Considerations For Future Fuels in Naval Vessels

Emissions regulations aimed at reducing carbon dioxide and other emissions are driving commercial research into alternative fuels. Being government owned, naval vessels are exempt from these regulations, but not auxiliary vessels including the RFA and patrol boats. Some governments have committed to meeting regulations where possible and public or even legal pressure may strengthen a requirement for operation on low-or zero emissions fuels in future, even if only in peacetime. These new fuels present major challenges for naval use, such as lower energy density, increased toxicity, increased flammability and explosion risk, which has implications on storage and use. This paper summarises ongoing work using the ZEOLIT tool, previously presented at INEC 2018, to assess the overall ship impacts of adopting alternative fuels over a range of warship sizes, rather than single exemplar designs. Application of methanol and ammonia to a generic frigate design has been found to lead to increases in size that do not seem excessive, and that more efficient but expensive machinery (fuel cells) is desirable as reductions in displacement are significant compared to increases in cost

Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation

When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel’s structure, are to be considered

Development of Bus Drive Technology towards Zero Emissions: A Review

This chapter aims to provide a comprehensive review of the latest low emission propulsion vehicles, particularly for bus applications. The challenges for city bus applications and the necessity for low emission technologies for public transportation are addressed. The review will be focusing on the London bus environment, which represents one of the busiest bus networks in the world. The low emission bus applications will be analysed from three main areas: hybrid electric buses, battery electric buses and fuel cell buses. This summarises the main technologies utilised for low emissions urban transportation applications. A comprehensive review of these low emission technologies provides the reader with a general background of the developments in the bus industry and the technologies utilised to improve the performance in terms of both efficiency and emission reduction. This will conclude with a summary of the advantages and disadvantages of the three main technologies and explore the potential opportunity of each

An End-to-End Task Allocation Framework for Autonomous Mobile Systems

This work aims to unravel the problem of task allocation and planning for multi-agent systems with a particular interest in promoting adaptability. We proposed a novel end-to-end task allocation framework employing reinforcement learning methods to replace the handcrafted heuristics used in previous works. The proposed framework achieves high adaptability and also explores more competitive results. Learning experiences from the feedback help to reach the advantages. The systematic objectives are adjustable and responsive to the reward design intuitively. The framework is validated in a set of tests with various parameter settings, where adaptability and performance are demonstrated

Development and Evaluation of a Degree of Hybridisation Identification Strategy for a Fuel Cell Supercapacitor Hybrid Bus

Abstract: Fuel cells (FC) are a clean energy source that are capable of powering a vehicle’s electrical energy requirements whilst providing zero operating emissions. In this study, a full-scaled computer model FC/supercapacitor (SC) hybrid has been developed to investigate the performance of the hybrid propulsion system under real-world performance conditions. A control strategy focused on maintaining a constant FC output at a user-defined value has been developed and applied to the FC/SC hybrid model. Driving cycles collected from a practical double-decker bus have been utilised to evaluate the developed model. It has been demonstrated that the proposed control strategy is capable of maintaining a constant and stable FC output while meeting a real world dynamic load. Based on the obtained results, a general strategy to identify the degree of hybridisation between the FC and the SC in a FC hybrid system has been developed and demonstrated

Anisotropic GPMP2: a fast continuous-time Gaussian processes based motion planner for unmanned surface vehicles in environments with ocean currents

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs' motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2

Using the forward movement of a container ship navigating in the Arctic to air-cool a marine organic Rankine cycle unit

Ice coverage in the Arctic is declining, opening up new shipping routes which can drastically reduce voyage lengths between Asia and Europe. There is also a drive to improve ships energy efficiency to meet international emissions design regulations such as the mandated Energy Efficiency Design Index. The organic Rankine cycle is one thermodynamic cycle that is being actively examined to improve the design and operational efficiency of ships. Low heat sink temperatures can significantly increase waste heat recovery systems thermal efficiency. In Arctic regions, the ambient air temperature can be much lower than the sea temperature, presenting interesting opportunities. However, using air as the cooling medium requires larger condensers and power compared to a water-cooled system. This paper investigates the exploitation of the forward movement of a container ship navigating in the Arctic and density-change induced flows as means of moving air through the condenser to reduce the fan power required. The organic Rankine cycle unit uses the waste heat available from the scavenge air to produce electric power. A two-step optimisation method is used with the objective of minimising the annual CO2 emissions of the ship. The results suggest that the supportive cooling could reduce the fan power by up to 60%, depending on ambient air temperature

Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults.

Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year. Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups. The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration. The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems. [Abstract copyright: © The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.