Design guidelines for granular particles in a conical centrifugal filter

Essential design criteria for successful drying of granular particles in a conical continuous centrifugal filter are developed in a dimensionless fashion. Four criteria are considered: minimum flow thickness (to ensure sliding bulk flow rather than particulate flow), desaturation position, output dryness and basket failure. The criteria are based on idealised physical models of the machine operation and are written explicitly as functions of the basket size lout, spin velocity Ω and input flow rate of powder Qp. The separation of sucrose crystals from liquid molasses is taken as a case study and the successful regime of potential operating points (lout, Ω) is plotted for a wide range of selected values of flow rate Qp. Analytical expressions are given for minimum and maximum values of the three independent parameters (lout, Ω, Qp) as a function of the slurry and basket properties. The viable operating regime for a conical centrifugal filter is thereby obtained as a function of the slurry and basket properties. © 2012 The Institution of Chemical Engineers.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.

A Grassmann-Rayleigh quotient iteration for computing invariant subspaces

The classical Rayleigh quotient iteration (RQI) allows one to compute a one-dimensional invariant subspace of a symmetric matrix A. Here we propose a generalization of the RQI which computes a p-dimensional invariant subspace of A. Cubic convergence is preserved and the cost per iteration is low compared to other methods proposed in the literature.

A Grassman-Rayleigh quotient iteration for computing invariant subspaces

The classical Rayleigh Quotient Iteration (RQI) computes a 1-dimensional invariant subspace of a symmetric matrix A with cubic convergence. We propose a generalization of the RQI which computes a p-dimensional invariant subspace of A. The geometry of the algorithm on the Grassmann manifold Gr(p,n) is developed to show cubic convergence and to draw connections with recently proposed Newton algorithms on Riemannian manifolds.

Relating monolithic and granular leaching from contaminated soil treated with different cementitious binders.

This work employed a clayey, silty, sandy gravel contaminated with a mixture of metals (Cd, Cu, Pb, Ni and Zn) and diesel. The contaminated soil was treated with 5 and 10% dosages of different cementitious binders. The binders include Portland cement, cement-fly ash, cement-slag and lime-slag mixtures. Monolithic leaching from the treated soils was evaluated over a 64-day period alongside granular leachability of 49- and 84-day old samples. Surface wash-off was the predominant leaching mechanism for monolithic samples. In this condition, with data from different binders and curing ages combined, granular leachability as a function of monolithic leaching generally followed degrees 4 and 6 polynomial functions. The only exception was for Cu, which followed the multistage dose-response model. The relationship between both leaching tests varied with the type of metal, curing age/residence time of monolithic samples in the leachant, and binder formulation. The results provide useful design information on the relationship between leachability of metals from monolithic forms of S/S treated soils and the ultimate leachability in the eventual breakdown of the stabilized/solidified soil.

Multi-scale modelling of granular avalanches

Avalanches, debris flows, and landslides are geophysical hazards, which involve rapid mass movement of granular solids, water and air as a single-phase system. The dynamics of a granular flow involve at least three distinct scales: the micro-scale, meso-scale, and the macro-scale. This study aims to understand the ability of continuum models to capture the micro-mechanics of dry granular collapse. Material Point Method (MPM), a hybrid Lagrangian and Eulerian approach, with Mohr-Coulomb failure criterion is used to describe the continuum behaviour of granular column collapse, while the micromechanics is captured using Discrete Element Method (DEM) with tangential contact force model. The run-out profile predicted by the continuum simulations matches with DEM simulations for columns with small aspect ratios ('h/r' < 2), however MPM predicts larger run-out distances for columns with higher aspect ratios ('h/r' > 2). Energy evolution studies in DEM simulations reveal higher collisional dissipation in the initial free-fall regime for tall columns. The lack of a collisional energy dissipation mechanism in MPM simulations results in larger run-out distances. Micro-structural effects, such as shear band formations, were observed both in DEM and MPM simulations. A sliding flow regime is observed above the distinct passive zone at the core of the column. Velocity profiles obtained from both the scales are compared to understand the reason for a slow flow run-out mobilization in MPM simulations. © 2013 AIP Publishing LLC.

UbiDM: A new paradigm for product design and manufacturing via ubiquitous computing technology

Since ubiquitous technology was introduced in the early 1980s, it has rapidly developed, and been applied to various domains mainly for the improvement of human life. In this article, the authors propose that ubiquitous computing technology can be effectively used for the design and manufacturing of a product by proposing a new paradigm, called UbiDM (Design and Manufacture via Ubiquitous Computing Technology). The key aspect of UbiDM is the utilisation of the entire product lifecycle information obtained via ubiquitous computing technology for the design and manufacture of the product. The new paradigm can solve many of the problems that have not been properly handled by previous manufacturing paradigms. Specifically, it will address the concept of UbiDM by the following aspects: (1) why there is a need for UbiDM; (2) the essence of UbiDM; (3) enabling technologies; (4) application area; (5) worldwide RD status; and (6) the societal impacts of UbiDM.