Electrical Conductivity Distribution Research Articles

Geostatistical Inversion of Frequency-Domain Electromagnetic Data for Near Surface Modelling

The detailed characterization of near-surface deposits is important for both environmental and economic reasons. These shallow subsurface systems can be very complex and heterogenous due to natural dynamics and anthropogenic interferences. Modeling techniques based exclusively on direct sampling generate limited informed three-dimensional models of the near-surface. Geophysical methods provide valuable and additional information to model the spatial distribution of the near-surface for locations where direct observations are not available. From this set of methods, frequency-domain electromagnetic induction (FDEM) has been successfully applied to image complex near-surface deposits. Yet, predicting the spatial distribution of relevant subsurface properties from geophysical data, and the integration of direct observations, is not straightforward. It requires solving a challenging geophysical inversion problem. Geostatistical modelling tools have been effectively applied to couple direct observations with geophysical data such as seismic reflection. We propose an iterative geostatistical FDEM inversion method able to integrate data from direct measurements of the near-surface with surface loop-loop FDEM measurements to simultaneously predict high-resolution models of electrical conductivity and magnetic susceptibility, and their associated uncertainty. The iterative geostatistical inversion method is based on stochastic sequential simulation and co-simulation as model perturbation and update techniques. The iterative optimization is based on the local data misfit between observed and simulated FDEM data, weighted by the sensitivity of the acquisition equipment. The proposed method is first demonstrated for a synthetic landfill data set created based on real data collected at a mine tailing disposal site in Portugal, and on a real data set collected at a site with archaeological features in Knowlton, UK. The results show the ability of the proposed method to accurately predict and characterize the spatial distribution of electrical conductivity and magnetic susceptibility down to the depth of interest while reproducing the recorded FDEM data.

Mapping and Assessment of Within-field Spatial Variability of Soil pH, Electrical Conductivity, and Particle Size Distribution to Delineate Management Zones

Mapping and Assessment of Within-field Spatial Variability of Soil pH, Electrical Conductivity, and Particle Size Distribution to Delineate Management Zones

Estimation of electrical conductivity models using multi-coil rigid-boom electromagnetic induction measurements

Electromagnetic induction measurements from multi-coil configuration instruments are used to obtain information about the electrical conductivity distribution in the subsurface. The resulting inverse problem might not have a unique and stable solution. In that case, a local inversion method can be trapped in a local minimum and lead to an incorrect solution. In this study, we evaluate the well-posedness of the inverse problem for two and three-layered electrical conductivity models. We show that for a two-layered model, uniqueness is ensured only when both in-phase and quadrature data are available from the measurements. Results from a Gauss–Newton inversion and a lookup table demonstrate that the solution space is convex. Furthermore, we demonstrate that for even a simple three-layered model, the data contained in such measurements are insufficient to reach a correct or stable solution. For models with more than 2 layers, independent prior information is necessary to solve the inverse problem. The insights from the numerical examples are applied in a field case.

Characterizing preferential infiltration of loess using geostatistical electrical resistivity tomography

Preferential infiltration is prevalent in loess and is pivotal in disasters such as landslides. However, the inherent multi-scale heterogeneity of loess makes traditional in-situ monitoring techniques challenging for characterizing the preferential infiltration process. This study employed the Geostatistical Electrical Resistivity Tomography (GERT) to examine the spatial distribution of mass water content (ωs) in loess strata to understand the preferential infiltration processes in loess. This study improved stimulus-response data quality and used GERT to characterize the spatial-temporal distribution of electrical conductivity (σ) and estimate ωs through the σ-ωs relationship. This study indicates that the surface loess layer has higher ωs than deeper layers, rapidly declining as depth increases. Under preferential infiltration, early-stage water forms a spherical saturated zone, transforming into an ellipsoid as it descends. Using equivalent homogeneous parameters as prior information effectively improved σ estimation. This study found a 15.9% error in the total change in water content based on the GERT survey compared to the known amount of injected water. It explores the possible impacts of the uncertainty of the unknown relationship between σ and ωs, its spatial variability, and the accuracy of the survey on the error. The formation of an electric double-layer structure within the loess as the saturation increases, which reduces the sensitivity of GERT to changes in water content, is likely another cause. Finally, these areas are essential for advancing future studies on applying geophysical tools to accurately estimate water distributions in loess formations.

