Multi-hit ballistic damage characterisation of 304 stainless steel plates with finite elements

Multi-impact of projectiles on thin 304 stainless steel plates is investigated to assess the degradation of ballistic performance, and to characterise the inherent mechanisms. Assessment of ballistic degradation is by means of a double-impact of rigid spheres at the same site on a circular clamped plate. The limiting velocity of the second impact, will be altered by the velocity of the antecedent impact. Finite element analyses were used to elucidate experimental results and understand the underlying mechanisms that give rise to the performance degradation. The effect of strength and ductility on the single and multi-impact performance was also considered. The model captured the experimental results with excellent agreement. Moreover, the material parameters used within the model were exclusively obtained from published works with no fitting or calibration required. An attempt is made to quantify the elevation of the ballistic limit of thin plates by the dynamic mechanism of travelling hinges. Key conclusions: The multi-hit performance scales linearly with the single-hit performance; and strength is a significantly greater effector of increased ballistic limit than ductility, even at the expense of toughness. © 2014 Elsevier Ltd.

Corrugated all-composite sandwich structures. Part 2: Failure mechanisms and experimental programme

A novel corrugated composite core, referred to as a hierarchical corrugation, has been developed and tested experimentally. Hierarchical corrugations exhibit a range of different failure modes depending on the geometrical properties and the material properties of the structures. In order to understand the different failure modes the analytical strength model, developed in part 1 of this paper, was used to make collapse mechanism maps for the different corrugation configurations. If designed correctly, the hierarchical structures can have more than 7 times higher weight specific strength compared to its monolithic counter part. The difference in strength arises mainly from the increase in buckling resistance of the sandwich core members compared to the monolithic version. The highest difference in strength is seen for core configurations with low overall density. As the density of the core increases, the monolithic core members get stockier and more resistant to buckling and thus the benefits of the hierarchical structure reduces. © 2008 Elsevier Ltd. All rights reserved.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.