Characterizing gas film conduction for particle- particle and particle-wall collisions

Heat transfer in granular media is an important mechanism in many industrial applications. For some applications conduction is an important mode of heat transfer. Several models have been proposed to describe particle scale conduction both between particles (particle-particle) and with walls (particle-wall). Within these conduction models are several distinct modes: conduction through physical contact (macro-contact), conduction through surface roughness (micro-contacts), and conduction through the stagnant gas film surrounding each particle (particle-fluid-particle or particle- fluid-wall). While these models have been developed and verified in literature, the relationship between the conduction heat transfer coefficient and key parameters is not immediately obvious. This is especially true for gas film conduction. In this work we investigate gas film conduction for particle- particle and particle-wall collisions via DEM simulations using a well-established gas film model to determine the behavior of the heat transfer coefficient as a function of the separation distance and particle size. With a better understanding of the gas film heat transfer coefficient, we propose a simplified model that captures the same response but is easier to understand and significantly more computationally efficient

Microcantilever Studies of Angular Field Dependence of Vortex Dynamics in BSCCO

Using a nanogram-sized single crystal of BSCCO attached to a microcantilever we demonstrate in a direct way that in magnetic fields nearly parallel to the {\it ab} plane the magnetic field penetrates the sample in the form of Josephson vortices rather than in the form of a tilted vortex lattice. We further investigate the relation between the Josephson vortices and the pancake vortices generated by the perpendicular field component.Comment: 5 pages, 8 figure

Conceptualisation of an Efficient Particle-Based Simulation of a Twin-Screw Granulator

Discrete Element Method (DEM) simulations have the potential to provide particle-scale understanding of twin-screw granulators. This is difficult to obtain experimentally because of the closed, tightly confined geometry. An essential prerequisite for successful DEM modelling of a twin-screw granulator is making the simulations tractable, i.e., reducing the significant computational cost while retaining the key physics. Four methods are evaluated in this paper to achieve this goal: (i) develop reduced-scale periodic simulations to reduce the number of particles; (ii) further reduce this number by scaling particle sizes appropriately; (iii) adopt an adhesive, elasto-plastic contact model to capture the effect of the liquid binder rather than fluid coupling; (iv) identify the subset of model parameters that are influential for calibration. All DEM simulations considered a GEA ConsiGma™ 1 twin-screw granulator with a 60° rearward configuration for kneading elements. Periodic simulations yielded similar results to a full-scale simulation at significantly reduced computational cost. If the level of cohesion in the contact model is calibrated using laboratory testing, valid results can be obtained without fluid coupling. Friction between granules and the internal surfaces of the granulator is a very influential parameter because the response of this system is dominated by interactions with the geometry

Defect cluster recognition system for fabricated semiconductor wafers

The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry's needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type)