Discovering formation of ice 0 by low-frequency measurement data

In this work, low-frequency characteristics of wetted nanoporous silicate materials were measured, as well as the specimen’s own low-frequency electric fluctuations at the frequencies of 1…100 Hz. The measurements at low frequencies were conducted at different voltages of the probing signal. A capacity cell was used in making the measurements. In the experiments, at the temperatures below –25…–30 °C, non-linearity of the medium was discovered. The experiments on the study of the specimen’s own electric fluctuations at these temperatures revealed their essential increase. These temperatures are below the point of phase transition of supercooled water to recently discovered ferroelectric ice 0. Based on the measurements made, a conclusion was made regarding formation of this modification of ice in the nanosize pores of the wetted materials under study. Ice 0 is a ferroelectric; therefore, its formation from deeply supercooled water may have a significant impact on the electric parameters of wetted bodies at the temperatures below –23 °C. At the interface of such ice with another dielectric, a thin layer with practically metallic conductivity emerges. Such a layer influences not only the non-linear dependence of dielectric permittivity on the electric field but also increases attenuation of electromagnetic radiation in a medium.

Research and Application of Rod Pump Working Condition Diagnosis and Virtual Production Metering Based on Electric Parameters

The conventional configurations of pumping well IOT consist of electric parameter indicator and dynamometer. The current, voltage, power, and other electrical parameters are easy to access, low costs, stable, and acquired daily during pumping well operation. If the working condition diagnosis and virtual production metering of pumping well can be realized through electrical parameters, the utilization of dynamometers can be cancelled or reduced, which is of great significance to reduce the investment and improve the coverage of IOT in oil wells. The conventional methods of diagnosis and analysis based on electrical parameters and virtual production metering are lack of theoretical basis. The combination of deep learning technology of big data and traditional methods will provide solutions to solve related technical problems. Considering that there are many energy transmission segments from the motor to the downhole pump, the characteristics of the electric parameter curve are more sophisticated and difficult to identify compared with dynamometer card due to the influence of the unbalance, pump fullness, rod/tube vibration, wax deposition and leakage. The shape characteristics of the electric parameter curve of the pumping well are analyzed in the time domain and frequency domain, which provides the basis for further diagnosis, analysis and production measurement. In this paper, an integrated multi-model diagnosis method is proposed. For the working conditions with a large scale of samples, the electrical parameters are converted to dynamometer cards for diagnosis by using the deep learning technology of big data. For the working conditions with sparse samples, the machine learning model is used to diagnosis directly with electrical parameters. The deep learning electric parameter model for production measurement is established. Through the combination of the big data model of electric parameters to dynamometer card, 3D mechanical model of rod string, and big data model of plunger leakage coefficient, the virtual production metering function of pumping well based on electrical parameters is successfully realized. The diagnosis and virtual production metering method and software based on electrical parameters have been applied in many oilfields of CNPC. The accuracy of identifying the upper and lower dead points of electric parameters is 98.0%; the coincidence rate of working condition diagnosis under electrical parameters is 92.0%; the average error of virtual production metering with electric parameters is 13.4%. The dynamometer and gauging room have been canceled in the demonstration area. The application of electrical parameters to diagnose working conditions and meter the production of pumping wells is the key to the low-cost IOT construction. Traditional mathematical and physical methods are difficult to solve this problem, but the application of big data analysis technology could do the job successfully.

Study of electric parameters of thin porous anodic alumina layers

Currently, the study of the electric parameters of porous anodic alumina (PAA) layers is of interest for sensor applications (humidity, DNA, etc.). PAA layers are synthesized using electrochemical anodizing of aluminum foil in potentiostatic mode with an aqueous solution of sulfuric acid and glycerin as an electrolyte. The surface morphology of the layers was studied by atomic force microscopy. The electric characteristics were studied using impedance spectroscopy at room temperature and under heating. An increase in the impedance of the heat-treated PAA sample was found, as well as an increase in the impedance with an increase in the measurement temperature. The results are explained by the influence of adsorbed water molecules on the electric characteristics of porous layers.

Reliability and Energy Costs Analysis of a Rural Hybrid Microgrid Using Measured Data and Battery Dynamics: A Case Study in the Coast of Perú

Hybrid microgrids constitute a promising solution for filling the electricity access gap that currently exists in rural areas; however, there is still relatively little information about their reliability and costs based on measured data in real working conditions. This article analyzes data obtained from the operation of a 9 kW hybrid microgrid in the fishermen’s cove of Laguna Grande, Paracas, in the Ica region of Perú, which has been running for 5 years. This microgrid has been equipped with data acquisition systems that measure and register wind speed, solar radiation, temperatures, and all the relevant electric parameters. Battery dynamics considerations are used to determine the depth of discharge in a real-time operative situation. The collected data are used to optimize the design using the specialized software HOMER, incorporating state-of-the-art technology and costs as a possible system upgrade. This work aims to contribute to better understanding the behavior of hybrid rural microgrids using data collected under field conditions, analyzing their reliability, costs, and corresponding sensitivity to battery size as well as solar and wind installed power, as a complement to a majority of studies based on simulations.

