Improved damping capacity and machinability of graphite particle-aluminum alloy composites

The damping capacity of cast graphitic aluminum alloy composites has been measured using a torsion pendulum at a constant strain amplitude. It was found that flake-graphite particles dispersed in the matrix of aluminum alloys increased the damping capacity; the improvement was greater, the higher the amount of graphite dispersed in the matrix. At sufficiently high graphite contents the damping capacity of graphitic aluminum composites approaches that of cast iron. The ratio between the damping capacity and the density of graphitic aluminum alloys is higher than cast iron, making them very attractive as light-weight, high-damping materials for possible aircraft applications. Machinability tests on graphite particle-aluminum composites, conducted at speeds of 315 sfm and 525 sfm, showed that the chip length decreased with the amount of graphite of a given size. When the size of graphite was decreased, at a given machining speed, the chip length decreased. Metallographic examination shows that graphite particles act as chip breakers, and are frequently sheared parallel to the plane of the

Nature of trapped particle instability

The amplification mechanism for the side bands which accompany a large amplitude electron wave on a plasma column are shown to arise due to two mode interaction between negative and positive energy waves.

Production of cast aluminium-graphite particle composites using a pellet method

Copper- and nickel-coated graphite particles can be successfully introduced into aluminium-base alloy melts as pellets to produce cast aluminium-graphite particle composites. The pellets were made by pressing mixtures of nickel- or copper-coated graphite particles and aluminium powders together at pressures varying between 2 and 20 kg mm–2. These pellets were dispersed in aluminium alloy melts by plunging and holding them in the melts using a refractory coated mild steel cone, until the pellets disintegrated and the powders were dispersed. The optimum pressure for the preparation of pellets was 2 to 5 kg mm–2 and the optimum size and percentage of aluminium powder were 400 to 1000mgrm and 35 wt% respectively. Under optimum conditions the recovery of the graphite particles in the castings was as high as 96%, these particles being pushed into the last freezing interdendritic regions. The tensile strength and the hardness of the graphite aluminium alloys made using the pellet method are comparable to those of similar composites made using gas injection or the vortex method. The pellet method however has the advantage of greater reproducibility and flexibility. Dispersion of graphite particles in the matrix of cast aluminium alloys using the pellet method increases their resistance to wear.

The effect of particle size distribution on TG

The effect of a particle size distribution on the fractional reaction has been analysed. The analysis shows that for non-isothermal TG the activation energy and frequency factor evaluated from the fractional reaction by conventional method depend on the particle size distribution, and this may lead to a kinetic compensating effect. Particle size distribution may also lead to an erroneous conclusion about the change in the mechanism of reaction.

A semi-analytical particle filter for identification of nonlinear oscillators

Particle filters find important applications in the problems of state and parameter estimations of dynamical systems of engineering interest. Since a typical filtering algorithm involves Monte Carlo simulations of the process equations, sample variance of the estimator is inversely proportional to the number of particles. The sample variance may be reduced if one uses a Rao-Blackwell marginalization of states and performs analytical computations as much as possible. In this work, we propose a semi-analytical particle filter, requiring no Rao-Blackwell marginalization, for state and parameter estimations of nonlinear dynamical systems with additively Gaussian process/observation noises. Through local linearizations of the nonlinear drift fields in the process/observation equations via explicit Ito-Taylor expansions, the given nonlinear system is transformed into an ensemble of locally linearized systems. Using the most recent observation, conditionally Gaussian posterior density functions of the linearized systems are analytically obtained through the Kalman filter. This information is further exploited within the particle filter algorithm for obtaining samples from the optimal posterior density of the states. The potential of the method in state/parameter estimations is demonstrated through numerical illustrations for a few nonlinear oscillators. The proposed filter is found to yield estimates with reduced sample variance and improved accuracy vis-a-vis results from a form of sequential importance sampling filter.

