Pablo García Fernández

Subgrid-scale modeling and implicit numerical dissipation in DG-based Large-Eddy Simulation

Over the past few years, high-order discontinuous Galerkin (DG) methods for Large-Eddy Simulation (LES) have emerged as a promising approach to solve complex turbulent flows. However, despite the significant research investment, the relation between the discretization scheme, the subgrid-scale (SGS) model and the resulting LES solver remains unclear. This paper aims to shed some light on this matter. To that end, we investigate the role of the Riemann solver, the SGS model, the time resolution, and the accuracy order in the ability to predict a variety of flow regimes, including transition to turbulence, wall-free turbulence, wall-bounded turbulence, and turbulence decay. The transitional flow over the Eppler 387 wing, the TaylorGreen vortex problem and the turbulent channel flow are considered to this end. The focus is placed on post-processing the LES results and providing with a rationale for the performance of the various approaches.

Giant Optical Polarization Rotation Induced by Spin-Orbit Coupling in Polarons

We have uncovered a giant gyrotropic magneto-optical response for doped ferromagnetic manganite La_{2/3}Ca_{1/3}MnO_{3} around the near room-temperature paramagnetic-to-ferromagnetic transition. At odds with current wisdom, where this response is usually assumed to be fundamentally fixed by the electronic band structure, we point to the presence of small polarons as the driving force for this unexpected phenomenon. We explain the observed properties by the intricate interplay of mobility, Jahn-Teller effect, and spin-orbit coupling of small polarons. As magnetic polarons are ubiquitously inherent to many strongly correlated systems, our results provide an original, general pathway towards the generation of magnetic-responsive gigantic gyrotropic responses that may open novel avenues for magnetoelectric coupling beyond the conventional modulation of magnetization.

Experimental Investigation on the Influence of Relative Density on the Compressive Behaviour of Metal Mesh Isolator

Vibrations, considered one of the major problems in the engineering applications, are analyzed to predict their detrimental effects on the equipment and structures. The metal mesh isolator, named also metal rubber, has become widely applied to mitigate the disturbing vibration due to its special production techniques. Metal rubber is a kind of novel style porous damping material that is manufactured via a process of wire-drawing, weaving and compression moulding. This paper investigates the influence on the compression and dissipative behaviour of the metal mesh isolator provided by the relative density. The mechanical properties of five metal mesh samples with cylindrical geometry and with different relative density are obtained from a quasi-static cyclic compression test. The loading-unloading results of the five samples, subjected to a constant compression level, show the strong dependence exerted by the relative density over the compressive properties. Experimental analysis indicated that the porosity affects the stiffness but has an opposite effect regarding the loss factor. By increasing the ratio between the isolator’s and wire’s densities, the nominal stiffness increases, but the reduction of loss factor is obvious.

Experimental Investigation of Normal/Lateral Excitation Direction Influence on the Dynamic Characteristics of Metal Mesh Isolator

Vibrations, considered one of the major problems in the engineering applications, are analyzed to predict their detrimental effects on the equipment and structures. The metal mesh isolator has become widely applied to mitigate the disturbing vibration due to its special production techniques. The metal mesh isolator is a kind of novel style porous damping material that is manufactured via a process of wire-drawing, weaving and compression molding. The influencing laws of the manufacturing parameters including the relative density and the working condition together with the excitation direction dependence should be taken into account in the characterization of the metallic wires material. In this paper, the mechanical properties of three models with different relative density will be investigated under different preload masses and for three acceleration levels. A number of experiments can be examined by changing the direction of excitation in order to describe the compression and non-compression molding direction effect on the dynamic behavior. A modal analysis is performed using the rational fraction polynomial method to determine the stiffness and the damping ratio from the measured transmissibility data. According to the reported experimental results, the major factor affecting the mechanical characteristics (stiffness and damping) is the sliding friction that exists at the contact-points between wires.

Vibratory Data Fusion for Gearbox Fault Detection Using Autoassociative Neural Networks

This paper deals with gearbox fault detection by vibration analysis. A new processing procedure is proposed which uses the information from several acquisition channels. The approach is based on the assumption that there is a non-linear relationship among the instantaneous vibration magnitude registered for each measurement location. This relationship is captured in the connection weight matrix of an Autoassociative Artificial Neural Network (AANN), which is trained to provide an output vector equal to the input one. In this work, the time synchronous average signal (TSA) for each channel corresponding to the no fault condition is used to train an AANN. Once the AANN is trained, it is used with new data registers as a linear prediction error filter. If the new register contains the same data structure as the training set the prediction error will be low and the machine is working properly. Otherwise, when the new register differs from the training set, as a consequence of a fault, prediction error will be increased in each channel. In this way the information from not only one channel but more than one is used for fault detection and diagnosis as the error signal depends on the TSA signal from all channels. The proposed approach provides a new tool for gear fault detection that is compared on the basis of experimental registers with the most traditional gear processing tools based on TSA such as residual and regular signals. The possibility of generalizing the net prediction capabilities using a training data set that contains several load cases is also explored.Copyright