An estimation of friction from the indentation of metals by acute-angled conical indenters

The paper describes a method for estimating the coefficient of friction (COF) between sliding surfaces by an indentation of an acute-angled indenter. The COF estimated by the method is compared with the COF measured by a friction tester. The conical indenter, which is made of WC and has an apex angle of 45°, is used for the experiments. A micro-Vickers hardness tester is used for the indentation tests. The friction tester used is one of Bowden–Leben type. A copper and a 0.45% carbon steel are used for the indentation and sliding specimens. The estimation of the COF is based on the equilibrium equation of indentation, which takes the friction between the contacting surfaces of the indenter and the specimen into consideration. The results show that the estimated COF is approximately equal to the measured one. Based on the results, the validity of this method is discussed.     

Evidence for enhanced thermal conduction through percolating structures in nanofluids

The unusually large enhancement of thermal conductivity (k/k(f)～4.0, where k and k(f) are the thermal conductivities of the nanofluid and the base fluid, respectively) observed in a nanofluid containing linear chain-like aggregates provides direct evidence for efficient transport of heat through percolating paths. The nanofluid used was a stable colloidal suspension of magnetite (Fe(3)O(4)) nanoparticles of average diameter 6.7?nm, coated with oleic acid and dispersed in kerosene. The maximum enhancement under magnetic field was about 48φ (where φ is the volume fraction). The maximum enhancement is observed when chain-like aggregates are uniformly dispersed without clumping. These results also suggest that nanofluids containing well-dispersed nanoparticles (without aggregates) do not exhibit significant enhancement of thermal conductivity. Our findings offer promising applications for developing a new generation of nanofluids with tunable thermal conductivity.     

A Study of Polymer Electrolyte Fuel Cells by the Measurement of AC Impedance,Current Interrupt,and Dew Points: 2. Effect of Cell Temperature

The effect of cell temperature on the performance of a polymer electrolyte fuel cell was examined in the present study. Measurements using the current interrupt and AC impedance methods showed that membrane resistance increased as the cell temperature was reduced. The charge transfer resistance, determined by the AC impedance method, also increased with decreasing cell temperature. The results of electrochemical analysis showed that the temperature of the cell strongly affected the performance of the membrane–electrode assembly in the cell. In addition, the water balance calculated from dew points of fuel gases changed with cell temperature. At a cell temperature of 80 °C, ca. 80% of the water generated on the cathode passed through the membrane to the anode, while at a cell temperature of 40 °C, only ca. 20% of the water on the cathode passed through the membrane to the anode.     

Another approach to asymptotic model matching problem for nonlinear systems

This paper deals with the asymptotic model matching problem for square nonlinear systems. This problem has been studied by Di Benedetto and Grizzle via geometric approach. We adopt another approach. It is based on an idea that this problem is similar to the nonlinear servomechanism (output regulation) problem. By means of slight modification of the problem statement, a sufficient condition is given. It is described as the solvability of a certain partial differential equation. Its reduction and approximation methods are proposed. © 1998 John Wiley & Sons, Ltd.     

Molecular Dynamics Study of Radial Corrugation in Carbon Nanotubes

Molecular dynamics simulations of multiwalled carbon nanotubes under hydrostatic pressure are performed to elucidate the novel class of radial buckling in the systems. It is revealed by all-atom simulations that the initial circular cross section transforms into a flower-like wavy configuration at critical pressure on the order of hundreds mega pascals or less. This kind of radial buckling, called radial corrugation, originates from the competition of the three relevant energies in the system: in-plane strain energy, van der Waals interaction energy between adjacent tubes, and out-of-plane bending energy. Their possible consequences for physical properties of carbon nanotubes are also discussed.     

Design of inferential sensors in the process industry: A review of Bayesian methods

In many industrial plants, development and implementation of advanced monitoring and control techniques require real-time measurement of process quality variables. However, on-line acquisition of such data may involve difficulties due to inadequacy of measurement techniques or low reliability of measuring devices. To overcome the shortcomings of traditional instrumentation, inferential sensors have been designed to infer process quality indicators from real-time measurable process variables. In recent years, due to the demonstrated advantages of Bayesian methods, interest in investigating the application of these methods for design of inferential sensors has grown. However, the potential of Bayesian methods for inferential modeling practices in the process industry has not yet been fully realized. This paper provides a general introduction to the main steps involved in development and implementation of industrial inferential sensors, and presents an overview of the relevant Bayesian methods for inferential modeling.     

Adaptive enhancement and binarization techniques for degraded plate images

Multimedia Tools and Applications - Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying computer techniques in Intelligent Transport Systems (ITS). They face...     

Densification behaviour of ceramic powder

A three-dimensional compaction device has been developed to carry out compaction of a ceramic powder. The details of the device, which provides compaction with various stress ratios, are described. A densification criterion for the powder is proposed; this is similar to that for the case for metal powders, but the role of the hydrostatic stress component appears to be different. Stress-strain rate relations are then derived using the concept of plastic potential; experimental results show that the normality of the strain rate vector to the surface which corresponds to the criterion almost holds.     

A predictive controller based on dynamic matrix control for a non-minimum phase robot manipulator

In this paper a hybrid control strategy is presented based on Dynamic Matrix Control (DMC) and feedback linearization methods for designing a predictive controller of five bar linkage manipulator as a MIMO system (two inputs and two outputs). Analyzing the internal dynamic of robot shows the open loop system is unstable and non-minimum phase, so in order to apply the predictive controller, special modifications are needed. These modifications on non-minimum phase behavior are performed using feedback linearization procedure based on state space realization. The design objective is to track a desirable set point as well as time varying trajectories as a command references with globally asymptotical stabilization. The proposed controller is applied to nonlinear fully coupled model of the typical five bar linkage manipulator with non-minimum phase behavior. Simulation results show that the proposed controller has good efficiency. The step responses of system with and without feedback linearization process illustrated that the mentioned modification for stabilizing is performed properly. After applying the proposed predictive controller, the joint angle of robot tracks the reference input while another input acts as the disturbance and vice versa.     

A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry

In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit.