Inverse Modeling Approach Research Articles

Inverse modeling of electromagnetic emissions using grey wolf optimization

The paper proposes an inverse modeling approach for evaluating radiated electromagnetic compatibility (EMC) in electronic components and circuits. To imitate the electromagnetic behavior of real devices, the suggested methodology generates an equivalent model using elementary dipoles. The Grey Wolf Optimization (GWO) algorithm solves an inverse problem to identify dipole parameters such as locations, intensities, and orientations. This technique improves model accuracy and robustness through a two-phase exploration and exploitation process that allows for effective parameter tuning while reducing simulation time and computational resources. Recent research in near-field measurement techniques illustrates the importance of reliable predictive methodologies in EMC evaluation. This method provides significant information on the impacts of power systems on surrounding electronic circuits, making it a useful tool for EMC testing. It also promotes the development of immunity models for high-frequency applications. Overall, this methodology simplifies the examination procedure, resulting in higher design efficiency for a wide range of electronic systems.

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach

Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisfy a system of ordinary differential equations (ODEs) associated with a lumped parameter description of the circulatory system. The model predictions have a maximum error of less than 5% when compared to those obtained by solving the ODEs numerically. An inverse modeling approach using the PINN model is also developed to rapidly estimate model parameters (in ∼ 3 min) from single-beat LV pressure and volume waveforms. Using synthetic LV pressure and volume waveforms generated by the PINN model with different model parameter values, we show that the inverse modeling approach can recover the corresponding ground truth values for LV contractility indexed by the end-systolic elastance Ees with a 1% error, which suggests that this parameter is unique. The PINN inverse modeling approach is then applied to estimate Ees using waveforms acquired from 11 swine models, including waveforms acquired before and after administration of dobutamine (an inotropic agent) in 3 animals. The estimated Ees is about 58% to 284% higher for the data associated with dobutamine compared to those without, which implies that this approach can be used to estimate LV contractility using single-beat measurements. The PINN inverse modeling can potentially be used in the clinic to simultaneously estimate LV contractility and other physiological parameters from single-beat measurements.

Inverse modelling approach to assess air pollutant emission trends, and source contributions in highly polluted cities

Pollutant concentrations are regularly measured in many cities, but emission loads often remain unknown and are irregularly estimated. This paper presents a methodology to estimate total emissions in a highly polluted megacity using an inverse modeling approach. An inverted Fixed Box Model (FBM) has been used with pollutant concentrations and meteorological variables to estimate emissions of pollutants by discounting the meteorological variability from the measured concentrations. Model-predicted emissions are validated with existing inventories and then used to assess annual, monthly, and diurnal trends in emissions. The applicability of the model is demonstrated for Delhi. Trend analysis shows that pollutants are emitted in a unique pattern that also differs over time periods of consideration. Yearly trends in Delhi show that PM10, NOX, and SO2 emissions have increased by 33%, 4%, and 16%, respectively, during 2008–2018. However, due to controls, the emission intensities have been found to stabilize for PM10 and decrease for NOX and SO2 pollutants in Delhi. Monthly trends indicate that summer months have higher emission rates owing to intensive energy demands (in automobiles, power plants), dust storms, and increased re-suspension of dust due to higher wind speeds. However, due to dispersive meteorology, ambient concentrations are lower in summers. The inverse modeling shows that PM10 emissions are dominated by dust sources (73%), while PM2.5 has the largest contribution from transport sector emissions (34%). NOX and SO2 emissions in the city are contributed heavily by the transport (84%) and industrial (92%) sectors, respectively. The model helps in understanding temporal emission trends and source contributions, which can be useful to implement effective control measures and track the progress of control.