Groundwater flow paths using combined self-potential, electrical resistivity, and induced polarization signals

SUMMARY The dam of Lampy (Black Mountain, Aude, France) is considered as one of the oldest dams in France. A geophysical survey is performed to better understand the pattern of groundwater flow downstream of this dam in the granitic substratum. Induced polarization is first used to image both electrical conductivity and normalized chargeability. Eight core samples of granite from this site are measured and analysed in the laboratory. Their electrical conductivity and normalized chargeability are expressed as a function of the porosity and cation exchange capacity (CEC). The field data and the petrophysical results are used to image the water content, the CEC and the permeability distribution of the substratum. Then, self-potential is used as a complementary passive geophysical technique, which, in absence of metallic bodies, is directly sensitive to groundwater flow through the so-called streaming potential effect. Indeed, the excess of electrical charges in the vicinity of the solid grains, in the so-called double layer, is dragged by the ground water flow generating in turn an electrical (streaming) current and therefore an electrical field. A map of the resulting self-potential signals is done over the area covered by the induced polarization profiles. This map shows a large positive anomaly with an amplitude of ∼80 mV possibly associated with upwelling groundwater in an area where the soil is water-saturated. A groundwater flow simulation is performed to model this anomaly. This is done in two steps. A preliminary groundwater flow model is built using the permeability and water content distributions obtained from the induced polarization data. Then, this groundwater flow model is updated using the information contained in the self-potential data including the electrical conductivity distribution obtained through resistivity tomography. The algorithm for the inversion of the self-potential data is validated through a 2-D numerical test. This analysis yields a groundwater flow model with the flow being focused through a high permeability zone. This study shows how three geoelectrical methods (self-potential, induced polarization and electrical resistivity) can be efficiently combined to image groundwater flow in the vicinity of a dam.

Transient characteristics of ultra-high frequency adjustable multi-pulse gas tungsten welding arc

In response to the shortcomings of traditional Gas Tungsten Arc Welding (GTAW), such as low penetration, slow welding speed, and low production efficiency, an Ultra-High-Frequency Adjustable Multi-Pulse Gas Tungsten Arc Welding (UFMP-GTAW) process is proposed. This process more effectively concentrates the temperature distribution of the welding arc. To characterize the arc morphology and its dynamic changes, high-speed photography captured ten images of the UFMP-GTAW welding arc within one cycle. The arc image processing algorithm proposed in the paper was used to study the arc's variation characteristics over one cycle. This algorithm includes binarization, arc segmentation, and edge detection to obtain arc morphology parameters. The arc symmetry algorithm is used to symmetrize the arc images for Abel inversion, resulting in the emission coefficient distribution. Further calculations provide the temperature distribution, electrical conductivity distribution, and current density distribution of the arc. Analysis reveals that changes in arc temperature, electrical conductivity, and current density lag behind changes in current. The temperature of the arc spreads from the central axis to the surrounding arc space. When high-frequency current variations occur, the heat input generated by the current increase takes time to transfer to the entire arc space. Conversely, if the current suddenly decreases, the heat input rapidly diminishes. However, because heat transfer takes time, the arc space remains relatively stable for a short period, making the ultra-high-frequency arc form more stable.

Capacitive deionization: Capacitor and battery materials, applications and future prospects

Water scarcity all over the world attracts alternative methods to purify saline water and supplement the available dwindling freshwater resources. Capacitive deionization (CDI) is hopeful to supply water to the population due to operation at low potential along with low energy expenditure when low salinity (5 mM NaCl) feed water solutions are desalinated. Electrode material is the main controlling factor in CDI system and a lot of efforts are devoted to develop excellent materials for better CDI performance. So far, carbon materials are widely used as the electrode for CDI, though limitations such as co-ion expulsion and faradaic reactions hinder their full utilization. Alternatively, battery materials are used since their performance is great due to mitigation of co-ions ejection as well as faradaic reactions. In 2019 our group reviewed factors affecting the performance of activated carbon electrode materials and revealed lack of selectivity, co-ion expulsion, low electrical conductivity and inappropriate pore size distribution to largely contribute to its low salt removal capacity [1]. Therefore, herein, we extend the discussion beyond AC to include other carbons such as aerogels, nanotubes, graphenes etc., and battery materials such as MXenes, sodium super ionic conductors, and BiOCl to mention the few. This article also discusses the extent CDI is applied in the laboratory scale as well as in the field for desalination of real water, wastewater remediation and removal of harmful contaminants to substantiate what it can offer beyond the laboratory experiments. The work is expected to save as a complete reference for the progress of CDI electrode materials and its applications in water purification.

