Conductivity Distribution Research Articles

Improvement of the DC electrical performance of silicone rubber for cable accessories through aromatic hydrocarbon voltage stabilizer

AbstractThe development of high voltage direct current (DC) cables poses new challenges to insulation. The electrical properties of silicone rubber (SiR) limit its application as cable accessories at high voltage levels. Therefore, this article attempted to add two kinds of voltage stabilizers with high electron affinity into the SiR to improve its DC electrical properties. The preferred addition percentage was initially determined by the breakdown strength. Properties such as conductivity, dielectric losses, and space charge distribution were also measured. The results showed that the DC electrical properties of organically modified SiR had improved comprehensively. There might be two reasons. First, the voltage stabilizer has a high electron affinity, which means it can trap high‐energy electrons, thus reducing the number of carriers and mobility. In addition, it has an electric field gradient effect, which can homogenize the electric field by migration.

Goos–Hänchen and Imbert–Fedorov shifts in tiltedWeyl semimetals

We establish the beam models of Goos–Hänchen (GH) and Imbert–Fedorov (IF) effects in tilted Weyl semimetals (WSMs), and systematically study the influences of Weyl cone tilting and chemical potential on the GH and IF shifts at a certain photon energy 1.96 eV. It is found that the GH and IF shifts in tilted type-I and type-II WSMs are both almost symmetric about the Weyl cone tilting. Meanwhile, the GH and IF shifts in type-I WSMs almost do not change with the tilt degree of Weyl cones, while those in type-II WSMs are extremely dependent on tilt degree. These trends are mainly due to the nearly symmetric distribution of WSMs conductivities, where the conductivities keep stable in type-I WSMs and gradually decrease with tilt degree in type-II WSMs. By adjusting the chemical potential, the boundary between type-I and type-II WSMs widens, and the dependence of the beam shifts on the tilt degree can be manipulated. Furthermore, by extending the relevant discussions to a wider frequency band, the peak fluctuation of GH shifts and the decrease of IF shifts occur gradually as the frequency increases, and the performance of beam shifts at photon energy 1.96 eV is equally suitable for other photon frequencies. The above findings provide a new reference for revisiting the beam shifts in tilted WSMs and determining the types of WSMs.

Scalp-Mounted Electrical Impedance Tomography of Cerebral Hemodynamics.

An Electrical Impedance Tomography (EIT) system has been developed for dynamic three-dimensional imaging of changes in conductivity distribution in the human head, using scalp-mounted electrodes. We attribute these images to changes in cerebral perfusion. At 100 frames per second (fps), voltage measurement is achieved with full-scale signal-to-noise ratio of 105 dB and common-mode rejection ratio > 90 dB. A novel nonlinear method is presented for 3-D imaging of the difference in conductivity distribution in the head, relative to a reference time. The method achieves much reduced modelling error. It successfully localizes conductivity inclusions in experimental and simulation tests, where previous methods fail. For > 50 human volunteers, the rheoencephalography (REG) waveform is observed in EIT voltage measurements for every volunteer, with peak-to-peak amplitudes up to approx. 50 μVrms. Images are presented of the change in conductivity distribution during the REG/cardiac cycle, at 50 fps, showing maximum local conductivity change of approx. 1% in grey/white matter. A total of 17 tests were performed during short (typically 5s) carotid artery occlusions on 5 volunteers, monitored by Transcranial Doppler ultrasound. From EIT measurements averaged over complete REG/cardiac cycles, 13 occlusion tests showed consistently decreased conductivity of cerebral regions on the occluded side, and increased conductivity on the opposite side. The maximum local conductivity change during occlusion was approx. 20%. The simplicity of the carotid artery intervention provides a striking validation of the scalp-mounted measurement system in imaging cerebral hemodynamics, and the REG images indicate its unique combination of sensitivity and temporal resolution.

Distributed Strain Monitoring Using Nanocomposite Paint Sensing Meshes.

