Coupled rotor-body vibrations with inplane degrees of freedom

In an effort to understand the vibration mechanisms of helicopters, the following basic studies are considered. A coupled rotor-fuselage vibration analysis including inplane degrees of freedom of both rotor and airframe is performed by matching of rotor and fuselage impedances at the hub. A rigid blade model including hub motion is used to set up the rotor flaplag equations. For the airframe, 9 degrees of freedom and hub offsets are used. The equations are solved by harmonic balance. For a 4-bladed rotor, the coupled responses and hub loads are calculated for various parameters in forward flight. The results show that the addition of inplane degrees of freedom does not significantly affect the vertical vibrations for the cases considered, and that inplane vibrations have similar resonance trends as do flapping vibrations

A scattering theory of ultrarelativistic solitons

We construct a perturbative framework for understanding the collision of solitons (more precisely, solitary waves) in relativistic scalar field theories. Our perturbative framework is based on the suppression of the space-time interaction area proportional to $1/(\gamma v)$, where $v$ is the relative velocity of an incoming solitary wave and $\gamma = 1/\sqrt{1-v^2} \gg 1$. We calculate the leading order results for collisions of (1+1) dimensional kinks in periodic potentials, and provide explicit, closed form expressions for the phase shift and the velocity change after the collisions. We find excellent agreement between our results and detailed numerical simulations. Crucially, our perturbation series is controlled by a kinematic parameter, and hence not restricted to small deviations around integrable cases such as the Sine-Gordon model.Comment: v3: 43 pages, 10 figures, references added, matches version accepted for publication in PR

Flavor-Changing Top Quark Rare Decays in the Littlest Higgs Model with T-Parity

We analyze the rare and flavor changing decay of the top quark into a charm quark and a gauge boson in the littlest Higgs model with T-parity (LHT). We calculate the one-loop level contributions from the T-parity odd mirror quarks and gauge bosons. We find that the decay $t\to c V, (V=g,\gamma,Z)$ in the LHT model can be significantly enhanced relative to those in the Standard Model. Our numerical results show that the top quark FCNC decay can be as large as $Br(t\to cg)\sim 10^{-2}$, $Br(t\to cZ)\sim 10^{-5}$ and $Br(t\to c\gamma)\sim 10^{-7}$ in the favorite parameter space in the LHT model.Comment: 12 pages, 3 figure

Non-linear supersymmetric Sigma-Model for Diffusive Scattering of Classical Waves with Resonance Enhancement

We derive a non-linear sigma-model for the transport of light (classical waves) through a disordered medium. We compare this extension of the model with the well-established non-linear sigma-model for the transport of electrons (Schroedinger waves) and display similarities of and differences between both cases. Motivated by experimental work (M. van Albada et al., Phys. Rev. Lett. 66 (1991) 3132), we then generalize the non-linear sigma-model further to include resonance scattering. We find that the form of the effective action is unchanged but that a parameter of the effective action, the mean level density, is modified in a manner which correctly accounts for the data.Comment: 4 pages, 1 Figure, to be published in Europhysics Letter

Multiorder neurons for evolutionary higher-order clustering and growth

This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the correlation of clusters found with ground truth information is used in measuring clustering accuracy, the proposed evolutionary multiorder neurons method can be shown to outperform other related clustering methods. The simulation results from the Iris, Wine, and Glass data sets show significant improvement when compared to the results obtained using self-organizing maps and higher-order neurons. The letter also proposes an intuitive model by which multiorder neurons can be grown, thereby determining the number of clusters in data