Influence of Magnetic Polarization on Diagrams of Inductive and Electromagnetic Logs

Abstract ––We present the results of the study of the effect of induced magnetic polarization of clay beds under the influence of an external harmonic electromagnetic field (frequencies 70 and 875 kHz). A two-stage numerical modeling procedure is proposed. At the first stage we determine the effective relative magnetic permeability of a synthetic sample with inclusions of clay particles. In this case a 3D heterogeneous mesh sample is generated. Then we numerically model a spatial distribution of an electric field. The electromotive force (EMF) induced in the measuring coil is calculated from this distribution. Relative magnetic permeability is determined by comparison with EMF of homogeneous samples with different values of magnetic permeability. It has been found that during the electric field excitation by an alternating current coil, the effect of induced magnetic polarization appears in the sample with clay particles. Its manifestation is that the effective magnetic permeability becomes complex. At the second stage we calculate the EMF diagram of the three-coil logging probe in the macro-model ‘clay cap – reservoir’. Magnetic permeability of the clay cap is given by a complex value. In the generated logs, extremes appear in the vicinity of the bottom of the clay cap; they do not correspond to the distribution of electrical conductivity and magnetic permeability in the given model. They can be incorrectly interpreted when analyzing real logs into individual formations. Numerical modeling at all stages is performed by the Vector Finite Element Method on a consistent adaptive tetrahedral partition and the first-order Webb vector basis.

Groundwater recharge sources and mechanisms in the Ethiopian central Afar rift: Insights from isotopic and hydrogeochemical tracers.

Groundwater flow and recharge mechanisms in the central Afar rift and the associated western marginal grabens and Northwestern Plateau (NWP) are poorly understood because of the complex geology and spatial heterogeneity in the aquifer systems. The major aquifers in the region include the Oligocene and Pliocene to recent volcanics and recent alluvio-lacustrine sediments. We employed hydrogeochemical, stable isotopes of water (18O and 2H), and geological structures insights to identify groundwater recharge sources and mechanisms, aiming to develop a representative conceptual groundwater flow model. Water samples from groundwater and surface water were collected based on lithological/aquifer variation and geomorphological setup for isotope and hydrochemical analysis. Findings reveal distinct isotopic differences between the deep (>350 m) and shallow groundwater systems of the Afar rift, with deep systems showing relative isotopic depletion, indicating varying recharge sources. The deep groundwater exhibited a similar isotopic signature to that of the marginal grabens and NWP. The distribution of major ion chemistry and electric conductivity (EC) of groundwaters from the western plateau to the Afar rift show significant spatial variability. However, in some corridors of western parts of the rift, there is a clear geochemical evolution along the flow paths inferring groundwater flow continuity between aquifers. The results revealed the existence of preferential deep groundwater recharge from the NWP through the highly fractured linking zones, interacting fault zones in between the marginal grabens, to the deeper volcanic aquifers of the central Afar rift. The proposed groundwater flow model of this study illustrates the deep groundwater flow from the NWP to the Afar rift is primarily directed by the orientations of the faulted marginal grabens and the highly fractured interacting zones. This model is pivotal for understanding groundwater dynamics in similar continental rift environments, offering insights for effective groundwater management.

Predicting wetland soil properties using machine learning, geophysics, and soil measurement data

