Sustainable hydrogen production via LiH hydrolysis for unmanned air vehicle (UAV) applications

In the current study, an experimental approach for the further understanding of the LiH hydrolysis reaction for hydrogen production is considered. The experimental work has been undertaken under small scale conditions by utilising fixed bed reactors. The hydrolysis reaction has been studied at several oven temperatures (150 °C, 300 °C and 500 °C). The favourable driving potentials for the hydrolysis reactions were identified by the utilisation of the Gibbs free energy analysis. The main outcome of the study is the deceleration of the reaction pace due to the formation of the by-product layers during the reaction. At the initial stage, due to the contact of steam with the unreacted and fresh LiH surface, the reaction proceeds on a fast pace, while the formation of the layers tends to decelerate the diffusion of steam into the core of material, forcing the production step to be slower. The hydrogen yield was found to be more than 90% of the theoretical value for all the reaction temperatures. Finally, a scenario of a hybrid-electric propulsion system for Unmanned Aerial Vehicles (UAVs) including Li-ion battery, Proton Membrane Fuel Cell (PEMFC) and an on-board hydrogen production system based on LiH hydrolysis is introduced and studied

A novel potential field algorithm and an intelligent multi-classifier for the automated control and guidance system (ACOS)

The ACOS project seeks to improve and develop novel robot guidance and control systems integrating Novel Potential Field autonomous navigation techniques, multi-classifier design with direct hardware implementation. The project development brings together a number of complementary technologies to form an overall enhanced system. The work is aimed at guidance and collision avoidance control systems for applications in air, land and water based vehicles for passengers and freight. Specifically, the paper addresses the generic nature of the previously presented novel Potential Field Algorithm based on the combination of the associated rule based mathematical algorithm and the concept of potential field. The generic nature of the algorithm allows it to be efficient, not only when applied to multi-autonomous robots, but also when applied to collision avoidance between a single autonomous agent and an obstacle displaying random velocity. In addition, the mathematical complexity, which is inherent when a large number of autonomous vehicles and dynamic obstacles are present, is reduced via the incorporation of an intelligent weightless multi-classifier system which is also presented