Accelerating the design of the effective surface of pressing tools with probabilistic inverse modeling approaches

Accelerating the design of the effective surface of pressing tools with probabilistic inverse modeling approaches

An Enhanced Reptile Search Algorithm for Inverse Modeling of Unsaturated Seepage Parameters in Clay Core Rockfill Dam Using Monitoring Data during Operation

The seepage characteristics of clay core walls are crucial for the seepage safety of core rockfill dams, with the permeability coefficient in the unsaturated zone being nonlinear. To accurately determine the unsaturated seepage parameters in clay core rockfill dams, this paper first proposes an enhanced reptile search algorithm (ERSA) by applying three improvement strategies: Arnold’s cat chaotic map, nonlinear evolutionary factor, and adaptive Cauchy–Gaussian mutation with variable weight. Then, by integrating the ERSA with the unsaturated seepage finite element method, an inverse modeling approach is developed. This approach is applied to an actual rockfill dam with operational monitoring data to determine the unsaturated seepage parameters of the clay core. Results indicate that the ERSA outperforms the original RSA in test functions, and the calculation results of the seepage parameters determined through inversion are consistent with the monitoring data, showing an overall mean absolute error of 1.086 m. The inverse modeling approach provides a valuable reference for determining unsaturated seepage parameters in similar clay core rockfill dams.

Grain boundary segregation for the Fe-P system: Insights from atomistic modeling and Bayesian inference

In this work we re-assess experimental data for grain boundary (GB) segregation of P in bcc Fe with thermodynamic and statistical methods. The data are based on Auger-Electron-Spectroscopy (AES) measurements which have provided P GB concentrations for various bulk contents and temperatures for ferrite. While in the previous investigations of this system a single-site McLean equation was used to extract segregation enthalpy and entropy, we employ multi-site segregation models as suggested by atomistic simulations. We use a recently introduced methodology based on inter-atomic potentials to calculate the segregation energy spectrum, as well as vibrational entropy and solute-solute interactions of P in polycrystalline Fe, thereby obtaining a three-dimensional distribution of energy, entropy and interactions. Using this trivariate distribution we predict P GB concentrations and compare them to the experimental measurements. Furthermore, we calibrate the parameters of physical models of increasing complexity to the experimental data with an inverse modeling approach based on Bayesian inference. While it is not possible to seamlessly link the results from AES to atomistic modeling, our investigation provides significant new insights for thermodynamic modeling of GB segregation. The vibrational entropy deduced from experiment differs from physics based atomistic simulations even when taking the spectral nature of energy, entropy and interactions into account. However, the correlations of the quantities are in good agreement when considering the trivariate model. Our investigation highlights the need for new and more accurate experimental datasets of GB segregation.

Substantial increase in perfluorocarbons CF4 (PFC-14) and C2F6 (PFC-116) emissions in China

The perfluorocarbons tetrafluoromethane (CF4, PFC-14) and hexafluoroethane (C2F6, PFC-116) are potent greenhouse gases with near-permanent atmospheric lifetimes relative to human timescales and global warming potentials thousands of times that of CO2. Using long-term atmospheric observations from a Chinese network and an inverse modeling approach (top-down method), we determined that CF4 emissions in China increased from 4.7 (4.2-5.0, 68% uncertainty interval) Gg y-1 in 2012 to 8.3 (7.7-8.9) Gg y-1 in 2021, and C2F6 emissions in China increased from 0.74 (0.66-0.80) Gg y-1 in 2011 to 1.32 (1.24-1.40) Gg y-1 in 2021, both increasing by approximately 78%. Combined emissions of CF4 and C2F6 in China reached 78 Mt CO2-eq in 2021. The absolute increase in emissions of each substance in China between 2011-2012 and 2017-2020 was similar to (for CF4), or greater than (for C2F6), the respective absolute increase in global emissions over the same period. Substantial CF4 and C2F6 emissions were identified in the less-populated western regions of China, probably due to emissions from the expanding aluminum industry in these resource-intensive regions. It is likely that the aluminum industry dominates CF4 emissions in China, while the aluminum and semiconductor industries both contribute to C2F6 emissions. Based on atmospheric observations, this study validates the emission magnitudes reported in national bottom-up inventories and provides insights into detailed spatial distributions and emission sources beyond what is reported in national bottom-up inventories.

