Solar wind turbulence from MHD to sub-ion scales: high-resolution hybrid simulations

We present results from a high-resolution and large-scale hybrid (fluid electrons and particle-in-cell protons) two-dimensional numerical simulation of decaying turbulence. Two distinct spectral regions (separated by a smooth break at proton scales) develop with clear power-law scaling, each one occupying about a decade in wave numbers. The simulation results exhibit simultaneously several properties of the observed solar wind fluctuations: spectral indices of the magnetic, kinetic, and residual energy spectra in the magneto-hydrodynamic (MHD) inertial range along with a flattening of the electric field spectrum, an increase in magnetic compressibility, and a strong coupling of the cascade with the density and the parallel component of the magnetic fluctuations at sub-proton scales. Our findings support the interpretation that in the solar wind large-scale MHD fluctuations naturally evolve beyond proton scales into a turbulent regime that is governed by the generalized Ohm's law.Comment: 5 pages, 5 figures; introduction and conclusions changed, references updated, accepted for publication in ApJ

Two-dimensional Hybrid Simulations of Kinetic Plasma Turbulence: Current and Vorticity vs Proton Temperature

Proton temperature anisotropies between the directions parallel and perpendicular to the mean magnetic field are usually observed in the solar wind plasma. Here, we employ a high-resolution hybrid particle-in-cell simulation in order to investigate the relation between spatial properties of the proton temperature and the peaks in the current density and in the flow vorticity. Our results indicate that, although regions where the proton temperature is enhanced and temperature anisotropies are larger correspond approximately to regions where many thin current sheets form, no firm quantitative evidence supports the idea of a direct causality between the two phenomena. On the other hand, quite a clear correlation between the behavior of the proton temperature and the out-of-plane vorticity is obtained.Comment: 4 pages, 2 figures, Proceedings of the Fourteenth International Solar Wind Conferenc

High-resolution hybrid simulations of kinetic plasma turbulence at proton scales

We investigate properties of plasma turbulence from magneto-hydrodynamic (MHD) to sub-ion scales by means of two-dimensional, high-resolution hybrid particle-in-cell simulations. We impose an initial ambient magnetic field, perpendicular to the simulation box, and we add a spectrum of large-scale magnetic and kinetic fluctuations, with energy equipartition and vanishing correlation. Once the turbulence is fully developed, we observe a MHD inertial range, where the spectra of the perpendicular magnetic field and the perpendicular proton bulk velocity fluctuations exhibit power-law scaling with spectral indices of -5/3 and -3/2, respectively. This behavior is extended over a full decade in wavevectors and is very stable in time. A transition is observed around proton scales. At sub-ion scales, both spectra steepen, with the former still following a power law with a spectral index of ~-3. A -2.8 slope is observed in the density and parallel magnetic fluctuations, highlighting the presence of compressive effects at kinetic scales. The spectrum of the perpendicular electric fluctuations follows that of the proton bulk velocity at MHD scales, and flattens at small scales. All these features, which we carefully tested against variations of many parameters, are in good agreement with solar wind observations. The turbulent cascade leads to on overall proton energization with similar heating rates in the parallel and perpendicular directions. While the parallel proton heating is found to be independent on the resistivity, the number of particles per cell and the resolution employed, the perpendicular proton temperature strongly depends on these parameters.Comment: 15 pages, 13 figures, submitted to Ap

Energy saving in tooling machines: a new unified approach to reduce energy consumption

Tooling machines are included in some EU directives, which set specific targets for the reduction of energy consumption in the near future. This paper aims to introduce a design approach that can be useful both for safety functional decomposition and for energy consumption evaluation of a generic tooling machines. This design approach tries to unify the existing divergent approach to energy efficient and safe tooling machines. A very simple application, already installed in some lathe machines currently produced in the EU, will give us all the necessary data (activity time counter) to perform a quantitative assessment in term of unified energy-efficient and safe machines. Moreover, the main results of an extensive survey made by a lathe manufacturer on real machines utilization and some measurement of wasted energy during standby mode of different machines will be presented. Those measurements show that it is not possible to define a proper LCA design method without considering that the wasted energy is a function of the size and type of processes and the specific operating conditions of the machine. Measurements, performed during stand-by of lathes with regenerative drives, are presented at the end of the paper

Crack identification using electrical impedance tomography and transfer learning

Sensing skins and electrical impedance tomography constitute a convenient and inexpensive alternative to dense sensor networks for distributed sensing in civil structures. However, their performance can deteriorate with the aging of the sensing film. Guaranteeing high identification performance after minor lesions is crucial to improving their ability to identify structural damage. In this paper, electrical resistance tomography is used to identify the crack locations in nanocomposite paint sprayed onto structural components. The main novelty consists of using crack annotations collected during visual inspections to improve the crack identification performance of deep neural networks trained using simulated datasets through transfer learning. Transfer component analysis is employed for simulation-to-real information transfer and applied at a population level, extracting low-dimensional domain-invariant features shared by simulated models and structures with similar geometry. The results show that the proposed method outperforms traditional approaches for crack localization in complex damage patterns