Particle dynamics and heat transfer at workpiece surface in heat treatment fluidised beds

The particle behaviour in a heat treatment fluidised bed was studied by the analysis of particle images taken with a high speed CCD digital video camera. The comparison of particle dynamics was performed for the fluidised beds without part, with single part and with multi-parts. The results show that there are significant differences in particle behaviours both in different beds and at different locations of part surfaces. The total and radiative heat transfer coefficients at different surfaces of a metallic part in a fluidised bed were measured by a heat transfer probe developed in the present work. The structure of the probe was optimized with numerical simulation of energy conservation for measuring the heat transfer coefficient of 150-600 W/m2K. The relationship between the particle dynamics and the heat transfer was analysed to form the basis for future more rational designs of fluidised beds as well as for improved quality control.

CHARGED PARTICLE DYNAMICS IN TIME DEPENDENT MAGNETIC FIELDS

In this work we study the behavior of charged particles immersed in a peculiar configuration of magnetic fields, which has a main constant field B(0) and a superimposed, transversal perturbation field B(1) sin(omega(p)t), with B(1) << B(0). By taking Cartesian coordinates and placing B(0) along the z axis and B(1) sin (omega(p)t) on the x axis, an analytical solution for y(t) may be obtained by solving an integrodifferential equation. Besides, the solution z(t) also exhibits a very interesting dynamics, and the entire system is conditioned by resonances between the particle orbit frequencies and the frequency of the magnetic transversal perturbation, omega(p). In this work we also discuss numerical simulations for the related particle trajectories, as well as potential applications in the context of separation phenomena.

Relativistic particle dynamics in D=2+1

We propose a SUSY variant of the action for a massless spinning particles via the inclusion of twistor variables. The action is constructed to be invariant under SUSY transformations and tau-reparametrizations even when an interaction field is including. The constraint analysis is achieved and the equations of motion are derived. The commutation relations obtained for the commuting spinor variables lambda(alpha) show that the particle states have fractional statistics and spin. At once we introduce a possible massive term for the non-interacting model.

Relativistic particle dynamics in D = 2+1

We propose a SUSY variant of the action for a massless spinning particles via the inclusion of twistor variables. The action is constructed to be invariant under SUSY transformations and τ-reparametrizations even when an interaction field is including. The constraint analysis is achieved and the equations of motion are derived. The commutation relations obtained for the commuting spinor variables λα show that the particle states have fractional statistics and spin. At once we introduce a possible massive term for the non-interacting model. © SISSA 2006.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Convergence results for stochastic particle systems with social interaction

We consider stochastic individual-based models for social behaviour of groups of animals. In these models the trajectory of each animal is given by a stochastic differential equation with interaction. The social interaction is contained in the drift term of the SDE. We consider a global aggregation force and a short-range repulsion force. The repulsion range and strength gets rescaled with the number of animals N. We show that for N tending to infinity stochastic fluctuations disappear and a smoothed version of the empirical process converges uniformly towards the solution of a nonlinear, nonlocal partial differential equation of advection-reaction-diffusion type. The rescaling of the repulsion in the individual-based model implies that the corresponding term in the limit equation is local while the aggregation term is non-local. Moreover, we discuss the effect of a predator on the system and derive an analogous convergence result. The predator acts as an repulsive force. Different laws of motion for the predator are considered.

Foundations of stochastic geometry and theory of random sets

The first section of this chapter starts with the Buffon problem, which is one of the oldest in stochastic geometry, and then continues with the definition of measures on the space of lines. The second section defines random closed sets and related measurability issues, explains how to characterize distributions of random closed sets by means of capacity functionals and introduces the concept of a selection. Based on this concept, the third section starts with the definition of the expectation and proves its convexifying effect that is related to the Lyapunov theorem for ranges of vector-valued measures. Finally, the strong law of large numbers for Minkowski sums of random sets is proved and the corresponding limit theorem is formulated. The chapter is concluded by a discussion of the union-scheme for random closed sets and a characterization of the corresponding stable laws.