Strain measurements are vital for monitoring the load-bearing capacity and safety of structures. A common approach is to affix strain gages onto structural surfaces. On the other hand, most aerospace, automotive, civil, and mechanical structures are painted and coated, often with many layers, prior to their deployment. There is an opportunity to design smart and multifunctional paints that can be directly pre-applied onto structural surfaces to serve as a sensing layer among their other layers of functional paints. Therefore, the objective of this study was to design a strain-sensitive paint that can be used for structural monitoring. Carbon nanotubes (CNT) were dispersed in paint by high-speed shear mixing, while paint thinner was employed for adjusting the formulation’s viscosity and nanomaterial concentration. The study started with the design and fabrication of the CNT-based paint. Then, the nanocomposite paint’s electromechanical properties and its sensitivity to applied strains were characterized. Third, the nanocomposite paint was spray-coated onto patterned substrates to form “Sensing Meshes” for distributed strain monitoring. An electrical resistance tomography (ERT) measurement strategy and algorithm were utilized for reconstructing the conductivity distribution of the Sensing Meshes, where the magnitude of conductivity (or resistivity) corresponded to the magnitude of strain, while strain directionality was determined based on the strut direction in the mesh.

Sustainable glass foams produced with stone waste as a pore-forming agent: Assessing the role of heating rate in foamability and glass foams recyclability

This work provides insights into the role of heating rate in the foaming ability of glass foams produced from discarded glass bottles. The pore-forming agents used were calcium carbonate and ornamental stone processing waste (SW), the latter being assessed for the first time for this purpose. Glass foams were produced with different contents of pore-forming agents: 1-5 wt% CaCO3 and 1-15 wt% SW. Higher SW contents were necessary due to the relatively lower concentration of carbonate minerals contained (about 15 wt% of dolomite). The powdered raw materials were homogenized and uniaxially pressed (20 MPa) to obtain cylindrical samples, which were dried (110 °C/24 h) and fired at 900 °C with a holding time of 30 min. The effect of different heating rates on the foamability of samples, in the range of 3–20 °C/min, was investigated. The samples were compared for volumetric expansion, bulk density, porosity, thermal conductivity, compressive strength, and pore size distribution profile. The optimized contents of pore formers were in the range of 1-3 wt% CaCO3 and 5-10 wt% SW, producing foams with properties compared to commercial ones, including porosities between 75% and 90%, thermal conductivities between 0.04 and 0.07 W/m.K, and compressive strength between 1.0 and 4.3 MPa. The thermal conductivity was shown to be highly dependent on the total porosity of the samples and little influenced by the pore size, while the compressive strength was shown to be strongly influenced by both. The variation in the heating rate proved to be less influential for the samples with the above-mentioned optimized pore-formers contents. However, for contents close to the lower limit (1 wt% CaCO3 and 5 wt% SW), heating rates equal to or greater than 10 °C/min are recommended to improve the foamability. Preliminary results of the foam crystallization effect after sintering were also presented. Although the bulk crystallization of soda-lime glass is widely known, the literature on its crystallization in powder form is little explored. The crystallization of glass foams plays an important role in the recyclability of waste generated in its processing into different formats, since foamability is lost. The results show the crystallization of quartz, devitrite, and wollastonite in all samples. Although pore-forming agents do not appear to act as nucleating agents, they do favor the crystallization of devitrite.

Random Characteristics of Hydraulic Gradient through Three-Dimensional Multilayer Embankment

The distribution characteristics of hydraulic gradient in embankment are closely related to seepage failure. Seepage failures such as flowing soil and piping will lead to serious damage and even the overall failure of embankment. The hydraulic conductivity has strong spatial variability, which changes the distribution of hydraulic gradient in embankment and increases the difficulty for predicting the embankment seepage instability. In this study, the distribution of soil hydraulic conductivity in a section of Shijiu Lake embankment was obtained by the permeability test. Based on Local Average Subdivision technique, a three-dimensional multilayer random field of embankment hydraulic conductivity was generated. Then, the mean and standard deviation of overflow point height and hydraulic gradient were calculated by the Monte Carlo method, which combined the generated three-dimensional random model and the deterministic analysis method of seepage field. Finally, the coefficient of variation (COV) of hydraulic conductivity (0.1, 0.3, 0.5, 0.7, 1.0, 2.0, and 3.0), the fluctuation scale in vertical direction (3 m) and the fluctuation scale in horizontal plane (3 m, 6 m, 12 m, 24 m, 36 m, and 48 m) were selected respectively for analyzing the random characteristics of embankment overflow point height and hydraulic gradient under the influence of different COV and fluctuation scale of embankment soil hydraulic conductivity.

