Optimal Selection of the Reference Potential Probe Point in DCPD Real-Time Monitoring of Crack Growth Rate

Quantitatively monitor the crack growth rate of material stress corrosion cracking (SCC) in an autoclave that simulates a high-temperature and high-pressure water environment, and the direct current potential drop (DCPD) method is the main method. Since the DCPD method tests micro-nano-voltage drop signals, the monitoring signal is weak and easy to be interfered by the environment. To reduce and balance the error caused by the temperature drift and other factors to the monitoring accuracy, it is very important to reasonably select the position of the reference potential probe point. In this study, genetic algorithm (GA), finite element method (FEM), and experimental analysis are used to optimize the position of the reference potential probe point of the compact tensile (CT) sample. Finite element method is used to analyze the electric potential field of the compact tensile sample, a mathematical model of the measurability and crack independence of the reference potential difference are constructed, genetic algorithm is used to find the optimal reference potential difference (RPD) probe point position, and finally, the crack monitoring experiments are performed to evaluate the feasibility of algorithm optimization results. The results indicate that the RPD measured at the current input point and the upper right position of the CT sample can provide the maximum compensation for the potential on both sides of the crack and make the performance of the monitoring signal optimal.

FEM based 3D modelling of partial discharge detection and localization in an oil-filled power transformer using piezoelectric acoustic sensor

The main cause of insulation degradation is due to partial discharges (PDs) occurring inside the transformer, and its detection and localization are the most effective, non-destructive methods to assess the insulation condition of the transformer. Among the PD detection methods, the acoustic PD detection technique is popular because of its various advantages. The acoustic PD detection method for accurate PD source localization becomes quite challenging when PD occurs inside the transformer core and windings. As the acoustic sound wave can be distorted and vibration with its distribution, so the type of PD sensors with their setting in the transformer should be thoroughly investigated and chosen. In this work, via simulation, the acoustic sound distribution inside the power transformer due to PD occurs is studied. Based on the knowledge of acoustic pressure wave distribution, a Lead Zirconate Titanate (PZT-5H) sensor is designed using Finite element method based COMSOL Multiphysics software and placed it on the outer walls of the transformer for PD detection and localization. The PD induction position has been recognized from the sensor signal using an artificial neural network. The results of PD detection and localization by the proposed piezoelectric sensor and COMSOL probe point are in good agreement.

Unconventional Magnetism in Layered Transition Metal Dichalcogenides

In this contribution to the MDPI Condensed Matter issue in Honor of Nobel Laureate Professor K.A. Müller I review recent experimental progress on magnetism of semiconducting transition metal dichalcogenides (TMDs) from the local-magnetic probe point of view such as muon-spin rotation and discuss prospects for the creation of unique new device concepts with these materials. TMDs are the prominent class of layered materials, that exhibit a vast range of interesting properties including unconventional semiconducting, optical, and transport behavior originating from valley splitting. Until recently, this family has been missing one crucial member: magnetic semiconductor. The situation has changed over the past few years with the discovery of layered semiconducting magnetic crystals, for example CrI 3 and VI 2 . We have also very recently discovered unconventional magnetism in semiconducting Mo-based TMD systems 2H-MoTe 2 and 2H-MoSe 2 [Guguchia et. al., Science Advances 2018, 4(12)]. Moreover, we also show the evidence for the involvement of magnetism in semiconducting tungsten diselenide 2H-WSe 2 . These results open a path to studying the interplay of 2D physics, semiconducting properties and magnetism in TMDs. It also opens up a host of new opportunities to obtain tunable magnetic semiconductors, forming the basis for spintronics.