Gas and particle dynamics of a contoured shock tube for pre-clinical microparticle drug delivery

We investigate the gas-particle dynamics of a device designed for biological pre-clinical experiments. The device uses transonic/supersonic gas flow to accelerate microparticles such that they penetrate the outer skin layers. By using a shock tube coupled to a correctly expanded nozzle, a quasi-one-dimensional, quasi-steady flow (QSF) is produced to uniformly accelerate the microparticles. The system utilises a microparticle cassette (a diaphragm sealed container) that incorporates a jet mixing mechanism to stir the particles prior to diaphragm rupture. Pressure measurements reveal that a QSF exit period - suitable for uniformly accelerating microparticles - exists between 155 and 220 mus after diaphragm rupture. Immediately preceding the QSF period, a starting process secondary shock was shown to form with its (x,t) trajectory comparing well to theoretical estimates. To characterise the microparticle, flow particle image velocimetry experiments were conducted at the nozzle exit, using particle payloads with varying diameter (2.7-48 mu m), density (600-16,800 kg/m(3)) and mass (0.25-10 mg). The resultant microparticle velocities were temporally uniform. The experiments also show that the starting process does not significantly influence the microparticle nozzle exit velocities. The velocity distribution across the nozzle exit was also uniform for the majority of microparticle types tested. For payload masses typically used in pre-clinical drug and vaccine applications (

Stable algorithm for event detection in event-driven particle dynamics : logical states

Acknowledgments The authors gratefully acknowledge the support of the German Research Foundation (DFG) through the Cluster of Excellence ‘Engineering of Advanced Materials’ at the University of Erlangen-Nuremberg and through Grant Po 472/25.

Particle dynamics and airflow patterns in a ventilated two-zone enclosure

Understanding the transport mechanisms of aerosol particles in enclosures has broad ramifications in the context of cleaning strategies, and health risk assessment (e. g., occupational exposure). This paper addresses airflow pattern and aerosol transport mechanism in a ventilated two-zone enclosure with the outlet (exhaust location) situated at different locations. A numerical approach that combines a Eulerian simulation of turbulent flow with a Lagrangian particle-tracking algorithm is used. Simulations are carried out using solid suspensions with different sizes (1 to 100 micron) and densities (240 and 2300 kg/m3). The effect of location of the outlet (exhaust) on airflow patterns and aerosol dynamics is analyzed and quantified.

Nonlinear Dynamics of Beams with Stochastic Parameter Variations

The aim of this paper is to investigate the steady state response of beams under the action of random support motions. The study is of relevance in the context of earthquake response of extended land based structures such as pipelines and long span bridges, and, secondary systems such as piping networks in nuclear power plant installations. The following complicating features are accounted for in the response analysis: (a) differential support motions: this is characterized in terms of cross power spectral density functions associated with distinct support motions, (b) nonlinear support conditions, and (c) stochastically inhomogeneous stiffness and mass variations of the beam structure; questions on non-Gaussian models for these variations are considered. The method of stochastic finite elements is combined with equivalent linearization technique and Monte Carlo simulations to obtain response moments.

Dynamics and transport properties of Kondo insulators

A many-body theory of paramagnetic Kondo insulators is described, focusing specifically on single-particle dynamics, scattering rates, dc transport and optical conductivities. This is achieved by development of a non-perturbative local moment approach to the symmetric periodic Anderson model within the framework of dynamical mean-field theory. Our natural focus is the strong-coupling, Kondo lattice regime, in particular the resultant 'universal' scaling behaviour in terms of the single, exponentially small low-energy scale characteristic of the problem. Dynamics/transport on all relevant (ω, T)-scales are considered, from the gapped/activated behaviour characteristic of the low-temperature insulator through to explicit connection to single-impurity physics at high ω and/or T; and for optical conductivities emphasis is given to the nature of the optical gap, the temperature scale responsible for its destruction and the consequent clear distinction between indirect and direct gap scales. Using scaling, explicit comparison is also made to experimental results for dc transport and optical conductivities of Ce3Bi4Pt3, SmB6 and YbB12. Good agreement is found, even quantitatively; and a mutually consistent picture of transport and optics results.