Mechanics of Deformation under Traction and Friction of a Micrometric Monolithic MoS2 Particle in Comparison with those of an Agglomerate of Nanometric MoS2 Particles

Tribology of small inorganic nanoparticles in suspension in a liquid lubricant is often impaired because these particles agglomerate even when organic dispersants are used. In this paper we use lateral force microscopy to study the deformation mechanism and dissipation under traction of two extreme configurations (1) a large MoS2 particle (similar to 20 mu m width) of about 1 mu m height and (2) an agglomerate (similar to 20 mu m width), constituting 50 nm MoS2 crystallites, of about 1 mu m height. The agglomerate records a friction coefficient which is about 5-7 times that of monolithic particle. The paper examines the mechanisms of material removal for both the particles using continuum modeling and microscopy and infers that while the agglomerate response to traction can be accounted for by the bulk mechanical properties of the material, intralayer and interlayer basal planar slips determine the friction and wear of monolithic particles. The results provide a rationale for selection of layered particles, for suspension in liquid lubricants.

Accurate Parameter Estimation of Contaminant Transport Inverse Problem using Particle Swarm Optimization

Swarm Intelligence techniques such as particle swarm optimization (PSO) are shown to be incompetent for an accurate estimation of global solutions in several engineering applications. This problem is more severe in case of inverse optimization problems where fitness calculations are computationally expensive. In this work, a novel strategy is introduced to alleviate this problem. The proposed inverse model based on modified particle swarm optimization algorithm is applied for a contaminant transport inverse model. The inverse models based on standard-PSO and proposed-PSO are validated to estimate the accuracy of the models. The proposed model is shown to be out performing the standard one in terms of accuracy in parameter estimation. The preliminary results obtained using the proposed model is presented in this work.

Oxide Particle Surface Chemistry and Ion Transport in "Soggy Sand" Electrolytes

The crucial role of oxide surface chemical composition on ion transport in "soggy sand" electrolytes is discussed in a systematic manner. A prototype soggy sand electrolytic system comprising aerosil silica functionalized with various hydrophilic and hydrophobic moieties dispersed in lithium perchlorate-ethylene glycol solution was used for the study. Detailed rheology studies show that the attractive particle network in the case of the composite with unmodified aerosil silica (with surface silanol groups) is most favorable for percolation in ionic conductivity, as well as rendering the composite with beneficial elastic mechanical properties: Though weaker in strength compared to the composite with unmodified aerosil particles, attractive particle networks are also observed in composites of aerosil particles with surfaces partially substituted with hydrophobic groups. The percolation in ionic conductivity is, however, dependent on the size of the hydrophobic moiety. No spanning attractive particle network was formed for aerosil particles with surfaces modified with stronger hydrophilic groups (than silanol), and as a result, no percolation in ionic conductivity was observed. The composite with hydrophilic particles was a sol, contrary to gels obtained in the case of unmodified aerosil, and partially substituted with hydrophobic groups.

Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

Nonlinear structural dynamical system identification using adaptive particle filters

The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibration data, are presented to reveal the correct functioning of the method. (c) 2007 Elsevier Ltd. All rights reserved.

Studies on Aerosol Particle Emission, Sampling and Authenticity

Emissions of coal combustion fly ash through real scale ElectroStatic Precipitators (ESP) were studied in different coal combustion and operation conditions. Sub-micron fly-ash aerosol emission from a power plant boiler and the ESP were determined and consequently the aerosol penetration, as based on electrical mobility measurements, thus giving thereby an indication for an estimate on the size and the maximum extent that the small particles can escape. The experimentals indicate a maximum penetration of 4% to 20 % of the small particles, as counted on number basis instead of the normally used mass basis, while simultaneously the ESP is operating at a nearly 100% collection efficiency on mass basis. Although the size range as such seems to appear independent of the coal, of the boiler or even of the device used for the emission control, the maximum penetration level on the number basis depends on the ESP operating parameters. The measured emissions were stable during stable boiler operation for a fired coal, and the emissions seemed each to be different indicating that the sub-micron size distribution of the fly-ash could be used as a specific characteristics for recognition, for instance for authenticity, provided with an indication of known stable operation. Consequently, the results on the emissions suggest an optimum particle size range for environmental monitoring in respect to the probability of finding traces from the samples. The current work embodies also an authentication system for aerosol samples for post-inspection from any macroscopic sample piece. The system can comprise newly introduced new devices, for mutually independent use, or, for use in a combination with each other, as arranged in order to promote the sampling operation length and/or the tag selection diversity. The tag for the samples can be based on naturally occurring measures and/or added measures of authenticity in a suitable combination. The method involves not only military related applications but those in civil industries as well. Alternatively to the samples, the system can be applied to ink for note printing or other monetary valued papers, but also in a filter manufacturing for marking fibrous filters.