An Inverse Modeling Approach for Retrieving High-Resolution Surface Fluxes of Greenhouse Gases from Measurements of Their Concentrations in the Atmospheric Boundary Layer

This study explores the potential of using Unmanned Aircraft Vehicles (UAVs) as a measurement platform for estimating greenhouse gas (GHG) fluxes over complex terrain. We proposed and tested an inverse modeling approach for retrieving GHG fluxes based on two-level measurements of GHG concentrations and airflow properties over complex terrain with high spatial resolution. Our approach is based on a three-dimensional hydrodynamic model capable of determining the airflow parameters that affect the spatial distribution of GHG concentrations within the atmospheric boundary layer. The model is primarily designed to solve the forward problem of calculating the steady-state distribution of GHG concentrations and fluxes at different levels over an inhomogeneous land surface within the model domain. The inverse problem deals with determining the unknown surface GHG fluxes by minimizing the difference between measured and modeled GHG concentrations at two selected levels above the land surface. Several numerical experiments were conducted using surrogate data that mimicked UAV observations of varying accuracies and density of GHG concentration measurements to test the robustness of the approach. Our primary modeling target was a 6 km2 forested area in the foothills of the Greater Caucasus Mountains in Russia, characterized by complex topography and mosaic vegetation. The numerical experiments show that the proposed inverse modeling approach can effectively solve the inverse problem, with the resulting flux distribution having the same spatial pattern as the required flux. However, the approach tends to overestimate the mean value of the required flux over the domain, with the maximum errors in flux estimation associated with areas of maximum steepness in the surface topography. The accuracy of flux estimates improves as the number of points and the accuracy of the concentration measurements increase. Therefore, the density of UAV measurements should be adjusted according to the complexity of the terrain to improve the accuracy of the modeling results.

Reduced Geostatistical Approach With a Fourier Neural Operator Surrogate Model for Inverse Modeling of Hydraulic Tomography

AbstractIn this study, we propose a new approach for inverse modeling of hydraulic tomography (HT) using the Fourier neural operator (FNO) as a surrogate forward model. FNO is a deep learning model that directly parameterizes the integral kernel in Fourier space to learn solution operators for parametric partial differential equations (PDEs). We trained a highly accurate FNO to learn the solution operators of PDEs in HT, which can efficiently generate multiple channels of hydraulic head fields corresponding to specific pumping tests in new parameter fields through a single forward pass. The trained FNO surrogate model is integrated with the reduced geostatistical approach (RGA) to provide a powerful tool, named RGA‐FNO, for inverse modeling of Gaussian random fields. RGA utilizes principal components to effectively reduce problem dimensions and encode geostatistical information. FNO benefits from deep learning automatic differentiation, enabling efficient backpropagation of gradients during inverse modeling. Our results show that RGA‐FNO can efficiently produce satisfactory estimations for random transmissivity fields with an exponential covariance function, which is non‐differentiable and visually non‐smooth. The RGA‐FNO application on steady‐state multi‐well HT is better than the single‐well transient pumping test for inverse modeling. The data required for inverse modeling applications of RGA‐FNO increases with the variance of the random transmissivity fields. FNO and other neural operator models have the potential to play an important role in groundwater modeling, especially in inverse modeling and experimental design, which often requires a large number of forward simulations.

