Influence of particle properties on the erosive wear of sintered boron carbide

Sintered boron carbide is very hard, and can be an attractive material for wear-resistant components in critical applications. Previous studies of the erosion of less hard ceramics have shown that their wear resistance depends on the nature of the abrasive particles. Erosion tests were performed on a sintered boron carbide ceramic with silica, alumina and silicon carbide erodents. The different erodents caused different mechanisms of erosion, either by lateral cracking or small-scale chipping; the relative values of the hardness of the erodent and the target governed the operative mechanism. The small-scale chipping mechanism led to erosion rates typically an order of magnitude lower than the lateral fracture mechanism. The velocity exponents for erosion in the systems tested were similar to those seen in other work, except that measured with the 125 to 150 μm silica erodent. With this erodent the exponent was initially high, then decreased sharply with increasing velocity and became negative. It was proposed that this was due to deformation and fragmentation of the erodent particles. In the erosion testing of ceramics, the operative erosion mechanism is important. Care must be taken to ensure that the same mechanism is observed in laboratory testing as that which would be seen under service conditions, where the most common erodent is silica.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

Robust multiplexed MPC for distributed multi-agent systems

This paper extends the authors' earlier work which adapted robust multiplexed MPC for application to distributed control of multi-agent systems with non-interacting dynamics and coupled constraint sets in the presence of persistent unknown, but bounded disturbances. Specifically, we propose exploiting the single agent update nature of the multiplexed approach, and fix the update sequence to enable input move-blocking and increased discretisation rates. This permits a higher rate of individual policy update to be achieved, whilst incurring no additional computational cost in the corresponding optimal control problems to be solved. A disturbance feedback policy is included between updates to facilitate finding feasible solutions. The new formulation inherits the property of rapid response to disturbances from multiplexing the control and numerical results show that fixing the update sequence does not incur any loss in performance. © 2011 IFAC.

Simultaneous reduction of particulate matter and NOx emissions using 4-way catalyzed filtration systems

The next generation of diesel emission control devices includes 4-way catalyzed filtration systems (4WCFS) consisting of both NOx and diesel particulate matter (DPM) control. A methodology was developed to simultaneously evaluate the NOx and DPM control performance of miniature 4WCFS made from acicular mullite, an advanced ceramic material (ACM), that were challenged with diesel exhaust. The impact of catalyst loading and substrate porosity on catalytic performance of the NOx trap was evaluated. Simultaneously with NOx measurements, the real-time solid particle filtration performance of catalyst-coated standard and high porosity filters was determined for steady-state and regenerative conditions. The use of high porosity ACM 4-way catalyzed filtration systems reduced NOx by 99% and solid and total particulate matter by 95% when averaged over 10 regeneration cycles. A "regeneration cycle" refers to an oxidizing ("lean") exhaust condition followed by a reducing ("rich") exhaust condition resulting in NOx storage and NOx reduction (i.e., trap "regeneration"), respectively. Standard porosity ACM 4-way catalyzed filtration systems reduced NOx by 60-75% and exhibited 99.9% filtration efficiency. The rich/lean cycling used to regenerate the filter had almost no impact on solid particle filtration efficiency but impacted NOx control. Cycling resulted in the formation of very low concentrations of semivolatile nucleation mode particles for some 4WCFS formulations. Overall, 4WCFS show promise for significantly reducing diesel emissions into the atmosphere in a single control device. © 2013 American Chemical Society.

An integrated 2-D model of a lithium ion battery: The effect of material parameters and morphology on storage particle stress

An integrated 2-D model of a lithium ion battery is developed to study the mechanical stress in storage particles as a function of material properties. A previously developed coupled stress-diffusion model for storage particles is implemented in 2-D and integrated into a complete battery system. The effect of morphology on the stress and lithium concentration is studied for the case of extraction of lithium in terms of previously developed non-dimensional parameters. These non-dimensional parameters include the material properties of the storage particles in the system, among other variables. We examine particles functioning in isolation as well as in closely-packed systems. Our results show that the particle distance from the separator, in combination with the material properties of the particle, is critical in predicting the stress generated within the particle. © 2012 Springer-Verlag.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.