Augmented Single Loop Single Vector Algorithm Using Nonlinear Approximations of Constraints in Reliability-Based Design Optimization

Reliability-based design optimization (RBDO) aims at minimizing a function of probabilistic design variables, given a maximum allowed probability of failure. The most efficient methods available for solving moderately nonlinear problems are single loop single vector (SLSV) algorithms that use a first-order approximation of the probability of failure in order to rewrite the inherently nested structure of the loop into a more efficient single loop algorithm. The research presented in this paper takes off from the fundamental idea of this algorithm. An augmented SLSV algorithm is proposed that increases the rate of convergence by making nonlinear approximations of the constraints. The nonlinear approximations are constructed in the following way: first, the SLSV experiments are performed. The gradient of the performance function is known, as well as an estimate of the most probable failure point (MPP). Then, one extra experiment, a probe point, per performance function is conducted at the first estimate of the MPP. The gradient of each performance function is not updated but the probe point facilitates the use of a natural cubic spline as an approximation of an augmented MPP estimate. The SLSV algorithm using probing (SLSVP) also incorporates a simple and effective move limit (ML) strategy that also minimizes the heuristics needed for initiating the optimization algorithm. The size of the forward finite difference design of experiment (DOE) is scaled proportionally with the change of the ML and so is the relative position of the MPP estimate at the current iteration. Benchmark comparisons against results taken from the literature show that the SLSVP algorithm is more efficient than other established RBDO algorithms and converge in situations where the SLSV algorithm fails.

Acceleratingk-nearest-neighbor searches

The search for whichkpoints are closest to a given probe point in a space ofNknown points, the `k-nearest-neighbor' or `KNN' problem, is a computationally challenging problem of importance in many disciplines, such as the design of numerical databases, analysis of multi-dimensional experimental data sets, multi-particle simulations and data mining. A standard approach is to preprocess the data into a tree and make use of the triangle inequality to prune the search time to the order of the logarithm ofNfor a single nearest point in a well balanced tree. All known approaches suffer from the `curse of dimensionality', which causes the search to explore many more branches of the tree than one might wish as the dimensionality of the problem increases, driving search times closer to the order ofN. Looking forknearest points can sometimes be done in approximately the time needed to search for one nearest point, but more often it requiresksearches because the results are distributed widely. The result is very long search times, especially when the search radius is large andkis large, and individual distance calculations are very expensive, because the same probe-to-data-point distance calculations need to be executed repeatedly as the top of the tree is re-explored. Combining two acceleration techniques was found to improve the search time dramatically: (i) organizing the search into nested searches in non-overlapping annuli of increasing radii, using an estimation of the Hausdorff dimension applicable to this data instance from the results of earlier annuli to help set the radius of the next annulus; and (ii) caching all distance calculations involving the probe point to reduce the cost of repeated use of the same distances. The result of this acceleration in a search of the combined macromolecular and small-molecule data in a combined six-dimensional database of nearly 900 000 entries has been an improvement in the overall time of the searches by one to two orders of magnitude.

On the detection of point sources in Planck LFI 70 GHz CMB maps based on cleaned K-map

We use the Planck LFI 70 GHz data to further probe point source detection technique in the sky maps of the cosmic microwave background (CMB) radiation. The method developed by Tegmark et al. for foreground reduced maps and the Kolmogorov parameter as the descriptor are adopted for the analysis of Planck satellite CMB temperature data. Most of the detected points coincide with point sources already revealed by other methods. However, we have also found nine source candidates for which still no counterparts are known.

Study on the spatial generation of breakdown spots in MIM capacitors with different aspect ratios

Metal-insulator-metal (MIM) large area (>10-4 cm2) capacitors with different aspect ratios were subjected to severe stress conditions (Eox>4-5 MV/cm) with the aim of generating a large density of breakdown spots (from 105 to 106 spots/cm2) in the same device. The resulting mark pattern on the top metal electrode associated with the failure events was analyzed first using conventional functional estimators for two-dimensional spatial statistics. Second, as a double check, the attention was focused on the same breakdown spot patterns but in relation to the probe point location. In this latter case, the objective was to rule out any stochastic dependence of the breakdown spot distribution on the position of the source of degradation and therefore to confirm whether or not the spots follow a complete spatial randomness (CSR) process. In order to simplify the mathematical treatment of the point-to-event distributions, the voltage probe was assumed to be located at one corner of the observation window which significantly reduces the number of cases to analyze. Infrared images revealed that the generation of the spots is associated with micro-explosions within the insulating material (HfO2) and with the local volatilization of the top metal electrode (Pt).