Modelling of dust generation, transport and remobilization in full-metal fusion reactors

The design, licensing and operation of magnetic confinement fusion reactors impose various limitations on the amount of metallic dust particles residing inside the plasma chamber. In this context, predictive studies of dust production and migration constitute one of the main sources of relevant data. These are mainly conducted using dust transport codes, which rely on coupled dust-plasma and dust-wall interaction models, and require external input on the dust and droplet initial conditions. Some particularities of dust modelling in reactor-relevant conditions are analyzed with an emphasis on dust generation mechanisms relevant for disruption scenarios and on dust remobilization mechanisms relevant for ramp-up scenarios. Emerging topics such as dust production by runaway electron impact and pre-plasma remobilization of magnetic dust are also discussed.

Structural characterization of defects in EFG- and HVPE-grown β-Ga2O3 crystals

This paper reviews the status of characterization of defects in β-Ga2O3 crystals grown by edge-defined film-fed growth and hydride vapor phase epitaxy using chemical etching, scanning electron microscopy, focused ion beam scanning ion microscopy, X-ray topography (XRT), and transmission electron microscopy (TEM). The observed defects are classified into four types: dislocations, stacking faults (SFs), twins, and plate-like nanovoids (PNVs). First, we present the detailed characterization of dislocations in the crystal by chemical etching, XRT, and TEM, and discuss possible slip systems. Next, we describe XRT analyses of two types of SFs: SFs 1 lying on the (2 ̅01) plane and SFs 2 on the (111) and (11 ̅1) planes. We describe the results for twins found in crystals via high-resolution TEM and electron diffraction analysis, and PNVs corresponding to etch pits on the (010) plane. Finally, we discuss possible generation mechanisms of the defects and their influence on device characteristics.

Thermo-mechanical modelling of the Friction Stir Spot Welding process: Effect of the friction models on the heat generation mechanisms

Friction Stir Spot Welding involves complex physical phenomena, which are very difficult to probe experimentally. In this regard, the numerical simulation may play a key role to gain insight into this complex thermo-mechanical process. It is often used to mimic specific experimental conditions to forecast outputs that may be substantial to analyse and elucidate the mechanisms behind the Friction Stir Spot Welding process. This welding technique uses frictional heat generated by a rotating tool to join materials. The heat generation mechanisms are governed by a combination of sliding and sticking contact conditions. In the numerical simulation, these contact conditions are thoroughly dependent on the used friction model. Hence, a successful prediction of the process relies on the appropriate selection of the contact model and parameters. This work aims to identify the pros and cons of different friction models in modelling combined sliding-sticking conditions. A three-dimensional coupled thermo-mechanical FE model, based on a Coupled Eulerian-Lagrangian formulation, was developed. Different friction models are adopted to simulate the Friction Stir Spot Welding of the AA6082-T6 aluminium alloy. For these friction models, the temperature evolution, the heat generation, and the plastic deformation were analysed and compared with experimental results. It was realized that numerical analysis of Friction Stir Spot Welding can be effective and reliable as long as the interfacial friction characteristics are properly modelled. This approach may be used to guide the contact modelling strategy for the simulation of the Friction Stir Spot Welding process and its derivatives.

Emergence of Low-Frequency Aftershocks of the 2019 Ridgecrest Earthquake Sequence

ABSTRACT Low-frequency earthquakes (LFEs) generally have relatively stronger spectral components in the lower frequency range compared with what is expected for regular earthquakes based on their magnitude. LFEs generally occur in volcanic systems or deep (>∼15 km) in plate boundary fault zones; however, LFEs have also been observed in nonvolcanic, upper crustal settings. Because there are few studies that explore the spatiotemporal behaviors of LFEs in the shallow crust, it remains unclear whether the shallow-crustal LFEs reflect local attenuation in their immediate vicinity or differences in their source mechanism. Therefore, it is important to identify shallow-crustal LFEs and to characterize their spatiotemporal activity, which may also improve our understanding of LFEs. In this study, we focus on detecting shallow-crustal LFEs and explore the possible generation mechanisms. We analyze 29,646 aftershocks in the 2019 Ridgecrest, California, earthquake sequence, by measuring the frequency index (FI) to identify candidate low-frequency aftershocks (LFAs), while accounting for the magnitude dependency of the FI. Using small earthquakes (ML 1–3) recorded in the borehole stations to minimize the attenuation effects in near-surface layers, we identify 68 clear LFAs in total. Based on their distribution and comparisons with other seismic parameters measured by Trugman (2020), the LFAs possess distinct features from regular events in the same depths range, including low corner frequencies and low stress drops. Events in the close vicinity of LFAs exhibit lower average FI values than regular aftershocks, particularly if the hypocentral distance between an LFA and its neighbors is less than 1 km. Our results suggest that LFAs are related to local heterogeneity or a highly fractured fault zone correlated with an abundance of cross faults induced by the aftershock sequence at shallow depths. Zones of high pore-fluid pressure in intensely fractured fault zones could cause the bandlimited nature of LFAs and LFEs in general.