PurposeMachine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil properties based on geophysical measurements constrained by soil samples.Materials and methodsA spatial distribution of soil apparent electrical conductivity (ECa) and laboratory measurements of SOM and SMC from twenty-two core samples were acquired at the St. Michael restored wetland near Defiance, Ohio. The correlations between ECa, SOM, and SMC were explored for predicting the spatial distribution of SOM and SMC. We used a Beta Variational AutoEncoder (β-VAE) approach to synthetically generate over 70,000 training data from the original twenty-two data from soil cores. The training data samples were taken from the latent space. The XGBoost algorithm was then trained on the β-VAE generated data and used to predict the spatial distribution of SOM and SMC at the site. We also validated the accuracy of the XGBoost predictions using an original holdout model validation technique.Results and discussionsThe generated synthetic data using the β-VAE include both soil attributes and ECa, which are larger and more diverse than the original training set with an absolute mean reconstructed error for SMC and SOM ranging from 0.018 to 0.022 and 0.026 to 0.041, respectively. This indicates that the β-VAE successfully generated a realistic synthetic dataset and overcame the technical barrier of using limited datasets. In addition, using generated data to expand the original training data helps the XGBoost model make more accurate predictions compared to training on the original data. The XGBoost prediction performance yielded average Lin’s concordance correlation coefficient (LCCC) values of 0.82 and 0.85 for SOM and SMC and a ratio of performance to deviation (RPD) values of 1.92 and 2.22 respectively, indicating a good performance.ConclusionsThis study validated the use of β-VAE to successfully generate synthetic wetland soil datasets with attributes of the original field data that can be effectively used to train the machine learning XGBoost model. The proposed framework offers an efficient solution for mapping the spatial variability of soil property in data-scarce wetland soil environments.

Maxwell–Wagner effect and second harmonic generation in gradient structures

AbstractFor the first time, we have registered, studied, and modeled the Maxwell–Wagner effect in structures with a smooth gradient of charge carriers’ mobility. The Maxwell–Wagner charge, formed after applying a DC voltage to the gradient structure at room temperature, is distributed over the entire region of the gradient. The experiments were performed with slides of a soda‐lime glass subjected to sodium‐to‐potassium ion exchange, where the ions concentration gradient provides gradient of conductivity while dielectric permittivity stays the same. The electric field generated by the Maxwell–Wagner charge is concentrated in potassium‐enriched less conductive regions of the slide and is sufficiently high to induce the second order optical nonlinearity in the initially isotropic glass. The effect causes over 100‐fold increase in a second harmonic signal relative to the signal from the surface of a virgin glass. This phenomenon in graded‐concentration structures is perspective for characterizing interfacial charges, geometry of the gradient regions, and spatial distribution of electrical conductivity in micro‐ and optoelectronic semiconductor structures.

Directly depositing highly densified and uniform Cu-Cr pseudobinary alloy

Cu-Cr pseudobinary alloys have excellent electrical properties and are widely used in many industries. Laser additive manufacturing can effectively avoid the shortcomings of traditional methods for preparing Cu-Cr alloys. However, the high reflection of Cu powder to laser energy brings difficulties to industrial practice. The assembled Cu powder is used as raw material to absorb more laser energy through the outer layer of Cr powder, thereby improving the melting efficiency of the powder and the quality of the molten pool. Laser remelting of unmelted Cr powder can inhibit the uneven nucleation and growth of precipitated Cr, and improve the uniformity of electrical conductivity and element distribution. This study provides a new idea for the preparation of high-performance Cu alloys.

Magnetotelluric Observations in the Caspian Sea

Abstract —To study the geological structure in the search for raw hydrocarbons within the Volga delta and the Caspian Sea, two intersecting profiles of magnetotelluric sounding were performed. The analysis of geoelectric sections based on the one-dimensional inversion of the initial and normalized invariant curves of apparent resistance showed that it is necessary to use methods of three-dimensional mathematical modeling to form reliable geoelectric models. Their initial construction necessary for the three-dimensional interpretation of invariant apparent resistance curves was carried out taking into account their one-dimensional inversion. The resulting model, including the lower structural part, is constructed by the method of interactive matching to the apparent resistance curves of model curves calculated according to the program of three-dimensional mathematical modeling. This approach made it possible to take into account the influence of local galvanic distortions on the apparent resistance curves when evaluating the distribution of electrical conductivity in the lower parts of the subsea deposits. As a result of the integrated interpretation of magnetotelluric data, blocks with increased conductivity have been identified in the subsea deposits of the Northern Caspian Sea, which are most likely associated with high fluid saturation of Cretaceous and Neogene sediments. Their position correlates with the regional discontinuous structures of the region.