Understanding the chemistry of atmospheric aerosol particle formation via observations of physical properties

It is widely accepted that the global climate is heating up due to human activities, such as burning of fossil fuels. Therefore we find ourselves forced to make decisions on what measures, if any, need to be taken to decrease our warming effect on the planet before any irrevocable damage occurs. Research is being conducted in a variety of fields to better understand all relevant processes governing Earth s climate, and to assess the relative roles of anthropogenic and biogenic emissions into the atmosphere. One of the least well quantified problems is the impact of small aerosol particles (both of anthropogenic and biogenic origin) on climate, through reflecting solar radiation and their ability to act as condensation nuclei for cloud droplets. In this thesis, the compounds driving the biogenic formation of new particles in the atmosphere have been examined through detailed measurements. As directly measuring the composition of these newly formed particles is extremely difficult, the approach was to indirectly study their different characteristics by measuring the hygroscopicity (water uptake) and volatility (evaporation) of particles between 10 and 50 nm. To study the first steps of the formation process in the sub-3 nm range, the nucleation of gaseous precursors to small clusters, the chemical composition of ambient naturally charged ions were measured. The ion measurements were performed with a newly developed mass spectrometer, which was first characterized in the laboratory before being deployed at a boreal forest measurement site. It was also successfully compared to similar, low-resolution instruments. The ambient measurements showed that sulfuric acid clusters dominate the negative ion spectrum during new particle formation events. Sulfuric acid/ammonia clusters were detected in ambient air for the first time in this work. Even though sulfuric acid is believed to be the most important gas phase precursor driving the initial cluster formation, measurements of the hygroscopicity and volatility of growing 10-50 nm particles in Hyytiälä showed an increasing role of organic vapors of a variety of oxidation levels. This work has provided additional insights into the compounds participating both in the initial formation and subsequent growth of atmospheric new aerosol particles. It will hopefully prove an important step in understanding atmospheric gas-to-particle conversion, which, by influencing cloud properties, can have important climate impacts. All available knowledge needs to be constantly updated, summarized, and brought to the attention of our decision-makers. Only by increasing our understanding of all the relevant processes can we build reliable models to predict the long-term effects of decisions made today.

Experiments on homogeneous nucleation and physicochemical properties related to atmospheric new particle formation

Aerosol particles play a role in the earth ecosystem and affect human health. A significant pathway of producing aerosol particles in the atmosphere is new particle formation, where condensable vapours nucleate and these newly formed clusters grow by condensation and coagulation. However, this phenomenon is still not fully understood. This thesis brings an insight to new particle formation from an experimental point of view. Laboratory experiments were conducted both on the nucleation process and physicochemical properties related to new particle formation. Nucleation rate measurements are used to test nucleation theories. These theories, in turn, are used to predict nucleation rates in atmospheric conditions. However, the nucleation rate measurements have proven quite difficult to conduct, as different devices can yield nucleation rates with differences of several orders of magnitude for the same substances. In this thesis, work has been done to have a greater understanding in nucleation measurements, especially those conducted in a laminar flow diffusion chamber. Systematic studies of nucleation were also made for future verification of nucleation theories. Surface tensions and densities of substances related to atmospheric new particle formation were measured. Ternary sulphuric acid + ammonia + water is a proposed candidate to participate in atmospheric nucleation. Surface tensions of an alternative candidate to nucleate in boreal forest areas, sulphuric acid + dimethylamine + water, were also measured. Binary compounds, consisting of organic acids + water are possible candidates to participate in the early growth of freshly nucleated particles. All the measured surface tensions and densities were fitted with equations, thermodynamically consistent if possible, to be easily applied to atmospheric model calculations of nucleation and subsequent evolution of particle size.