Potential Association Between the Low-Energy Plasma Structure and the Patchy Pulsating Aurora

While the pulsating auroral phenomena have been recognized and studied for decades, our understating of their generation mechanisms remains incomplete to date. In one main class of pulsating auroras which is termed “patchy pulsating auroras” (PPA), the auroral patches are found to basically maintain their shape and size over many pulsation cycles. Also, PPAs are repeatedly found to essentially co-move with the ExB convection drift. The above properties led many researchers to hypothesize that PPA might connect to a structure of enhanced cold plasma in the magnetosphere. In this study, we review the existing evidence, and provide new perspective and support, of the low-energy plasma structure potentially associated with PPA. Based on observations from both the magnetosphere and the topside ionosphere, we suggest that ionospheric auroral outflows might constitute one possible source mechanism of the flux tubes with enhanced low-energy plasma that connect to the PPA. We also review the existing theories of pulsating auroras, with particular focus on the role of low-energy plasma in these theories. To date, none of the existing theories are complete and mature enough to offer a quantitatively satisfactory explanation of pulsating auroras. At last, we suggest a few future research directions to advance our understanding of pulsating auroras: a) more accurate measurements of the cold plasma density, b) more developed theories of the underlying mechanisms of ELF/VLF wave modulation, and c) auxiliary processes in the topside ionosphere or near-Earth region accompanying pulsating auroras.

Influence of Magnetostriction Induced by the Periodic Permanent Magnet Electromagnetic Acoustic Transducer (PPM EMAT) on Steel

The periodic permanent magnet electromagnetic acoustic transducer (PPM EMAT) is a sensor that can generate and receive shear horizontal (SH) waves without direct contact with the inspected medium using the Lorentz mechanism. However, the PPM EMAT experiences high signal variance on ferromagnetic steel under specific conditions, such as a change in signal amplitude when the sensor is moved in the direction of SH wave propagation. Magnetostriction effects are hypothesized to be the cause of these anomalous behaviors; the objective of this paper is to determine the relative strengths of the magnetostriction and Lorentz wave generation mechanisms for this type of EMAT on steel. This goal is accomplished through the use of a second EMAT, which induces only magnetostriction (MS-EMAT), to calibrate a novel semi-empirical magnetostriction model. It is found that magnetostriction effects reduce the amplitude of the SH wave generated by this particular PPM EMAT transmitter by an average of 29% over a range of input currents. It is also determined that magnetostriction is significant only in the investigated PPM EMAT transmitter, not the receiver. In terms of practical application, it is shown that the MS-EMAT is less sensitive to changes in the static and dynamic fields than PPM EMATs at specific operating points; this makes the MS-EMAT a viable alternative for nondestructive evaluation despite lower amplitudes.

Neuro-inspired Framework for Cognitive Manufacturing Control

There are currently certain categories of manufacturing enterprises whose structure, organization and operating context have an extremely high degree of complexity, especially due to the way in which their various components interact and influence each other. For them, a series of paradigms have been developed, including intelligent manufacturing, smart manufacturing, cognitive manufacturing; which are based equally on information and knowledge management, management and interpretation of data flows and problem solving approaches. This work presents a new vision regarding the evolution of the future enterprise based on concepts and attributes acquired from the field of biology. Our approach addresses in a systemic manner the structural, functional, and behavioral aspects of the enterprise, seen as a complex dynamic system. In this article we are proposing an architecture and management methodology based on the human brain, where the problem solving is achieved by Perception – Memory – Learning and Behavior Generation mechanisms. In order to support the design of such an architecture and to allow a faster learning process, a software modeling and simulation platform was developed and is briefly presented.