Optimising groundwater recharge to counteract seawater intrusion through an inverse modelling approach and identifying efficient recharge structure locations

In semi-arid coastal urban regions, groundwater faces challenges from seawater intrusion due to reduced recharge and increased extraction. This study employs advanced numerical modeling to comprehensively assess seawater intrusion along the South Chennai coast from 2018 to 2022. The inverse modelling approach utilizes observed data, such as groundwater heads or concentrations, to estimate aquifer properties, like hydraulic conductivity, by minimizing the disparity between model predictions and actual measurements to identify the best-fit parameter values. This inverse method forecasts the necessary recharge rate to address seawater intrusion by increasing groundwater head and decreasing chloride concentrations. The estimation of recharge incorporates variables such as rainfall, soil infiltration capacity, land use, and the vital groundwater draft calculated based on per capita consumption and population. The study uses FEFLOW software to develop a robust conceptual model, simulating groundwater flow and chloride transport. The inverse modeling approach resulted in critical recharge adjustments, enhancing rates by 40%–60% in specific regions. The predictive scenarios, integrating proposed recharge structures, show promising results, notably with a 50% reduction in chloride concentration during the post-monsoon season compared to the pre-monsoon period in wells 8 and 3. A notable decrease in chloride concentration of over 90% was observed during May 2025 in well no: 15 due to its proximity to the Adayar River. These fluctuations are influenced by seasonal variations resulting from climatic changes in South India, with intense rainfall accompanying northeast monsoon winds playing a significant role. Increased recharge rates and strategically placed structures along the Buckingham Canal provide significant relief in the most impacted northern coastal area, acting as effective barriers. These interventions successfully reduce chloride concentration, providing a sustainable and effective solution to mitigate the persistent challenge of seawater intrusion.

The lithospheric structure of Greenland from a stepwise forward and inverse modelling approach

Summary Greenland’s tectonic history is complex, and the resulting lithospheric structure is, although extensively researched, not well constrained. In this study, we model the lithospheric structure of Greenland in a consistent, integrated framework with three steps. First, we build a lithospheric background model by forward modelling, adjusted to gravity gradient data and shear wave velocities from a regional tomography model. Subsequently, we jointly invert for the upper crustal density and susceptibility structure by minimizing the gravity residuals and magnetic total field anomaly misfit. The last modelling step searches for upper crustal thermal parameters to fit our model to the most recent geothermal heat flow predictions for Greenland. Finally, we present 3D models of the density, temperature and velocity structure for the lithosphere as well as thermal parameters and susceptibilities for the upper crust. Our model also includes the depth of the Moho and LAB in Greenland. A comparison between inverted crustal parameters and surface geology shows a clear correlation. The novelty of our model is that all these results are consistent with each other and simultaneously explain a wide range of observed data.

Apportioning sources of chemicals of emerging concern along an urban river with inverse modelling

Concentrations of chemicals in river water provide crucial information for assessing environmental exposure and risks from fertilisers, pesticides, heavy metals, illicit drugs, pathogens, pharmaceuticals, plastics and perfluorinated substances, among others. However, using concentrations measured along waterways (e.g., from grab samples) to identify sources of contaminants and understand their fate is complicated by mixing of chemicals downstream from diverse diffuse and point sources (e.g., agricultural runoff, wastewater treatment plants). To address this challenge, a novel inverse modelling approach is presented. Using waterway network topology, it quantifies locations and concentrations of contaminant sources upstream by inverting concentrations measured in water samples. It is computationally efficient and quantifies uncertainty. The approach is demonstrated for 13 contaminants of emerging concern (CECs) in an urban stream, the R. Wandle (London, UK). Mixing (the forward problem) was assumed to be conservative, and the location of sources and their concentrations were treated as unknowns to be identified. Calculated CEC source concentrations, which ranged from below detection limit (a few ng/L) up to 1μg/L, were used to predict concentrations of chemicals downstream. Using this approach, >90% of data were predicted within observational uncertainty. Principal component analysis of calculated source concentrations revealed signatures of two distinct chemical sources. First, pharmaceuticals and insecticides were associated with a subcatchment containing a known point source of treated effluent from a wastewater treatment plant. Second, illicit drugs and salicylic acid were associated with multiple sources, interpreted as input from untreated sewage including Combined Sewer Overflows (CSOs), misconnections, runoff and direct disposal throughout the catchment. Finally, a simple algorithmic approach that incorporates network topology was developed to design sampling campaigns to improve resolution of source apportionment. Inverse modelling of contaminant measurements can provide objective means to apportion sources in waterways from spot samples in catchments on a large scale.