Estimation method for layered ground thermal conductivity using genetic algorithm based on a 2-D heat transfer model

Estimation of ground thermal conductivity plays an important role in design of borehole heat exchanger (BHE) for ground source heat pump (GSHP) systems, and the ground is usually heterogeneous with layered thermal properties. Therefore, this study presented a novel estimation method for layered ground thermal conductivity by using distributed thermal response test (DTRT) and genetic algorithm. Firstly, a comprehensive new 2-D heat transfer model for coaxial BHE was built to simulate borehole fluid temperature profiles, it revealed that the distribution of ground thermal conductivity showed a significant influence on outlet temperature and temperature profiles. Then, based on the built model, the vertical distribution of ground thermal conductivity was estimated through DTRT data by using the genetic algorithm. It was found that the DTRT data at 2 h was effective to estimate the layered ground thermal conductivity by using the new 2-D heat transfer model. Furthermore, the estimated distribution of ground thermal conductivity was almost independent of numbers or thickness of sub-layer, and the largest difference in distributions of ground thermal conductivity among different layer thickness was less than 0.3 W/(m·K). Also, the estimation convergence showed independence with layer thickness, converged results could be obtained after 10 iterations for different layer conditions.

The Mapping of Irrigated Paddy Fields which were Polluted by Detergent Waste in Kolam Village, Percut Sei Tuan Sub-district, Deli Serdang District

Rice is the staple food of the Indonesian people and in some places polluted by waste. This research aim was to provide a survey and map on the effects of detergent waste (phosphate available), electrical conductivity and soil pH in Kolam Village, Percut Sei Tuan Subdistrict of Deli Serdang District and analysis of sample points to P available, electrical conductivity and soil pH. This research started in September 2017 until January 2018. The method used was Grid Free and P available analysis used Bray II method, analysis of Electrical Conductivity with conductivity meter and analysis of soil pH with a pH meter. The results showed that the P available distribution consisted of very low (18%), low (52%) and moderate (30%). The distribution of soil electrical conductivity was low and the pH of the soil was acidic. Based on the analysis of sample points on P available, Electrical Conductivity and soil pH showed that the farther the distances from the irrigation pollutant channel, the availability of P decreased (0.088 ppm / meter) but did not affect the pH and electrical conductivity.

Three‐dimensional inversion of semi‐airborne transient electromagnetic data based on finite element method

ABSTRACTThe semi‐airborne transient electromagnetic method is characterized by a large detection depth, high signal‐to‐noise ratio, fast acquisition rate, low cost, and ability to be used for exploration in scenarios with complex geological conditions. As a result, the semi‐airborne transient electromagnetic method has a great potential in the fields of mineral exploration, hydrological study and geohazard detection. However, most of the existing semi‐airborne transient electromagnetic data interpretation methods still rely on one‐dimensional forward modelling and inversion, which could lead to an incorrect geological interpretation. In this paper, we develop a three‐dimensional inversion for semi‐airborne transient electromagnetic data to recover a reliable subsurface conductivity distribution. The forward modelling is based on the effective vector finite element method with an unstructured tetrahedral mesh, which is capable of simulating complex geo‐electric structures and the topography. Through the numerical experiments of several three‐dimensional synthetic models, we study the influence of the number of excitation sources, flight mode and complex topography on the inversion results. The synthetic model studies reveal that: (1) by increasing the number of excitation sources, we can obtain a higher resolution conductivity image with less artefacts; (2) the detectability can be increased by flying along the terrain when the topography is complex; (3) the topography should be considered in the forward modelling and inversion to obtain a reasonable conductivity model using the semi‐airborne transient electromagnetic method; and (4) the three‐dimensional inversion still works when the inhomogeneity is present nearby the transmitter.

Effect of Stretching on Thermal Behaviour of Electro-Conductive Weft-Knitted Composite Fabrics.

