementation of a biomimetic robot fish

In this paper, a novel design of a biomimetic robot fish is presented. Based on the propulsion and maneuvering mechanisms of real fishes, a tail mechanical structure with cams and connecting rods for fitting carangiform fish body wave is designed, which provides the main propulsion. Two pectoral fins are mounted, and each pectoral fin can flap separately and rotate freely. Coordinating the movements of the tail and pectoral fins, the robot fish can simulate the movements of fishes in water. In order to obtain the necessary environmental information, several kinds of sensors (video, infrared, temperature, pressure and PH value sensors) were mounted. Finally, the realization of the robot fish is presented.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Design of an angular radial robotic stingray

The propeller is the primary propulsion method for underwater vehicles. It is relatively simple to implement and generally uses rotational motion from the drive through to the propeller. However, it is difficult to seal a high speed propeller shaft from water ingress. As an alternative we can look at nature's own underwater inhabitants and study their locomotive methods.