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.

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.