Comparing machine learning and inverse modeling approaches for the source term estimation

Comparing machine learning and inverse modeling approaches for the source term estimation

Information theoretic clustering for coarse-grained modeling of non-equilibrium gas dynamics

We present a new framework towards the objective of learning coarse-grained models based on the maximum entropy principle. We show that existing methods for assigning clusters using the maximum entropy approach are heuristic or sub-optimal. We propose a machine learning framework informed by rate-distortion theory to learn optimal cluster assignments, aiming to improve the effectiveness of the coarse-graining process. Our approach involves transforming the discrete optimization problem into a probabilistic and continuous form. Our inverse modeling approach involves the development of a fully differentiable, adjoint-driven dynamics solver for use with gradient-based optimization techniques. The entire framework is end-to-end differentiable, facilitating backward pass gradient computation to flow through the ODE solve and probabilistic coarse-graining to train a classifier. We demonstrate application in the evolution of particle quantum states in non-equilibrium conditions. The high dimensionality of these equations poses a significant challenge for efficient and accurate computations, even for seemingly simple chemical systems. Training is performed using a loss function informed by rate-distortion theory to cluster the rovibrational states of some molecule, effectively forming a reduced order model of the master equations. We also introduce variable transformations along with several ways of improving the tractability of the training process. These allow the framework to process the extremely high-dimensional discrete optimization problem successfully. Our method is general, and to demonstrate the effectiveness in a controlled setting, we apply it to a one-dimensional Gaussian source quantization problem for which an analytical solution is known, followed by the problem of isothermal relaxation of a ▪ system with O(104) degrees of freedom.

Seasonal climate variations during Marine Isotope Stages 3 and 2 inferred from high‐resolution oxygen isotope ratios in horse tooth enamel from Lower Austria

ABSTRACTWe present sequential oxygen isotope records (δ18Ophosphate vs. VSMOW) of horse tooth enamel phosphate of six individuals from two adjacent Palaeolithic sites in Lower Austria. Three molars from the site Krems‐Wachtberg date to 33–31k cal a bp, and three molars from Kammern‐Grubgraben to 24–20k cal a bp. All teeth show seasonal isotope variations, which are used to reconstruct the annual oxygen isotope composition of drinking water (δ18Odw) and palaeotemperatures. Measured δ18Ophosphate values ranged from 8.6 to 13.0‰ and from 10.8 to 13.9‰ at Krems‐Wachtberg and Kammern‐Grubgraben, respectively. An inverse modelling approach was used to reconstruct summer and winter temperatures after a correction for glacial oceanic source water δ18O. Reconstructed annual δ18Odw was −16.4 ± 1.5‰ at Krems‐Wachtberg and −15.3 ± 1.4‰ at Kammern‐Grubgraben, resulting in annual temperatures of −5.7 ± 3.1 and −3.5 ± 2.9°C, respectively. Summer and winter temperatures reconstructed from individual teeth exhibit high seasonal variations with moderate summer temperatures and extremely low winter temperatures typical for a polar tundra climate. Isotopic differences between individuals are attributed to interannual climate variability or to different drinking water sources. Our reconstructed temperatures are, overall, consistent with previously reported values from European horse teeth, when taking regional differences into account.

