arXiv subjects

Britta Nestler

Publications and source records attributed to Britta Nestler.

At least 19 recordsLinked to original sources

arXiv2026

Pore-scale modeling of capillary-driven binder migration during battery electrode drying

Sodium-ion batteries employing hard carbon electrodes are considered a drop-in technology for lithium-ion batteries. Electrode drying is a critical manufacturing step, as binder migration during pore emptying impacts the mechanical integrity and electrical performance of the electrode. Existing modeling approaches predominantly rely on the film shrinkage phase in a one dimensional way or neglect the capillary transport, resulting in a lack of physically consistent microstructure resolved predictions of binder migration. In this work, a spatially resolved pore scale continuum model is extended to explicitly describe capillary driven binder transport during pore emptying. The model is applied to hard carbon microstructures with varying mean particle diameters. The simulations reveal that smaller particle sizes lead to a more homogeneous binder distribution, whereas higher evaporation rates and increased surface tension promote stronger binder gradients. Variations in solvent viscosity show only a minor influence on binder migration, as long as no hydrophilic or hydrophobic behavior is present. Finally, the simulations demonstrate that an explicit description of capillary transport and microstructural effects is essential for accurately predicting binder migration and provides a basis for the targeted optimization of electrode drying processes.

cs.CE↗

arXiv2026

Solid-State Dewetting of Polycrystalline Thin Films: a Phase Field Approach

Solid-state dewetting is the process by which thin solid films break up and retract on a substrate, forming nanostructures. While dewetting of single-crystalline films is understood as a surface-energy-driven process mediated by surface diffusion, polycrystalline films exhibit additional complexity due to the presence of grain boundaries. Most theoretical and computational studies have focused on single-crystalline dewetting. Here, we present the application of the grandpotential multi-phase-field model to the dewetting of thin polycrystalline films in three dimensions, reproducing the key phenomenology of this process. By considering isotropic interface/surface energy, we illustrate its consistency with predictions based on energetic arguments and the morphological evolution towards equilibrium. We also provide novel analytical criteria for the onset of three-dimensional dewetting, serving as fundamental theoretical benchmarks, and highlight the critical role of triple junctions. Moreover, we unveil the dewetting behavior of polycrystalline patches, extending the scenarios of their single-crystalline counterparts.

cond-mat.mtrl-sci↗

arXiv2026

The microscopic origin of droplet line tension

The size dependence of the equilibrium droplet contact angle is governed by line tension. In this work, we identify a contribution to line tension arising from gravitational effects and pressure-induced changes in volume-fraction-dependent interfacial tensions within an adsorption layer. This mechanism addresses a multiscale problem of line tension in droplets ranging from nanometric to millimetric sizes that change sign and span several orders of magnitude, in agreement with experimental and simulation results. The sign of the apparent line tension is controlled by surface wettability, the initial volume fraction in the adsorption layer, and the droplet size, which also strongly influences its magnitude. Our results provide a unified physical interpretation of the experimentally observed variability in both the sign and magnitude of line tensions.

physics.flu-dyn↗

arXiv2026

Slip and friction at fluid-solid interfaces: Concept of adsorption layer

When a fluid flows past a solid surface, its macroscopic motion arises from a subtle interplay between microscopic hydrodynamic and thermodynamic effects at the fluid-solid interface. Classical hydrodynamic models often rely on an unphysical no-slip boundary condition or an arbitrarily prescribed slip length, yet both approaches lack a rigorous physical foundation. This work introduces the concept of an Adsorption Layer (AL), an interfacial region of thickness delta l, where fluid-solid molecular interactions regulate both surface adsorption/depletion and interfacial slip. By applying the energy minimization principle, we derive balance equations within the AL that couple fluid-solid friction, viscous stresses, and surface adsorption dynamics. This framework establishes a self-consistent thermodynamic coupling between the AL and the bulk fluid, unlike conventional sharp-interface models. A key finding is the often-overlooked role and coupling of pressure and chemical potential gradients in the direction normal to the interface. This theoretical advance successfully explains the confinement-induced enhancement of water slippage in carbon nanotubes, quantitatively agreeing with molecular dynamics and experimental data -- an effect classical slip models fail to reproduce. Furthermore, when extended to binary liquids, the theory captures spatial variations in slip velocity near moving contact lines, highlighting the role of interfacial friction in shaping local flow. Our results demonstrate that the slip length is not a fixed material constant but rather an emergent, geometry- and composition-dependent property arising from coupled interfacial thermodynamics and hydrodynamics. This framework provides a physically grounded description of interfacial momentum transfer, with significant implications for microfluidics and surface engineering.

