A Model for Granular Texture with Steric Exclusion

We propose a new method to characterize the geometrical texture of a granular packing at the particle scale including the steric hindrance effect. This method is based on the assumption of a maximum disorder (entropy) compatible both with strain-induced anisotropy of the contact network and steric exclusions. We show that the predicted statistics for the local configurations is in a fairly agreement with our numerical data.Comment: 9 pages, 5 figure

An iterative method with error estimators

AbstractIterative methods for the solution of linear systems of equations produce a sequence of approximate solutions. In many applications it is desirable to be able to compute estimates of the norm of the error in the approximate solutions generated and terminate the iterations when the estimates are sufficiently small. This paper presents a new iterative method based on the Lanczos process for the solution of linear systems of equations with a symmetric matrix. The method is designed to allow the computation of estimates of the Euclidean norm of the error in the computed approximate solutions. These estimates are determined by evaluating certain Gauss, anti-Gauss, or Gauss–Radau quadrature rules

Dry granular flows: micromechanical interpretation of impacts on rigid obstacles.

The evaluation of impact forces exerted by flowing granular masses on rigid obstacles is of fundamental importance for the assessment of the associated risk and for the design of protection measures. A number of formulae are available in the literature for the maximum impact force; most of them are based on oversimplifying hypotheses about the behaviour of the granular material. For practical applications, formulations based on either hydrodynamic or elastic body models are often employed. These formulations require the use of empirical correcting factors. In order to better understand the impact mechanics, the authors have recently performed an extensive numerical campaign by using a Discrete Element approach (PFC3D code), where a dry granular mass is represented as a random distribution of rigid spherical particles. A new design formula, combining the hydrodynamic and elastic body theories, has been proposed on the base of the results obtained at the macroscopic scale. The parameters of the formula have been correlated with geometrical factors, namely front inclination and flow height. In this paper, the same DEM model is further used in order to investigate the relationship between the evolution with time of the impact force and the micromechanics of the granular mass. In particular, information about contact forces and particle velocities will be discussed and critically compared with macroscopic results. In order to progressively introduce the complexity of the impact phenomenon, three geometrical conditions are considered: a) vertical front, confined flow; b) vertical front, free surface flow; c) inclined front, free surface flow

An approach to enhance efficiency of DEM modelling of soils with crushable grains

In this study oedometric compression tests of hydrocarbon coke, Fontainebleau sand and silica sand are simulated in three dimensions using breakable particles. The method adapts a rigorous breakage criterion for elasto-brittle spheres to represent failure of grains isolated between platens or within granular masses. The breakage criterion allows for the effect of particle bulk and contact properties to be treated separately. A discrete fragmentation multigenerational approach is applied as a spawning procedure. The number of particles quickly increases during the simulation, but is kept manageable by systematic fine exclusion and upscaling. Fine exclusion leads to mass losses between generations, but that loss is accounted for outside the mechanical model. Sensitivity analysis shows that it is enough to keep 53% of the crushed particle mass within the mechanical model to correctly reproduce experimental macroscopic behaviour. Practical upscaling rules are proposed for (a) contact stiffness, (b) breakage criteria and (c) grain size distribution, and validated simulating the same test, reducing by half the initial number of particles. The results are promising as both the mechanical and grading evolution are well captured with two orders of magnitude savings in computing efficiency

The IAS-MEEG Package: A Flexible Inverse Source Reconstruction Platform for Reconstruction and Visualization of Brain Activity from M/EEG Data

We present a standalone Matlab software platform complete with visualization for the reconstruction of the neural activity in the brain from MEG or EEG data. The underlying inversion combines hierarchical Bayesian models and Krylov subspace iterative least squares solvers. The Bayesian framework of the underlying inversion algorithm allows to account for anatomical information and possible a priori belief about the focality of the reconstruction. The computational efficiency makes the software suitable for the reconstruction of lengthy time series on standard computing equipment. The algorithm requires minimal user provided input parameters, although the user can express the desired focality and accuracy of the solution. The code has been designed so as to favor the parallelization performed automatically by Matlab, according to the resources of the host computer. We demonstrate the flexibility of the platform by reconstructing activity patterns with supports of different sizes from MEG and EEG data. Moreover, we show that the software reconstructs well activity patches located either in the subcortical brain structures or on the cortex. The inverse solver and visualization modules can be used either individually or in combination. We also provide a version of the inverse solver that can be used within Brainstorm toolbox. All the software is available online by Github, including the Brainstorm plugin, with accompanying documentation and test data