Evaluation of crop model-based simplified marginal net return maximising nitrogen application rates on site-specific level in maize

AbstractCrop growth models such as DSSAT-CERES-Maize have proven to be useful for analysing plant growth and yield within homogenous land units. The paper presents results of newly developed model-based site-specific Soil Profile Optimisation (SPO) tools in combination with an updated version of an already published Nitrogen Prescription Model (NPM). Site-specific soil profiles were generated through an inverse modelling approach based on measured site-specific yield (point-based) and tops weight (above-ground biomass time-series) and evaluated. Site-specific soil profiles generated based only on measured yield variability were able to explain 72% (R2 0.72) of yield variability (dependent variable) based on selected soil profile input parameters (independent variable). Site-specific soil profiles generated based on measured yield and tops variability simultaneously (multiple target variable) explained 68% of yield variability (R2 0.68). The NPM uses the SPO generated site-specific soil profiles for economic evaluation of site-specific N application rates. NPM simulated N application rates, aiming at the maximisation of marginal net return (MNR) were 25% lower compared to the uniform N application rates with an assumed grain and N price of 0.17 and 0.3 Euro kg−1 respectively, under rainfed conditions over three years based on soil profiles generated via an inverse modelling approach only from measured yield variability (one target variable). N application rates were 28% lower when based on soil profiles generated from simultaneously included grain and tops variability in the inverse modelling approach. The results highlight the importance of site-specific fertilizer management when maximising MNR.

Global change alters coastal plankton food webs by promoting the microbial loop: An inverse modelling and network analysis approach on a mesocosm experiment

Marine organisms are currently, and will continue to be, exposed to the simultaneous effects of multiple environmental changes. Plankton organisms form the base of pelagic marine food webs and are particularly sensitive to ecosystem changes. Thus, warming, acidification, and changes in dissolved nutrient concentrations have the potential to alter these assemblages, with consequences for the entire ecosystem. Despite the growing number of studies addressing the potential influence of multiple drivers on plankton, global change may also cause less obvious alterations to the networks of interactions among species. Using inverse analyses applied to data collected during a mesocosm experiment, we aimed to compare the ecological functioning of coastal plankton assemblages and the interactions within their food web under different global change scenarios. The experimental treatments were based on the RCP 6.0 and 8.5 scenarios developed by the IPCC, which were extended (ERCP) to integrate the future predicted changes in coastal water nutrient concentrations. Overall, we identified that the functioning of the plankton food web was rather similar in the Ambient and ERCP 6.0 scenarios, but substantially altered in the ERCP 8.5 scenario. Using food web modelling and ecological network analysis, we identified that global change strengthens the microbial loop, with a decrease of energy transfer efficiency to higher trophic levels. Microzooplankton responded as well by an increased degree of herbivory in their diet and represented, compared to mesozooplankton, by far the main top-down pressure on primary producers. We also observed that the organisation of the food web and its capacity to recycle carbon was higher under the ERCP 8.5 scenario, but flow diversity and carbon path length were significantly reduced, illustrating an increased food web stability at the expense of diversity. Here, we provide evidence that if global change goes beyond the ERCP 6.0 scenario, coastal ecosystem functioning will be subjected to dramatic changes.

Cohesive Properties of Bimaterial Interfaces in Semiconductors: Experimental Study and Numerical Simulation Using an Inverse Cohesive Contact Approach.

Examining crack propagation at the interface of bimaterial components under various conditions is essential for improving the reliability of semiconductor designs. However, the fracture behavior of bimaterial interfaces has been relatively underexplored in the literature, particularly in terms of numerical predictions. Numerical simulations offer vital insights into the evolution of interfacial damage and stress distribution in wafers, showcasing their dependence on material properties. The lack of knowledge about specific interfaces poses a significant obstacle to the development of new products and necessitates active remediation for further progress. The objective of this paper is twofold: firstly, to experimentally investigate the behavior of bimaterial interfaces commonly found in semiconductors under quasi-static loading conditions, and secondly, to determine their respective interfacial cohesive properties using an inverse cohesive zone modeling approach. For this purpose, double cantilever beam specimens were manufactured that allow Mode I static fracture analysis of the interfaces. A compliance-based method was used to obtain the crack size during the tests and the Mode I energy release rate (GIc). Experimental results were utilized to simulate the behavior of different interfaces under specific test conditions in Abaqus. The simulation aimed to extract the interfacial cohesive contact properties of the studied bimaterial interfaces. These properties enable designers to predict the strength of the interfaces, particularly under Mode I loading conditions. To this extent, the cohesive zone modeling (CZM) assisted in defining the behavior of the damage propagation through the bimaterial interfaces. As a result, for the silicon-epoxy molding compound (EMC) interface, the results for maximum strength and GIc are, respectively, 26 MPa and 0.05 N/mm. The second interface tested consisted of polyimide and silicon oxide between the silicon and EMC layers, and the results obtained are 21.5 MPa for the maximum tensile strength and 0.02 N/mm for GIc. This study's findings aid in predicting and mitigating failure modes in the studied chip packaging. The insights offer directions for future research, focusing on enhancing material properties and exploring the impact of manufacturing parameters and temperature conditions on delamination in multilayer semiconductors.

