Recent development in the mechanics of superplasticity and its applications

This paper reviews the recent developments in the mechanics of superplasticity and its applications in industrial practice. After introducing the phenomena of superplasticity, the basic experiments for determining material deformation behavior and related parameters, and constructing superplastic constitutive equations, are reviewed. Finite element related formulations and techniques for simulating superplastic forming are discussed, together with some practical applications. (C) 2004 Elsevier B.V. All rights reserved.

Edge-to-edge matching and its applications - Part I. Application to the simple HCP/BCC system

The edge-to-edge matching crystallographic model has been used to predict all the orientation relationships (OR) between crystals that have simple hexagonal close packed (HCP) and body-centered cubic (BCC) structures. Using the critical values for the interatomic spacing misfit along the matching directions and the cl-value mismatch between matching planes, the model predicted all the four common ORs, namely the Burgers OR, the Potter OR, the Pitsch-Schrader OR and the Rong Dunlop OR, together with the corresponding habit planes. Taking the c(H)/a(H) and a(H)/a(B) ratios as variables, where H and B denote the HCP and BCC structures respectively, the model also predicted the relationship between these variables and the four ORs. These predictions are perfectly consistent with the published experimental results. As was the case in the FCC/BCC system, the edge-to-edge matching model has been shown to be a powerful tool for predicting the crystallographic features of diffusion-controlled phase transformations. (C) 2004 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Edge-to-edge matching and its applications - Part II. Application to Mg-Al, Mg-Y and Mg-Mn alloys

A model for the crystallography and morphology of diffusion-controlled phase transformations - edge-to-edge matching - has been used to predict the orientation relationships (OR) and habit planes of precipitates Mg17Al12 in Mg-Al alloy, Mg24Y5 in Mg-Y alloy and alpha-Mn in Mg-Mn alloy. Based on the crystal structures and lattice parameters only, the model predicts that the possible ORs between Mg17Al12 and Mg matrix are the near Burgers OR, the Potter OR, the Gjonnes-Ostmoe OR and the Crawley OR. In the Mg-Y alloy, the OR between Mg24Y5 precipitates and the Mg matrix is predicted to be the Burgers OR only. The model also predicts that there are no reproducible ORs between alpha-Mn and Mg in the Mg-Mn alloy. Combining the edge-to-edge matching model and W. Zhang's Deltag approach, the habit plane and side facets of the precipitate for each OR can be determined. All the predicted ORs and the corresponding habit planes in Mg-Al and Mg-Y alloys agree very well with the experimental results. (C) 2004 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Comparative evaluation of microscopic car-following behavior

Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport, systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a traffic-simulation system depends highly on the quality of the traffic-flow model at its core, with the two main critical components being the car-following and lane-changing models. This paper presents findings from a comparative evaluation of car-following behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Statiten-simulation (VISSIM)]. The car-following algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a car-following experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a one-lane road. The experiment was replicated in each tool and the simulated car-following behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gipps-based models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative drift and goal-seeking behavior test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings.

Particle tracking in a salinity gradient: A method for measuring sinking rate of individual phytoplankton in the laboratory

This paper presents a new method to measure the sinking rates of individual phytoplankton “particles” (cells, chains, colonies, and aggregates) in the laboratory. Conventional particle tracking and high resolution video imaging were used to measure particle sinking rates and particle size. The stabilizing force of a very mild linear salinity gradient (1 ppt over 15 cm) prevented the formation of convection currents in the laboratory settling chamber. Whereas bulk settling methods such as SETCOL provide a single value of sinking rate for a population, this method allows the measurement of sinking rate and particle size for a large number of individual particles or phytoplankton within a population. The method has applications where sinking rates vary within a population, or where sinking rate-size relationships are important. Preliminary data from experiments with both laboratory and field samples of marine phytoplankton are presented here to illustrate the use of the technique, its applications, and limitations. Whereas this paper deals only with sinking phytoplankton, the method is equally valid for positively buoyant species, as well as nonbiological particles.