Phonon density of states and compression behavior in iron sulfide under pressure

We report the partial phonon densities of states (DOS) of iron sulfide, a possible component of the rocky planet's core, measured by the Fe-57 nuclear resonant inelastic x-ray scattering and calculate the total phonon DOS under pressure. From the phonon DOS, we drive thermodynamic parameters. A comparison of the observed and estimated compressibilities makes it clear that there is a large pure electronic contribution in the observed compressibility in the metallic state. Our results present the observation of thermodynamic parameters of iron sulfide with the low-spin state of an Fe2+ ion at the high density, which is similar to the condition of the Martian core

Goertler instability in compressible boundary layers along curved surfaces with suction and cooling

The Goertler instability of the laminar compressible boundary layer flows along concave surfaces is investigated. The linearized disturbance equations for the three-dimensional, counter-rotating streamwise vortices in two-dimensional boundary layers are presented in an orthogonal curvilinear coordinate. The basic approximation of the disturbance equations, that includes the effect of the growth of the boundary layer, is considered and solved numerically. The effect of compressibility on critical stability limits, growth rates, and amplitude ratios of the vortices is evaluated for a range of Mach numbers for 0 to 5. The effect of wall cooling and suction of the boundary layer on the development of Goertler vortices is investigated for different Mach numbers

Order via Nonlinearity in Randomly Confined Bose Gases

A Hartree-Fock mean-field theory of a weakly interacting Bose-gas in a quenched white noise disorder potential is presented. A direct continuous transition from the normal gas to a localized Bose-glass phase is found which has localized short-lived excitations with a gapless density of states and vanishing superfluid density. The critical temperature of this transition is as for an ideal gas undergoing Bose-Einstein condensation. Increasing the particle-number density a first-order transition from the localized state to a superfluid phase perturbed by disorder is found. At intermediate number densities both phases can coexist.Comment: Author Information under http://www.theo-phys.uni-essen.de/tp/ags/pelster_dir/. International Journal of Bifurcation and Chaos (in press

Magneto-structural coupling and harmonic lattice dynamics in CaFe2_2As2_2 probed by M\"ossbauer spectroscopy

In this paper we present detailed M\"ossbauer spectroscopy study of structural and magnetic properties of the undoped parent compound CaFe$_2$As$_2$ single crystal. By fitting the temperature dependence of the hyperfine magnetic field we show that the magneto-structural phase transition is clearly first-order in nature and we also deduced the compressibility of our sample to be $1.67\times10^{-2}\,GPa^{-1}$. Within the Landau's theory of phase transition, we further argue that the observed phase transition may stem from the strong magneto-structural coupling effect. Temperature dependence of the Lamb-M\"ossbauer factor show that the paramagnetic phase and the antiferromagnetic phase exhibit similar lattice dynamics in high frequency modes with very close Debye temperatures, $\Theta_D \sim$270\,K.Comment: 6 pages,5 figures Accepted by J. Phys.: Condens. Matte

Direct numerical simulation of dispersed particles in a compressible fluid

We present a direct numerical simulation method for investigating the dynamics of dispersed particles in a compressible solvent fluid. The validity of the simulation is examined by calculating the velocity relaxation of an impulsively forced spherical particle with a known analytical solution. The simulation also gives information about the fluid motion, which provides some insight into the particle motion. Fluctuations are also introduced by random stress, and the validity of this case is examined by comparing the calculation results with the fluctuation-dissipation theorem.Comment: 7 pages, 5 figure

Negative Linear Compressibility

While all materials reduce their intrinsic volume under hydrostatic (uniform) compression, a select few actually \emph{expand} along one or more directions during this process of densification. As rare as it is counterintuitive, such "negative compressibility" behaviour has application in the design of pressure sensors, artificial muscles and actuators. The recent discovery of surprisingly strong and persistent negative compressibility effects in a variety of new families of materials has ignited the field. Here we review the phenomenology of negative compressibility in this context of materials diversity, placing particular emphasis on the common structural motifs that recur amongst known examples. Our goal is to present a mechanistic understanding of negative compressibility that will help inform a clear strategy for future materials design.Comment: Submitted to PCC

A functional link neural network with modified cuckoo search for prediction tasks

The impact of temperature, relative humidity and ozone changes bring a sharp warming climate. These changes can cause extreme consequences such as floods, hurricanes, heat waves and droughts. Therefore, prediction of temperature and relative humidity is an important factor to measure the environmental changes. Neural network, especially the Multi-Layer Perceptron (MLP) which uses Back Propagation algorithm (BP) as a supervised learning method, has been successfully applied in various problems for meteorological prediction tasks. However, this architecture has still been facing problems which the convergence rate is very low due to the multi layering topology of the network. Thus, this research proposed an implementation of Functional Link Neural Network (FLNN) which composed of a single layer of tunable weight trained with the Modified Cuckoo Search algorithm (MCS). The proposed approach was used to predict the daily temperatures, relative humidity and ozone data. Extensive simulation results have been compared with standard MLP trained with the BP, FLNN with BP and FLNN with CS. Promising results have shown that the proposed model has successfully out performed 14% percentage compared to other network models with reduced prediction error and fast convergence rate