257,613 research outputs found

    Multifluid, Magnetohydrodynamic Shock Waves with Grain Dynamics II. Dust and the Critical Speed for C Shocks

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    This is the second in a series of papers on the effects of dust on multifluid, MHD shock waves in weakly ionized molecular gas. We investigate the influence of dust on the critical shock speed, v_crit, above which C shocks cease to exist. Chernoff showed that v_crit cannot exceed the grain magnetosound speed, v_gms, if dust grains are dynamically well coupled to the magnetic field. We present numerical simulations of steady shocks where the grains may be well- or poorly coupled to the field. We use a time-dependent, multifluid MHD code that models the plasma as a system of interacting fluids: neutral particles, ions, electrons, and various ``dust fluids'' comprised of grains with different sizes and charges. Our simulations include grain inertia and grain charge fluctuations but to highlight the essential physics we assume adiabatic flow, single-size grains, and neglect the effects of chemistry. We show that the existence of a phase speed v_phi does not necessarily mean that C shocks will form for all shock speeds v_s less than v_phi. When the grains are weakly coupled to the field, steady, adiabatic shocks resemble shocks with no dust: the transition to J type flow occurs at v_crit = 2.76 v_nA, where v_nA is the neutral Alfven speed, and steady shocks with v_s > 2.76 v_nA are J shocks with magnetic precursors in the ion-electron fluid. When the grains are strongly coupled to the field, v_crit = min(2.76 v_nA, v_gms). Shocks with v_crit < v_s < v_gms have magnetic precursors in the ion-electron-dust fluid. Shocks with v_s > v_gms have no magnetic precursor in any fluid. We present time-dependent calculations to study the formation of steady multifluid shocks. The dynamics differ qualitatively depending on whether or not the grains and field are well coupled.Comment: 43 pages with 17 figures, aastex, accepted by The Astrophysical Journa

    Nonlinear adaptive control using non-parametric Gaussian Process prior models

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    Nonparametric Gaussian Process prior models, taken from Bayesian statistics methodology are used to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal (caution), and excitation of the system. The controller has dual features, since it is both tracking a reference signal and learning a model of the system from observed responses. The general method and its main features are illustrated on a simulation example

    Kolmogorov-Burgers Model for Star Forming Turbulence

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    The process of star formation in interstellar molecular clouds is believed to be controlled by driven supersonic magnetohydrodynamic turbulence. We suggest that in the inertial range such turbulence obeys the Kolmogorov law, while in the dissipative range it behaves as Burgers turbulence developing shock singularities. On the base of the She-Leveque analytical model we then predict the velocity power spectrum in the inertial range to be E_k ~ k^{-1.74}. This result reproduces the observational Larson law, ~ l^{0.74...0.76}, [Larson, MNRAS 194 (1981) 809] and agrees well with recent numerical findings by Padoan and Nordlund [astro-ph/0011465]. The application of the model to more general dissipative structures, with higher fractal dimensionality, leads to better agreement with recent observational results.Comment: revised, new material added, 8 page

    Teaching Students to Fish: Creating a Sustainable Student Peer Research Program

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    A Peer Research Mentor (PRM) program was developed at Musselman Library, Gettysburg College to augment traditional reference services and expand library outreach. Goals included enhancing these students’ information literacy skills helping them become better researchers, as well as sharing that knowledge with peers. This poster will highlight the initial and on-going training, their involvement at the reference desk, and outreach projects to date

    Neural networks for modelling and control of a non-linear dynamic system

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    The authors describe the use of neural nets to model and control a nonlinear second-order electromechanical model of a drive system with varying time constants and saturation effects. A model predictive control structure is used. This is compared with a proportional-integral (PI) controller with regard to performance and robustness against disturbances. Two feedforward network types, the multilayer perceptron and radial-basis-function nets, are used to model the system. The problems involved in the transfer of connectionist theory to practice are discussed

    Rubber-coated bellows improves vibration damping in vacuum lines

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    Compact-vibration damping systems, consisting of rubber-coated metal bellows with a sliding O-ring connector, are used in vacuum lines. The device presents a metallic surface to the vacuum system and combines flexibility with the necessary stiffness. It protects against physical damage, reduces fatigue failure, and provides easy mating of nonparallel lines

    Calculation of material properties and ray tracing in transformation media

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    Complex and interesting electromagnetic behavior can be found in spaces with non-flat topology. When considering the properties of an electromagnetic medium under an arbitrary coordinate transformation an alternative interpretation presents itself. The transformed material property tensors may be interpreted as a different set of material properties in a flat, Cartesian space. We describe the calculation of these material properties for coordinate transformations that describe spaces with spherical or cylindrical holes in them. The resulting material properties can then implement invisibility cloaks in flat space. We also describe a method for performing geometric ray tracing in these materials which are both inhomogeneous and anisotropic in their electric permittivity and magnetic permeability
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