physics.flu-dyn↗

arXiv2025

Unsupervised Segmentation of Micro-CT Scans of Polyurethane Structures By Combining Hidden-Markov-Random Fields and a U-Net

Extracting digital material representations from images is a necessary prerequisite for a quantitative analysis of material properties. Different segmentation approaches have been extensively studied in the past to achieve this task, but were often lacking accuracy or speed. With the advent of machine learning, supervised convolutional neural networks (CNNs) have achieved state-of-the-art performance for different segmentation tasks. However, these models are often trained in a supervised manner, which requires large labeled datasets. Unsupervised approaches do not require ground-truth data for learning, but suffer from long segmentation times and often worse segmentation accuracy. Hidden Markov Random Fields (HMRF) are an unsupervised segmentation approach that incorporates concepts of neighborhood and class distributions. We present a method that integrates HMRF theory and CNN segmentation, leveraging the advantages of both areas: unsupervised learning and fast segmentation times. We investigate the contribution of different neighborhood terms and components for the unsupervised HMRF loss. We demonstrate that the HMRF-UNet enables high segmentation accuracy without ground truth on a Micro-Computed Tomography ($μ$CT) image dataset of Polyurethane (PU) foam structures. Finally, we propose and demonstrate a pre-training strategy that considerably reduces the required amount of ground-truth data when training a segmentation model.

cs.CV↗

arXiv2025

Comparing the 3D morphology of solid-oxide fuel cell anodes for different manufacturing processes, annealing times, and operating temperatures

Solid oxide fuel cells (SOFCs) are becoming increasingly important due to their high electrical efficiency, the flexible choice of fuels and relatively low emissions of pollutants. However, the increasingly growing demands for electrochemical devices require further performance improvements. Since it is well known that the 3D morphology of the electrodes, which is significantly influenced by the underlying manufacturing process, has a profound impact on the resulting performance, a deeper understanding for the structural changes caused by modifications of the manufacturing process or degradation phenomena is desirable. In the present paper, we investigate the influence of the annealing time and the operating temperature on the 3D morphology of SOFC anodes using 3D image data obtained by focused-ion beam scanning electron microscopy, which is segmented into gadolinium-doped ceria, nickel and pore space. In addition, structural differences caused by manufacturing the anode via infiltration or powder technology, respectively, are analyzed quantitatively by means of various geometrical descriptors such as specific surface area, length of triple phase boundary per unit volume, mean geodesic tortuosity, and constrictivity. The computation of these descriptors from 3D image data is carried out both globally as well as locally to quantify the heterogeneity of the anode structure.

cond-mat.mtrl-sci↗

arXiv2025

Modeling the drying process in hard carbon electrodes based on the phase-field method

The present work addresses the simulation of pore emptying during the drying of battery electrodes. For this purpose, a model based on the multiphase-field method (MPF) is used, since it is an established approach for modeling and simulating multiphysical problems. A model based on phase fields is introduced that takes into account fluid flow, capillary effects, and wetting behavior, all of which play an important role in drying. In addition, the MPF makes it possible to track the movement of the liquid-air interface without computationally expensive adaptive mesh generation. The presented model is used for the first time to investigate pore emptying in real hard carbon microstructures. For this purpose, the microstructures of real dried electrodes are used as input for the simulations. The simulations performed here demonstrate the importance of considering the resolved microstructural information compared to models that rely only on statistical geometry parameters such as pore size distributions. The influence of various parameters such as different microstructures, fluid viscosity, and the contact angle on pore emptying are investigated. In addition, this work establishes a correlation between the capillary number and the breakthrough time of the solvent as well as the height difference of the solvent front at the time of breakthrough. The results indicate that the drying process can be optimized by doping the particle surface, which changes the contact angle between the fluids and the particles.