Electrically controlled spin polarized current in Dirac semimetals

We propose a highly tunable 100% spin-polarized current generated in a spintronics device based on Dirac semimetal under a magnetic field, which can be achieved merely by controlling electric parameters, i.e. the gate voltage, the barrier in the lead and the coupling strength between the leads and Dirac semimetal. These parameters are all related to the special properties of Dirac semimetal and Weyl semimetal. The spin polarized current generated by gate voltage is guaranteed by its semimetallic feature, because of which the density of state vanishes near Dirac nodes. The barrier controlled current results from the different distance of Weyl nodes generated by the Zeeman field. And the coupling strength controlled spin polarized current originate from the surface Fermi arcs. All these features make a great potential to realized Dirac semimetal based spintronic devices.

Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study

Increasing the energy efficiency of machining operations can contribute to more sustainable manufacturing. Therefore, there is a necessity to investigate, evaluate, and optimize the energy consumed during machining operations. The research highlights a method employed to prioritize the most energy-intensive machining operation and highlights the significance of electric parameters as predictors in power estimation of machining operations. Multi regression modeling with standardized regression weights was used to identify significant power quality predictors for active power evaluation for machining operations. The absolute error and the relative error both decreased when the active power was measured by the power analyzer for each of the identified machining operations, compared to the standard power equation and that obtained from the modeled regression equations. Furthermore, to determine energy-intensive machining operation, a hybrid decision-making technique based on TOPSIS (a technique for order preference by similarity to ideal solution) and DoM (degree of membership) was utilized. Allocation of weights to energy responses was carried out using three methods, i.e., equal importance, entropy weights, and the AHP (analytical hierarchy process). Results revealed that a drilling process carried out on material ST 52.3 is energy-intensive. This accentuates the significance of electric parameters in the assessment of active power during machining operations.

DETECTION OF POTENTIALLY UNRELIABLE VLSI BASED ON THE ANALYSIS OF TEMPERATURE-SENSITIVE PARAMETERS

The paper discusses methods for non-destructive diagnostic testing of very large scale integration circuits (VLSI) based on the “junction-case” thermal resistance parameter. This parameter is important because VLSI’s failure rate depends on junction temperature, which in turn depends on thermal resistance “junction-case”. There are three known methods for detecting potentially unreliable VLSIs with increased thermal resistance value: 1) non-destructive measurement of thermal resistance; 2) scanning acoustic microscopy; 3) an approach based on the statistical analysis of temperature-sensitive electric parameters. The paper presents advantages and disadvantages of each method. Special attention is paid to statistical analysis of temperature-sensitive electric parameters because this method allows detecting of potentially unreliable VLSIs without using expensive equipment. This method does not require changes in existing measurement programs. Electric parameters, which depend on temperature, are temperature-sensitive parameters. These parameters are useful for detecting VLSIs with deviations from the main batch. This allows decreasing of risk of potentially unreliable VLSIs application in high reliable equipment. With the proposed approach the high reliable equipment lifetime can be increased.

Characterization of Soil and Rock Hosting an Aquifer in Southeast Panama City using Geotechnical, Geophysical and Geochemical Parameters

The aim of the present study is to assess the combined use of geotechnical and electrical geophysical methods to determine water quality and rocks mechanics in an aquifer. The aquifer studied is located in the Tocumen sector of Panamá City, located to the southeast of city, where there is a need to study the possible use of this aquifer to provide drinking and/or irrigation water based on its quality. To this end, a 10 m well was perforated and sampled to characterize the host soil and rock trough granulometry, determine the Atterberg limits, measure the physicochemical parameters and perform a chemical analysis, including reactivity (pH), electrical conductivity (EC), organic matter content, cation exchange capacity, calcium carbonate, sulfates, chlorides, SiO2, Al2O3, Fe2O3, CaO, MgO, SO3, Na2O and K2O. In addition, a 2D electrical resistivity tomography profile was conducted in order to correlate the electric parameters with the physicochemical and chemical ones and extend them laterally to check the continuity of the characteristics measured. The results show a good correlation between geotechnical, geophysical and chemical parameters, thus highlighting the presence of discontinuities that must be overcome by infiltrated rainwater to reach the deepest levels, which are characterized by the presence of water. The water chemistry varies with depth, with sodium bicarbonated water being the predominant facies.

Physics and biophysics fundamentals of transcranial stimulation

Transcranial stimulation induces or modulates neural activity in the brain through basic physical and biophysical processes. Transcranial electrical stimulation and transcranial magnetic stimulation impose an exogenous electric field in the brain that is determined by the stimulation device and the geometric and electric parameters of the head. The imposed electric field drives an electric current through the brain tissue, which macroscopically behaves as a volume conductor. The electric field polarizes neuronal membranes as described by the cable equation, resulting in direct activation of individual neurons and neural networks or indirect modulation of intrinsic activity. Computational modeling can estimate the delivered electric field as well as the resultant responses of individual neurons. This dosimetric information can be used to optimize and individualize stimulation targeting. The field distributions of transcranial stimulation are well understood and characterized, whereas analysis and modeling of the neural responses require further investigation, especially at the network level.