We consider a fermionic Hubbard chain with an additional next-to-nearest neighbor hopping term. We study the thermalization rates of the quasi-momentum distribution function within a quantum Boltzmann equation approach. We find that the thermalization rates are proportional to the square of the next-to-nearest neighbor hopping: Even weak next-to-nearest neighbor hopping in addition to nearest neighbor hopping leads to thermalization in a two-particle scattering quantum Boltzmann equation in one dimension. We also investigate the temperature dependence of the thermalization rates, which away from half filling become exponentially small for small temperature of the final thermalized distribution.
We investigate the evolution of a photoexcitation in correlated materials over a wide range of time scales. The system studied is a one-dimensional model of a manganite with correlated electron, spin, orbital, and lattice degrees of freedom, which we relate to the three-dimensional material Pr1−xCaxMnO3. The ground-state phases for the entire composition range are determined and rationalized by a coarse-grained polaron model. At half-doping a pattern of antiferromagnetically coupled Zener polarons is realized. Using time-dependent density-matrix renormalization group (tDMRG), we treat the electronic quantum dynamics following the excitation. The emergence of quasiparticles is addressed, and the relaxation of the nonequilibrium quasiparticle distribution is investigated via a linearized quantum-Boltzmann equation. Our approach shows that the magnetic microstructure caused by the Zener polarons leads to an increase of the relaxation times of the excitation. arXiv:1610.07246v4 [cond-mat.str-el]
Magnetic Induction Tomography (MIT) is a non-invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. With the exploitation of parallelism, it is possible to achieve high quality inexpensive MIT images for biomedical applications on clinically relevant time scales. In this paper we investigate the performance of different parallel implementations of the forward eddy current problem, which is the main computational component of the inverse problem through which measured voltages are converted into images. We show that a heterogeneous parallel method that exploits multiple CPUs and GPUs can provide a high level of parallel scaling, leading to considerably improved runtimes. We also show how multiple GPUs can be used in conjunction with deal.II, awidely-used open source finite element library. KEYWORDScomputational electromagnetics, magnetic induction tomography, parallel applications INTRODUCTIONMagnetic Induction Tomography (MIT) is a non-invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. These parameters are the permittivity, , the permeability, , and the conductivity, . An MIT device consists of two sets of coils placed around the boundary of the object to be imaged. The first set of coils is used for the purpose of excitation, and by passing a current through each coil in turn, a primary magnetic field is created. The second set of coils is then used for measurement. This procedure causes an eddy current when each of the primary magnetic fields interacts with a conducting body inducing secondary magnetic fields, and hence voltages, that are measured in the second set of coils. In order to estimate the electromagnetic properties of the material, ( , , ), from the induced currents and measured voltage, an inverse problem must be solved. In many practical applications, the distribution of one or more of these material parameters is assumed to be constant throughout the medium of interest.Conventional imaging techniques for imaging cerebral stroke, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), are expensive. Although MRI may be used for real-time image reconstruction, 1 it has been proposed that MIT can offer a low cost alternative in the first stages of diagnosis. 2 However, the low conductivity contrast between biomedical tissues presents significant challenges to MIT, and there are considerable difficulties in employing current computational techniques to solve the associated inverse problem. 3,4 Enabling MIT to take the step from being an experimental technique, which has already received some clinical interest, to become a viable imaging technique for the detection and monitoring of conditions, such as cerebral stroke, requires a step change in the quality of ...
The properties of granular or sintered manufactured materials or natural materials depend on their microstructure. Obtaining meaningful insights on the microstructure of these materials often relies on the correct identification of individual grains and pores. Their direct identification on 3D digital images is challenging but opens the possibility to detailed simulations and accurate predition of material behavior, such as mechanical simulations that depend on the grain orientation. Such simulations give vital information to figure out the most promising designs of manufactured materials such as electrodes or to reach decisions based on reservoir rock properties and determined by Digital Rock Physics. With this motivation, several new algorithms have been integrated into the GeoDict® [1] software. GeoDict’s capabilities to segment 3D scans, e.g. CT or FIB-SEM scans have been expanded to identify also individual grains and pores through the new modules GrainFind and PoreFind that are presented here, together with the option to use the results of GrainFind for the creation of digital twins of granular materials.
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