Precipitation induced densification in a sintered Al-Zn-Mg-Cu alloy

It is demonstrated that slow cooling to 200 degrees C from a high sintering temperature (620 degrees C) reduces porosity in an Al-8Zn-2.5Mg-1Cu powder compact when compared to isothermal sintering at the higher temperature for a longer time. The reduction in porosity is attributed to shrinkage associated with removal of solute from the aluminium solid solution and heterogeneous precipitation of the eta phase (MgZn2), particularly onto pore surfaces. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Precipitation and Fire Effects on Flowering of a Rare Prairie Orchid

A small, isolated population of the threatened western prairie fringed orchid (Platanthera praeclara Sheviak & Bowles) occurs at Pipestone National Monument, Minnesota, in a mesic prairie that is periodically burned to control invasive cool-season grasses. During 1995-2004, monitoring counts of flowering orchids in the monument varied considerably for different years. Similar precipitation amounts in the spring and histories of burning suggest that fire and precipitation in the spring were not the causes of the variation. For the eight non-burn years in the monitoring record, we compared the number of flowering plants and the precipitation amounts during six growth stages of the orchid and found a 2-variab1e model (precipitation during senescence/bud development and precipitation in the dormant period) explained 77% of the annual variation in number of flowering plants. We also conducted a fire experiment in early May 2002, the typical prescribed burn period for the monument, and found that the frequency of flowering, vegetative, and absent plants observed in July did not differ between burned and protected locations of orchids. We used the model and forecasts of precipitation in the spring to develop provisional burn decision scenarios. We discussed management implications of the scenarios.

Particle formation during soy protein precipitation

The current success of soy foods is driving soy ingredient manufacturers to develop innovative products for food manufacturers. One such innovation is separating the soy proteins glycinin and beta-conglycinin to take advantage of their individual functional and nutritional properties. Precipitation by acidification is a low-cost method for separating these two proteins. Separation is achieved by preferentially precipitating glycinin at pH ~ 6 while beta-conglycinin remains in solution. Understanding the particle formation during protein precipitation is important as it can influence the efficiency of the precipitation process as well as subsequent downstream processes such as the particle-liquid separation step, usually achieved by centrifugation. Most of the previous soy protein precipitation studies are limited to precipitation at pH 4 as this is the pH range most commonly used in the commercial manufacturing of soy protein isolates. To date, there have been no studies on the particle formation during precipitation at pH > 5.Precipitation of soy protein is generally thought to occur by the rapid formation of primary particles in the size range of 0.1 - 0.3 microns followed by aggregation of these particles via collision to aggregates of size about 1 - 50 microns. The formation of the primary particles occurs on a time scale much shorter than that of the overall precipitation process (Nelson and Glatz, 1985). This study shows that precipitation of soy protein is indeed rapid. At high pH levels, binary liquid-liquid separation occurs forming a protein-rich heavy phase. The protein-rich phase appears as droplets which can be coalesced to form a uniform bulk layer under centrifugation forces. Upon lowering the pH level by the addition of acid, further protein is precipitated as amorphous material which binds the droplets together to form aggregates of amorphous precipitates. Liquid-liquid separation has been observed in many protein solutions but this phenomenon has only scarcely been reported in the literature for soy proteins. It presents an exciting opportunity for an innovative product. Features of the liquid-phase protein such as protein yield and purity will be characterized.

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed

A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting

The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. © 2004 Elsevier B.V. All rights reserved.

The effects of metallurgical variables on the mechanical properties of high-chromium cast irons

The.use of high-chromium cast irons for abrasive wear resistance is restricted due to their poor fracture toughness properties. An.attempt was made to improve the fracture characteristics by altering the distribution, size and.shape of the eutectic carbide phase without sacrificing their excellent wear resistance. This was achieved by additions of molybdenum or tungsten followed by high temperature heat treatments. The absence of these alloying elements or replacement of them with vanadium or manganese did not show any significant effect and the continuous eutectic carbide morphology remained the same after application of high temperature heat treatments. The fracture characteristics of the alloys with these metallurgical variables were evaluated for both sharp-cracks and blunt notches. The results were used in conjunction with metallographic and fractographic observations to establish possible failure mechanisms. The fracture mechanism of the austenitic alloys was found to be controlled not only by the volume percent but was also greatly influenced by the size and distribution of the eutectic carbides. On the other hand, the fracture mechanism of martensitic alloys was independent of the eutectic carbide morphology. The uniformity of the secondary carbide precipitation during hardening heat treatments was shown to be a reason for consistant fracture toughness results being obtained with this series of alloys although their eutectic carbide morphologies were different. The collected data were applied to a model which incorporated the microstructural parameters and correlated them with the experimentally obtained valid stress intensity factors. The stress intensity coefficients of different short-bar fracture toughness test specimens were evaluated from analytical and experimental compliance studies. The.validity and applicability of this non-standard testing technique for determination of the fracture toughness of high-chromium cast irons were investigated. The results obtained correlated well with the valid results obtained from standard fracture toughness tests.