84 resultados para Unmanned Aerial Vehicles (UAVs)


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Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are rapidly gaining popularity as a means of de-carbonization in the transport sector in tackling sustainable energy supply and environment pollution problems. To build a proper battery model is essential in predicting battery behaviour under various operating conditions for avoiding unsafe battery operations and developing proper controlling algorithms and maintenance strategies. This paper presents a comprehensive review of battery modelling methods. In particular, the mechanism and characteristics of Li-ion batteries are presented, and different modelling methods are discussed. Considering that equivalent electric circuit models (EECMs) are the most widely used, a detailed analysis of the modelling procedure is presented.

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Indirect bridge monitoring methods, using the responses measured from vehicles passing over bridges, are under development for about a decade. A major advantage of these methods is that they use sensors mounted on the vehicle, no sensors or data acquisition system needs to be installed on the bridge. Most of the proposed methods are based on the identification of dynamic characteristics of the bridge from responses measured on the vehicle, such as natural frequency, mode shapes, and damping. In addition, some of the methods seek to directly detect bridge damage based on the interaction between the vehicle and bridge. This paper presents a critical review of indirect methods for bridge monitoring and provides discussion and recommendations on the challenges to be overcome for successful implementation in practice.

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Bridge structures are subject to continuous degradation due to the environment, ageing and excess loading. Monitoring of bridges is a key part of any maintenance strategy as it can give early warning if a bridge is becoming unsafe. This paper will theoretically assess the ability of a vehicle fitted with accelerometers on its axles to detect changes in damping of bridges, which may be the result of damage. Two vehicle models are used in this investigation. The first is a two degree-of-freedom quarter-car and the second is a four degree-of-freedom halfcar. The bridge is modelled as a simply supported beam and the interaction between the vehicle and the bridge is a coupled dynamic interaction algorithm. Both smooth and rough road profiles are used in the simulation and results indicate that changes in bridge damping can be detected by the vehicle models for a range of vehicle velocities and bridge spans.

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This paper describes a ‘drive-by’ method of bridge inspection using an instrumented vehicle. Accelerometers on the vehicle are proposed as a means of detecting damage on the bridge in the time it takes for the vehicle to cross the bridge at full highway speed. For a perfectly smooth road profile, the method is shown to be feasible. Changes in bridge damping, which is an indicator of damage, are clearly visible in the acceleration signal of a quarter-car vehicle on a smooth road surface modelled using MatLab. When road profile is considered, the influence of changes in bridge damping on the vehicle acceleration signal is much less clear. However, when a half-car model is used on a road with a rough profile, it is again possible to detect changes in bridge damping, provided the vehicle has two identical axles.

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A conventional way to identify bridge frequencies is utilizing vibration data measured directly from the bridge. A drawback with this approach is that the deployment and maintenance of the vibration sensors are generally costly and time-consuming. One way to cope with the drawback is an indirect approach utilizing vehicle vibrations while the vehicle passes over the bridge. In the indirect approach, however, the vehicle vibration includes the effect of road surface roughness, which makes it difficult to extract the bridge modal properties. One solution may be subtracting signals of two trailers towed by a vehicle to reduce the effect of road surface roughness. A simplified vehicle-bridge interaction model is used in the numerical simulation; the vehicle - trailer and bridge system are modeled as a coupled model. In addition, a laboratory experiment is carried out to verify results of the simulation and examine feasibility of the damage detection by the indirect method.

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During benthic cultivation Mytilus edulis (blue mussels) are subject to predation pressure from a number of predators including Carcinus maenas (shore crabs). This predator can be responsible for substantial losses of mussels from the fishery and a full understanding of the predator–prey relationship between M. edulis and C. maenas is required to ensure attempts that reduce predatory pressure and subsequent commercial loss are successful. Whilst much work has examined the prey–predator size relationships between C. maenas and M. edulis, far less research has investigated how stress, such as periods of extended aerial exposure, may affect these relationships. We tested whether profit in terms of calories gained by crabs consuming mussels stressed by aerial exposure for 48 h differed from that of mussels at ambient conditions and whether being stressed affected the mussel's likelihood of predation. We also tested whether the size relationship between predators and their prey differed when mussels were stressed. We found that the profitability of prey (calories gained per second of handling time) did not vary between stressed and unstressed mussels. Handling times for stressed and unstressed mussels were similar, even when crabs were presented with mussels of the maximum size that they are able to consume. Small crabs were more likely to reject a mussel of preferred size if it was unstressed, suggesting that crabs may be able to assess that these mussels would require extra effort to break into and consume. Our findings suggest that the predator–prey relationship between mussels and crabs is not altered when mussels are stressed. C. maenas remains a voracious predator and regardless of the condition of mussels laid on commercial beds there is a need to control this predator in attempt to reduce losses in the benthic fishery.

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Simulation is a well-established and effective approach to the development of fuel-efficient and low-emissions vehicles in both on-highway and off-highway applications.

The simulation of on-highway automotive vehicles is widely reported in literature, whereas research relating to non-automotive and off-highway vehicles is relatively sparse. This review paper focuses on the challenges of simulating such vehicles and discusses the differences in the approach to drive cycle testing and experimental validation of vehicle simulations. In particular, an inner-city diesel-electric hybrid bus and an ICE (Internal Combustion Engine) powered forklift truck will be used as case studies.

Computer prediction of fuel consumption and emissions of automotive vehicles on standardised drive cycles is well-established and commercial software packages such as AVL CRUISE have been specifically developed for this purpose. The vehicles considered in this review paper present new challenges from both the simulation and drive-cycle testing perspectives. For example, in the case of the forklift truck, the drive cycles involve reversing elements, variable mass, lifting operations, and do not specify a precise velocity-time profile. In particular, the difficulties associated with the prediction of productivity, i.e. the maximum rate of completing a series of defined operations, are discussed. In the case of the hybrid bus, the standardised drive cycles are unrepresentative of real-life use and alternative approaches are required in the development of efficient and low-emission vehicles.

Two simulation approaches are reviewed: the adaptation of a standard automotive vehicle simulation package, and the development of bespoke models using packages such as MATLAB/Simulink.

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This paper presents an approach to COLREGs compliant ship navigation. A system architecture is proposed, which will be implemented and tested on two platforms: networked bridge simulators and at sea trials using an autonomous unmanned surface vessel. Attention is paid to collision avoidance software and its risk mitigation.