This experiment presents a study carried out on the electric charge passing textiles for heat production in compression weft-knitted composite fabrics used for medical purposes. The aim was to flourish compression support of knitted structure with integrated highly sensitive metal (silver) coated polyamide multifilament yarns and to evaluate its heat origination attributes after stretching in different levels as well as changes of the temperature during the time. A flat double needle-bed knitting machine was utilized to fabricate the selected specimens together with elastomeric inlay-yarn incorporated into the structure for compression generation and silver coated polyamide yarn laid as ground yarn in a plated structure for heat generation. Six different variants depending on the metal coated yarn amount used and the fabric structure along with two types of the conductive yarn linear density were fabricated for this research work. Scanning electron microscope (SEM) images were preoccupied to show the morphology of conductive yarn and thermal pictures were captured to study the evenness of the heat over the surface of composite fabrics depending on conductive yarn distribution in the pattern repeat. The temperature profile of fabricated composite fabrics and comparison of the heat generation by specimens after stretching in different levels was studied

High-accuracy rebar position detection using deep learning–based frequency-difference electrical resistance tomography

Rebar corrosion is one of the most critical mechanisms causing structural deterioration in reinforced concrete structures. However, rebar corrosion assessment is difficult in that typical non-destructive testing methods have limitations in accurately detecting rebar positioning in concrete. This study presents high-accuracy rebar position detection using a deep learning–based electrical resistance tomography (ERT) technique. Two data sets were prepared as input data: (1) the original circular ERT images in a Cartesian coordinate system and (2) the transformed rectangular ERT images in a polar coordinate system. The proposed convolutional neural network (CNN) model successfully distinguished rebar position from ERT images. Most of the radial and angular positions of the rebar were accurately identified by the model, despite rebar's wide distribution of high conductivity in the raw ERT images. Notably, the detection performance clearly depended on the coordinate types in the ERT images, whether they were Cartesian or polar coordinates.

Damage detection in cementitious materials with optimized absolute electrical resistance tomography

Electrical resistance tomography (ERT) serves as a non-invasive, non-destructive, non-radioactive imaging technique. It has potential applications in industrial and biological imaging. This paper presents an optimized inverse algorithm, named Newton’s Constrained Reconstruction Method (NCRM), to detect damage in cementitious materials. Several constraints were utilized in the proposed algorithm to optimize initial parameters. The range and spatial distribution of conductivities within the sample were chosen as two main constraints. Two sets of numerical and a set of experimental voltage data were used to reconstruct conductivity distribution images based on this algorithm. To evaluate the quality of reconstructed images, two image quality evaluation indicators, correlation coefficient and position error were used. Results show that the proposed algorithm NCRM has the ability to enhance the reconstructed image quality with fewer artifacts and has better positioning accuracy.

A wavelet frame constrained total generalized variation model for imaging conductivity distribution

<p style='text-indent:20px;'>Electrical impedance tomography (EIT) is a sensing technique with which conductivity distribution can be reconstructed. It should be mentioned that the reconstruction is a highly ill-posed inverse problem. Currently, the regularization method has been an effective approach to deal with this problem. Especially, total variation regularization method is advantageous over Tikhonov method as the edge information can be well preserved. Nevertheless, the reconstructed image shows severe staircase effect. In this work, to enhance the quality of reconstruction, a novel hybrid regularization model which combines a total generalized variation method with a wavelet frame approach (TGV-WF) is proposed. An efficient mean doubly augmented Lagrangian algorithm has been developed to solve the TGV-WF model. To demonstrate the effectiveness of the proposed method, numerical simulation and experimental validation are conducted for imaging conductivity distribution. Furthermore, some comparisons are made with typical regularization methods. From the results, it can be found that the proposed method shows better performance in the reconstruction since the edge of the inclusion can be well preserved and the staircase effect is effectively relieved.</p>

Image Reconstruction of Conductivity Distribution With Combined L 1-Norm Fidelity and Hybrid Total Variation Penalty