cs.CE↗

arXiv2024

Simulation of dendritic-eutectic growth with the phase-field method

Solidification is an important process in many alloy processing routes. The solidified microstructure of alloys is usually made up of dendrites, eutectics or a combination of both. The evolving morphologies are largely determined by the solidification process and thus many materials properties are dependent on the processing conditions. While the growth of either type of microstructure is well-investigated, there is little information on the coupled growth of both microstructures. This work aims to close this gap by formulating a phase-field model capable of reproducing dendritic, eutectic as well as dendritic-eutectic growth. Following this, two-dimensional simulations are conducted which show all three types of microstructures depending on the composition and processing conditions. The effect of the dendritic-eutectic growth on the microstructural lengths, which determine materials properties, is investigated and the morphological hysteresis between eutectic growth and dendritic-eutectic growth is studied by employing solidification velocity jumps. Further, the influence of primary crystallization is investigated in large-scale two-dimensional simulations. Finally, qualitative three-dimensional simulations are conducted to test for morphological changes in the eutectic.

cond-mat.mtrl-sci↗

arXiv2024

ML-extendable framework for multiphysics-multiscale simulation workflow and data management using Kadi4Mat

As material modeling and simulation has become vital for modern materials science, research data with distinctive physical principles and extensive volume are generally required for full elucidation of the material behavior across all relevant scales. Effective workflow and data management, with corresponding metadata descriptions, helps leverage the full potential of data-driven analyses for computer-aided material design. In this work, we propose a research workflow and data management (RWDM) framework to manage complex workflows and resulting research (meta)data, while following FAIR principles. Multiphysics multiscale simulations for additive manufacturing investigations are treated as showcase and implemented on Kadi4Mat: an open source research data infrastructure. The input and output data of the simulations, together with the associated setups and scripts realizing the simulation workflow, are curated in corresponding standardized Kadi4Mat records with extendibility for further research and data-driven analyses. These records are interlinked to indicate information flow and form an ontology based knowledge graph. Automation scheme for performing high-throughput simulation and post-processing integrated with the proposed RWDM framework is also presented.

cond-mat.mtrl-sci↗

arXiv2024

Revealing process and material parameter effects on densification via phase-field studies

Sintering is an important processing step in both ceramics and metals processing. The microstructure resulting from this process determines many materials properties of interest. Hence the accurate prediction of the microstructure, depending on processing and materials parameters, is of great importance. The phase-field method offers a way of predicting this microstructural evolution on a mesoscopic scale. The present paper employs this method to investigate concurrent densification and grain growth and the influence of stress on densification. Furthermore, the method is applied to simulate the entire freeze-casting process chain for the first time ever by simulating the freezing and sintering processes separately and passing the frozen microstructure to the present sintering model.

cond-mat.mtrl-sci↗

arXiv2024

Breakup of liquid jets: Thermodynamic perspectives

Breakup of a liquid jet into a chain of droplets is common in nature and industry. Previous researchers developed profound mathematic and fluid dynamic models to address this breakup phenomenon starting from tiny perturbations. However, the morphological evolution of the jets at large amplitude perturbations is still an open question. Here, we report a concise thermodynamic model based on the surface area minimization principle. Our results demonstrate a reversible breakup transition from a continuous jet via droplets towards a uniform-radius cylinder, which has not been found previously, but is observed in our simulations. This new observation is attributed to a geometric constraint, which was often overlooked in former studies. We anticipate our model to be a shortcut to tackle many similar highly nonlinear morphological evolutions without solving abstruse fluid dynamic equations for inviscid fluids.