The spatiotemporal heterogeneity of fertosphere hotspots impacted by biochar addition and the implications for NH3 and N2O emissions

The fertosphere, as the interfaces between fertilizer granular and soil particles, represents a key hotspot for nitrogen transformation processes, particularly for ammonia (NH3) and nitrous oxide (N2O) emissions. Understanding the heterogeneity of the fertosphere, especially when incorporating organic amendments like biochars, is crucial for predicting NH3 and N2O emissions after soil fertilization. In this study, we investigated the effects of three types of biochar (pristine, aged, and acid-washed biochar) on heterogeneity of fertosphere induced by localized urea application. pH-specific planar optodes were employed to visualize pH gradients in fertosphere hotspots with high spatial and temporal resolution. In addition, we conducted thorough measurements of the gradient distribution of electric conductivity (EC), mineral N, aqueous NH3 in soil and enzyme activities relevant to nitrification. Concurrently, NH3 and N2O emissions from the soil were continuously monitored at a high temporal resolution. Initially, urea-induced fertosphere exhibited significant NH3 emissions, primarily attributed to the pH elevation resulting from urea hydrolysis. However, after 6 days, NH3 emissions subsided, and there was a notable sharp increase in N2O emissions. Importantly, compared to urea application alone, the inclusion of pristine biochar led to a delay in soil pH decline with a 19% rise in NH3 emission. Aged biochar, characterized by a higher content of oxygen functional groups, demonstrated increased NH4+/NH3 adsorption capacity and enhanced ammonia monooxygenase (AMO) activity in soil, resulting in an 18% reduction in NH3 emission. While a slight decrease of 5% in NH3 cumulative emission was observed in the acid-washed biochar treatment. Notably, biochar could potentially promote nitrification-derived N2O emissions due to the accumulation of NH3 oxidation products (NH2OH). These findings could contribute to refining N transformation models for fertilized soils, and optimizing N fertilizer application strategies.

A layered multifunctional framework based on polyacrylonitrile and MOF derivatives for stable lithium metal anode

Composite Li metal anodes based on three-dimensional (3D) porous frameworks have been considered as an effective material for achieving stable Li metal batteries with high energy density. However, uneven Li deposition behavior still occurs at the top of 3D frameworks owing to the local accumulation of Li ions. To promote uniform Li deposition without top dendrite growth, herein, a layered multifunctional framework based on oxidation-treated polyacrylonitrile (OPAN) and metal-organic framework (MOF) derivatives was proposed for rationally regulating the distribution of Li ions flux, nucleation sites, and electrical conductivity. Profiting from these merits, the OPAN/carbon nano fiber-MOF (CMOF) composite framework demonstrated a reversible Li plating/stripping behavior for 500 cycles with a stable Coulombic efficiency of around 99.0% at the current density of 2 mA/cm2. Besides, such a Li composite anode exhibited a superior cycle lifespan of over 1300 h under a low polarized voltage of 18 mV in symmetrical cells. When the Li composite anode was paired with LiFePO4 (LFP) cathode, the obtained full cell exhibited a stable cycling over 500 cycles. Moreover, the COMSOL Multiphysics simulation was conducted to reveal the effects on homogeneous Li ions distribution derived from the above-mentioned OPAN/CMOF framework and electrical insulation/conduction design. These electrochemical and simulated results shed light on the difficulties of designing stable and safe Li metal anode via optimizing the 3D frameworks.

Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering

IntroductionThe mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. MethodsMulti-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.Results and discussionThe method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.

Temporal dynamics of water level and water sources analyses of karst tidal springs in Guilin, China

Abstract The hydrological dynamics and sources of karst tidal springs are difficult to capture and quantify due to fluctuations in their flow velocity. In this study, the Laolongshui (LLS) karst tidal spring in the Maocun underground river basin of Guilin City was taken as an example, with a 2-year continuous monitoring of the electrical conductivity, and water level, as well as water chemical analysis. The results showed the following: (1) discovered the variation pattern of LLS water level in different seasons, the water level fluctuates regularly on a daily scale, with a rise and fall time of 43.6 min after heavy rainfall in the rainy season, while in the dry season, it lasts for about 74–80 h. (2) Four peaks were extracted from the frequency distribution of electrical conductivity, representing the response of springwater under different rainfall conditions. (3) The annual average frequencies of the occurrence of P1, P2, P3, and P4 in terms of time are 53.82, 39.29, 6.18, and 0.72%, respectively. The results provide a new method for analyzing groundwater's source and dynamic changes in karst areas.