Modeling initiation and propagation of thermal runaway in pouch Li-ion battery cells: Effects of heating rate and state-of-charge

The implementation of lithium-ion batteries (LIB) in battery energy storage systems (BESS) for utility-scale renewable energy generation has led to increased fire and explosion hazards. These hazards are attributable to the heat and flammable gases released during LIB thermal runaway (TR). TR can be triggered within an individual LIB cell and cause intra-cell TR propagation (TRP), the energy release from which causes TR in adjacent cells, leading to inter-cell TRP. Safety assessments are critical to preventing such incidents, and numerical modeling is a key tool in developing such assessments. This work presents an inverse modeling approach utilizing measurements of cell-level experiments of large-format 63Ah pouch cells used in BESS. Such an approach is a practical alternative to existing approaches that rely on a detailed thermochemical characterization of LIB internal components which are seldom available when dealing with commercial batteries encountered in BESS. Owing to the critical role played by gas and solid venting for external flaming and inter-cell TRP, we develop an approach to capture these quantities in the model. Two key parameters affecting TRP are the state-of-charge (SOC) and heating rate. The former provides some control of the system hazard, the latter affects TR behavior of LIB cells subject to different levels of preheating often encountered in BESS fires. We develop the capability to capture the effects of these parameters in the model. To determine model parameters, we rely on existing single-cell experimental data at different heating rates, and augment this with new experiments for the same type of cells at different SOCs. The modeling results recover the experimental TR and intra-cell TRP behavior quantitatively. Analysis reveals that, at lower heating rates, the temperature is greater at the time of the onset of TR, but the location is farther away from the heated cell surface; its location and the greater internal temperature then facilitate faster intra-cell TRP. When SOC is lower, the reduced internal heat release delays the onset of TR, and we find that its location moves away from the heated surface and intra-cell TRP is slower. Our findings provide physical insights to TR initiation and intra-cell propagation in support of large-scale TRP model development.

Degradation and accumulation of organic matter in euxinic surface sediments

The impact of oxygen on the preservation of organic matter in marine surface sediments is still controversial. We revisited this long-standing debate by determining the burial efficiency of sedimentary organic matter in the Black Sea, the largest anoxic and euxinic basin in the modern ocean. Surface sediments were sampled in the Danube paleodelta on the northwestern margin of the Black Sea at 420–1550 m water depth. Steady-state modeling of solid species (particulate organic carbon and nitrogen) and solutes (ammonium, sulfate, and total alkalinity) in sediments was performed to quantify rates of mass accumulation, particulate organic matter (POM) degradation, and POM burial. We develop a novel analytical model to quantify these rates applying an inverse modelling approach to down core data accounting for molecular diffusion, sediment burial and compaction. Our model results indicate that 56.7 ± 6.6 % of the particulate organic matter deposited in the study area is not degraded in surface sediments but accumulates below 10 cm sediment depth. This burial efficiency is substantially higher than those previously derived for seafloor areas underlying oxygenated bottom waters. Hence, our study confirms previous studies showing that euxinic bottom water conditions promote the preservation of particulate organic matter in marine sediments.