Image reconstruction of conductivity distribution is a highly nonlinear ill-posed inverse problem in electrical impedance tomography (EIT). To solve the problem, Tikhonov regularization and total variation (TV) regularization methods have been generally adopted. However, reconstructed images suffer from excessive smoothness or staircase effect. In this article, a novel approach is proposed for imaging conductivity distribution. Unlike traditional regularization methods, fidelity term based on <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>-norm is introduced to stabilize the ill-posed problem and enforce sparsity in the solution. Moreover, a first-order and high-order TV combined hybrid penalty term is introduced to keep the sharp profile and restrain the staircase effect. During image reconstruction, regularization parameter and weighting factor are determined by the adaptive method. In addition, a soft-thresholding operator is introduced to remove artifact and enhance robustness to noise. Numerical simulations have been performed to validate the performance of the proposed approach against Landweber, Tikhonov, TV, high-order TV, and iterated soft-thresholding methods. The impacts of conductivity distribution, conductivity contrast, contact impedance, and boundary shape on reconstruction are studied. Besides, robustness to noise and computation time are evaluated. The performance of the proposed approach is also verified by phantom experiments. Simulation and experimental results demonstrate that the proposed approach performs well in image reconstruction of conductivity distribution. Clearer background is observed and inclusions with sharp or smooth edges can be better reconstructed.

Nonlinear Difference Imaging to Image Local Conductivity of Single-Layer Graphene Using Electrical Impedance Tomography

Single-layer graphene synthesized using the chemical vapor deposition (CVD) technique contains defects on the graphene surface that are formed during the growth and transfer process. These defects influence the graphene electrical characteristics, especially the conductivity distribution. Nondestructive characterization of graphene samples is therefore essential for developing single-layer graphene devices. Electrical impedance tomography (EIT), a low-cost nondestructive method with high temporal resolution characteristics, can be applied to obtain the local conductivity distribution. Due to the very high conductivity of the background and inaccurate knowledge of contact impedances, the conventional absolute imaging methods do not give desired conductivity reconstruction for graphene. This study uses a nonlinear difference imaging (NDI) approach to estimate the local conductivity changes across the graphene surface quantitatively. In NDI, the conductivity after the change is parameterized as a linear combination of initial and the conductivity change. The conductivity change restricted to the region of interest (ROI) is determined using the Otsu method. The NDI with Otsu method (NDIWO) estimates the initial distribution and conductivity changes simultaneously from the two datasets measured at different time intervals. NDIWO tolerates modeling errors to some extent and quantitively provides more accurate local conductivity changes. The feasibility of NDIWO is tested with numerical simulations and experiments with graphene wafer of size 2.5 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times2.5$ </tex-math></inline-formula> cm, and results show that the proposed method is successful in estimating the conductivity changes with better accuracy than conventional absolute and linear difference imaging.

A Flexible Image-Guided Shape Reconstruction Framework for Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) owns the advantages of safety, high temporal resolution and functional imaging characteristics, thus is considered as a promising medical/ industrial imaging modality. However, due to the ill-posedness of its inverse problem, the spatial resolution of EIT is low, which is greatly impeded its practical applications. A flexible image-guided shape reconstruction framework for EIT is proposed to alleviate the problem. A statistical inverse problem framework for image-guided inclusion boundary reconstruction is established to directly reconstruct the inclusion boundary and the corresponding conductivity distribution. The morphology prior information is extracted from the images obtained from other high resolution modes and is used to guide the boundary reconstruction process in the form of a conditional probability model. A series of simulation and experimental studies are carried out for different application backgrounds. The lung EIT imaging guided by CT image and EIT inclusion detection guided by B-ultrasound image are simulated and analyzed respectively. The results show that the proposed method has high applicability to prior images of different modes. It not only achieves high-precision shape reconstruction but also high-precision conductivity estimation under the guidance of accurate prior images. Meanwhile, the proposed method has high robustness to the possible influence of noise in prior images or incomplete structural information. Even if the information in the prior image is biased or incomplete, the proposed method can still achieve high precision boundary reconstruction and conductivity estimation. The results of quantitative analysis show that compared with the method without image prior, the proposed method has an average reduction of 70% in area error, while the conductivity estimation error is reduced by about 5 times on average.