Time-Lapse Electromagnetic Conductivity Imaging for Soil Salinity Monitoring in Salt-Affected Agricultural Regions

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using electromagnetic induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated soil apparent electrical conductivity (ECa) measurements were taken using an electromagnetic conductivity meter (CMD2) and inverted (using a time-lapse inversion algorithm) to generate electromagnetic conductivity images (EMCIs), representing soil electrical conductivity (σ) distribution. This process involved converting EMCI data into salinity cross-sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated accurate prediction ability of soil salinity with an R2 value of 0.81. The soil salinity cross-sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmers’ income.

Modeling of blood flow in cerebral arterial circulation and its dynamic impact on electrical conductivity in a realistic multi-compartment head model

Background and ObjectiveThis study aims to assess the dynamic impact of non-Newtonian cerebral arterial circulation on electrical conductivity within a realistic multi-compartment head model. Evaluating this research question is crucial and challenging due to its relevance to electrophysiological modalities like transcranial electrical stimulation (tES), electro-/magnetoencephalography (EEG/MEG), and electrical impedance tomography (EIT). In these modalities, accurate forward modeling depends on the electrical conductivity, which is affected by complex tortuous vessel networks, limited data acquisition in Magnetic Resonance Imaging (MRI), and non-linear blood flow phenomena, including shear rate and viscosity in non-Newtonian fluid. MethodsTo obtain an approximation for the blood concentration, we first use Navier–Stokes equations (NSEs) to solve for the pressure and velocity of the blood in the major vessels. Then Fick's law is used to solve for the blood concentration in the tissues. Finally, Archie's law is used to estimate the electrical conductivity distribution based on the blood concentration. ResultsThe results, obtained with an open 7 Tesla MRI dataset, suggest that a dynamic model of cerebral blood flow (CBF) for both arterial and microcirculation can be established; we find blood pressure and electrical conductivity distributions given a numerically simulated pulse sequence and approximate the blood concentration and electrical conductivity inside the brain based on those. ConclusionsOur model provides an approximation of the dynamical blood flow and the corresponding electrical conductivity distribution in the different parts of the brain. The advantage of our approach is that it is applicable with limited a priori information about the blood flow and with an arbitrary head model distinguishing the arteries.

Using Magnetic Field Analysis for Localizing Defects of Fuel Cell & Electrolyzer Components

Production capacities of electrolysis and fuel cell technologies are expected to increase significantly in the upcoming years, reducing costs of stack components through numbering-up. To ensure reliability and durability and reduce costs along the manufacturing process, developing suitable in-line non-destructive diagnostic methods for quality control becomes essential for their industrialization.In both fuel cell and electrolyzer stacks, the main components' electrical resistance and current density distribution greatly influence stack efficiency and longevity. Here, the flowing electric current generates a magnetic field depending on its strength and direction. Potential defects caused by the manufacturing process, including cracks, defective welding spots, coating adhesion issues, delamination, and contamination, influence the components' electric conductivity and current density distribution and hence the magnetic field distribution.This work investigates non-destructive diagnostic methods using magnetic field analysis (MFA) to identify changes in the magnetic field distribution and, thus, local electrical resistance and current density distributions of fuel cell and electrolyzer components. The methods used are magnetic field imaging (MFI) and Eddy Current Testing (ECT). MFI analyzes the magnetic field distribution in all three spatial directions to trace electrical currents and identify electrical defects, creating a magnetic field signature that can potentially distinguish between functional and non-functional components along the manufacturing process. ECT measures variations in local conductivity by inducing eddy currents in the sample through an alternating magnetic field generated by a sender coil. These eddy currents create a detectable magnetic field in a receiver and are affected by defects and changes in electrical conductivity. Therefore, the method can characterize sheet resistance and material homogeneity through electrical anisotropy in electrically conductive components.In initial experiments, ECT measurements on coated and uncoated hollow embossed stainless steel bipolar half plates showed systematic differences depending on quality characteristics resulting in unique patterns and inhomogeneities which require further investigation. Furthermore, experiments with both methods on other stack components will be presented and discussed. In addition, their in-line capability will be assessed and evaluated. However, these first promising results imply that MFA is potentially useful as an in-line, non-destructive diagnostic method for quality control.