Fast Gibbs sampling for high-dimensional Bayesian inversion

Solving ill-posed inverse problems by Bayesian inference has recently attracted considerable attention. Compared to deterministic approaches, the probabilistic representation of the solution by the posterior distribution can be exploited to explore and quantify its uncertainties. In applications where the inverse solution is subject to further analysis procedures, this can be a significant advantage. Alongside theoretical progress, various new computational techniques allow to sample very high dimensional posterior distributions: In [Lucka2012], a Markov chain Monte Carlo (MCMC) posterior sampler was developed for linear inverse problems with $\ell_1$-type priors. In this article, we extend this single component Gibbs-type sampler to a wide range of priors used in Bayesian inversion, such as general $\ell_p^q$ priors with additional hard constraints. Besides a fast computation of the conditional, single component densities in an explicit, parameterized form, a fast, robust and exact sampling from these one-dimensional densities is key to obtain an efficient algorithm. We demonstrate that a generalization of slice sampling can utilize their specific structure for this task and illustrate the performance of the resulting slice-within-Gibbs samplers by different computed examples. These new samplers allow us to perform sample-based Bayesian inference in high-dimensional scenarios with certain priors for the first time, including the inversion of computed tomography (CT) data with the popular isotropic total variation (TV) prior.Comment: submitted to "Inverse Problems

Cone penetration tests in a virtual calibration chamber

A virtual calibration chamber was built using a threedimensional model based on the discrete-element method. The chamber was then filled with a scaled granular equivalent of Ticino sand, the material properties of which were selected by curve-fitting triaxial tests. Cone penetration tests were then performed under different initial densities and isotropic stresses. Penetration resistance in the virtual calibration chamber was affected by the same cone/chamber size effect that affects physical calibration chambers and was corrected accordingly. The corrected cone resistance obtained from the virtual calibration chamber cone penetration tests shows good quantitative agreement with correlations that summarise previous physical results

The anisotropy of granular materials

The effect of the anisotropy on the elastoplastic response of two dimensional packed samples of polygons is investigated here, using molecular dynamics simulation. We show a correlation between fabric coefficients, characterizing the anisotropy of the granular skeleton, and the anisotropy of the elastic response. We also study the anisotropy induced by shearing on the subnetwork of the sliding contacts. This anisotropy provides an explanation to some features of the plastic deformation of granular media.Comment: Submitted to PR

Stress and Strain in Flat Piling of Disks

We have created a flat piling of disks in a numerical experiment using the Distinct Element Method (DEM) by depositing them under gravity. In the resulting pile, we then measured increments in stress and strain that were associated with a small decrease in gravity. We first describe the stress in terms of the strain using isotropic elasticity theory. Then, from a micro-mechanical view point, we calculate the relation between the stress and strain using the mean strain assumption. We compare the predicted values of Young's modulus and Poisson's ratio with those that were measured in the numerical experiment.Comment: 9 pages, 1 table, 8 figures, and 2 pages for captions of figure

VARIAN CLINAC 6 MeV Photon Spectra Unfolding using a Monte Carlo Meshed Model

[EN] Energy spectrum is the best descriptive function to determine photon beam quality of a Medical Linear Accelerator (LinAc). The use of realistic photon spectra in Monte Carlo simulations has a great importance to obtain precise dose calculations in Radiotherapy Treatment Planning (RTP). Reconstruction of photon spectra emitted by medical accelerators from measured depth dose distributions in a water cube is an important tool for commissioning a Monte Carlo treatment planning system. Regarding this, the reconstruction problem is an inverse radiation transport function which is ill conditioned and its solution may become unstable due to small perturbations in the input data. This paper presents a more stable spectral reconstruction method which can be used to provide an independent confirmation of source models for a given machine without any prior knowledge of the spectral distribution. Monte Carlo models used in this work are built with unstructured meshes to simulate with realism the linear accelerator head geometry.Ha recibido financiación de DESARROLLO E IMPLEMENTACION DE UN SISTEMA DE PLANIFICACION RADIOTERAPÉUTICA (TELETERAPIA Y BRAQUITERAPIA) BASADO EN EL MÉTODO MONTECARLO PARA EL HUPLAFE (UNIVERSIDAD POLITECNICA DE VALENCIA), DESARROLLO Y VALIDACION EXPERIMENTAL DE UN SISTEMA DE PLANIFICACION DE TRATAMIENTOS RADIOTERAPEUTICOS UTILIZANDO TECNICAS DE MONTE CARLO (UNIVERSIDAD POLITECNICA DE VALENCIA), Dosimetría in vivo en radioterapia sin filtro aplanador (FFF) (UNIVERSIDAD POLITECNICA DE VALENCIA), MONTE CARLO TREATMENT PLANNING SYSTEM: SOFTWARE PARA EL CALCULO DOSIMETRICO DE ALTA PRECISION EN RADIOTERAPIA. (UNIVERSIDAD POLITECNICA DE VALENCIA) y SISTEMA DE PLANIFICACION DE TRATAMIENTOS EN RADIOTERAPIA BASADO EN EL METODO DE MOTE CARLO Y EL PROCESADO 3D DE IMAGENES MEDICAS (GENERALITAT VALENCIANA)Morató-Rafet, S.; Juste Vidal, BJ.; Miró Herrero, R.; Verdú Martín, GJ. (2017). VARIAN CLINAC 6 MeV Photon Spectra Unfolding using a Monte Carlo Meshed Model. EPJ Web of Conferences. 153(04012):1-7. https://doi.org/10.1051/epjconf/201715304012S171530401