Bayesian Inversion for Surface Magnetic Resonance Tomography Based on Geostatistics

Magnetic resonance tomography (MRT) has the advantages of direct, quantitative and unique interpretation in the field of groundwater detection. Currently, the inversion of MRT data primarily uses the QT (Q-time) inversion method based on Tikhonov regularization. However, when the heterogeneity of an aquifer is high, and the water content distribution is markedly uneven, this method removes many details in the model and cannot perform uncertainty analysis on the results. To solve these problems, we propose a Bayesian inversion for MRT data based on geostatistics. The method uses previously known geological data, such as drilling, to determine prior information model containing variograms and mixture Gaussian probability distributions for generating many stochastic realizations. Under the Bayesian framework, a modified Markov chain Monte Carlo strategy (MCMC) is used to obtain the posterior probability distributions of subsurface aquifers and hydraulic conductivity, and the results of quantitative uncertainty analysis. By comparing the inversion performance for simulated models, the imaging result of the Bayesian method is found to be markedly more accurate than that of the QT method for subsurface two-dimensional aquifers, particularly in explaining the stochastic model (i.e., the water-bearing model with an uneven distribution of water content). This method can also intuitively quantify the uncertainty of the imaging results, which mitigates the shortcomings of existing inversion methods. This paper also discusses the effects of prior information, number of chains and noise levels on the results, and also validates the effectiveness and practicability of the proposed method using field-measured data.

A Unified Singular Solution of Laplace’s Equation with Neumann and Dirichlet Boundary Conditions

Laplace’s equation is one of the important equations in studying applied mathematics and engineering problems including the study of temperature distribution of steady-state heat conduction or the concentration distribution of steady-state diffusion problems. In this study, the analytical method has been applied to solve the Laplace's equation in a two-dimensional domain. For the specified Neumann or Dirichlet boundary conditions, the analytical solution of temperature distribution in the quarter-plane can be found by several methods including the Fourier transform method, similarity method, and the method of Green’s function with images. For different boundary conditions, the solution of temperature distribution of the Laplace’s equation will be in a totally different form. Nevertheless, the merit of this research is that the solutions of steady-state temperature distribution in the quarter plane with Neumann and Dirichlet boundary conditions are unified under the singular similarity solution with source type singularity. With the typical benchmarked examples for finding the temperature distribution by the numerical integral method, it is shown that Gibbs phenomenon behaves at a jump discontinuity, where serious oscillation result was found especially near the singular points of the boundary. In addition, the temperature distribution in the domain can be easily calculated without oscillation phenomenon near the singular points from the similarity solutions.

Effect of ECAP processing on distribution of second phase particles, hardness and electrical conductivity of Cu−0.81Cr−0.07Zr alloy

The effect of equal-channel angular pressing (ECAP) processing at room temperature and 300 °C on the distribution of the second phase particles and its influence on hardness and electrical conductivity of the commercial Cu−0.81Cr−0.07Zr alloy were investigated. Microstructural characterization indicated that the area fraction of coarse Cr-rich particles decreased after ECAP processing. This reduction was attributed to the Cr dissolution induced by plastic deformation. The electrical conductivity of the alloy decreased by 12% after 4 ECAP passes at room temperature due to the increase of electrons scattering caused by higher Cr content in solid solution and higher density of defects in the matrix. These results were supported by the reduction of the Cu lattice parameter and by the exothermic reactions, during differential scanning calorimetry (DSC) analysis, observed only in the samples subjected to ECAP processing. Aging heat treatment after ECAP processing promoted an additional hardening effect and the complete recuperation of the electrical conductivity, caused by the re-precipitation of the partially dissolved particles. The better combination of hardness (191 HV) and electrical conductivity (83.5%(IACS)) was obtained after 4 ECAP passes at room temperature and subsequent aging at 380 °C for 1 h.

A study of thermal effect depending on temperature field adjacent to turbulent premixed flames using DNS

Turbulent premixed flames are heat release phenomena in the turbulent flow with premixed fuel and oxidiser. Turbulent premixed flames form the temperature field and are affected by the interaction between flow and heat. This study focused the temperature field adjacent to the flames and considered the effect of the interaction between flow and heat on the flames using DNS (Direct numerical simulation). In the development of planar flame in the turbulence field, the results show that spread of the flame zone leads to enhancement of the effect of turbulence field on the isosurface of temperature upstream of flames, and that the distribution of heat conduction does not deviate from the isosurface of temperature in the case of small wrinkles on the isosurface of temperature.