physics.flu-dyn↗

arXiv2023

Brownian motion of droplets induced by thermal noise

Brownian motion (BM) is pivotal in natural science for the stochastic motion of microscopic droplets. In this study, we investigate BM driven by thermal composition noise at sub-micro scales, where inter-molecular diffusion and surface tension both are significant. To address BM of microscopic droplets, we develop two stochastic multi-phase-field models coupled with the full Navier-Stokes equation, namely Allen-Cahn-Navier-Stokes (ACNS) and Cahn-Hilliard-Navier-Stokes (CHNS). Both models are validated against capillary wave theory; the Einstein's relation for the Brownian coefficient at thermodynamic equilibrium is recovered. Moreover, by adjusting the co-action of the diffusion, Marangoni effect, and viscous friction, two non-equilibrium phenomena are observed. (I) The droplet motion transits from the Brownian to Ballistic with increasing Marangoni effect which is emanated from the energy dissipation mechanism distinct from the conventional fluctuation-dissipation theorem. (II) The deterministic droplet motion is triggered by the noise induced non-uniform velocity field which leads to a novel droplet coalescence mechanism associated with the thermal noise.

physics.flu-dyn↗

arXiv2023

A U-Net-Based Self-Stitching Method for Generating Periodic Grain Structures

When modeling microstructures, the computational resource requirements increase rapidly as the simulation domain becomes larger. As a result, simulating a small representative fraction under periodic boundary conditions is often a necessary simplification. However, the truncated structures leave nonphysical boundaries, which are detrimental to numerical modeling. Here, we propose a self-stitching algorithm for generating periodic structures, demonstrated in a grain structure. The main idea of our algorithm is to artificially add structural information between mismatched boundary pairs, using the hierarchical spatial predictions of the U-Net. The algorithm provides an automatic and unbiased way to obtain periodic boundaries in grain structures and can be applied to porous microstructures in a similar way.

cond-mat.mtrl-sci↗

arXiv2023

Unravelling densification during sintering by multiscale modelling of grain motion

The resulting microstructure after the sintering process determines many materials properties of interest. In order to understand the microstructural evolution, simulations are often employed. One such simulation method is the phase-field method, which has garnered much interest in recent decades. However, the method lacks a complete model for sintering, as previous works could show unphysical effects and the inability to reach representative volume elements. Thus the present paper aims to close this gap by employing molecular dynamics and determining rules of motion which can be translated to a phase-field model. The key realization is that vacancy absorption induced motion of grains travels through a grain structure without resistance. Hence the total displacement field of a green body is simply the superposition of all grains reacting in isolation to local vacancy absorption events. The resulting phase-field model is shown to be representative starting from particle counts between 97 and 262 and contains the qualitative correct dependence of sintering rate on particle size.

cond-mat.mtrl-sci↗

arXiv2023

The effect of compression shock heating in collision welding

This work discusses the origin of temperature rise during the collision welding process. The different physical irreversible and reversible mechanisms which act as heat sources are described: isentropic compression work, shock dissipation, plasticity, and phase transitions. The temperature increase due to these effects is quantified in a continuum mechanics approach, and compared to predictions of atomistic molecular dynamics simulations. Focusing on a single impact scenario of 1100 aluminium at 700 m/s, our results indicate that shock heating and plastic work only effect a temperature rise of 100 K, and that the effects of phase change are not significant. This temperature rise cannot explain welding. In consequence, the relevant mechanism which effects bonding in collision welding must be due to the jet, which is only formed at oblique impact angles.

cond-mat.mtrl-sci↗

arXiv2023

An improved grand-potential phase-field model of solid-state sintering for many particles

Understanding the microstuctural evolution during the sintering process is of high relevance as it is a key part in many industrial manufacturing processes. Simulations are one avenue to achieve this understanding, especially field-resolved methods such as the phase-field method. Recent papers have shown several weaknesses in the most common phase-field model of sintering, which the present paper aims to ameliorate. The observed weaknesses are shortly recounted, followed by presenting model variations aiming to remove these deficiencies. The models are tested in the classical two-particle geometry, with the most promising model being run on large-scale three-dimensional packings to determine representative volume elements. A densification that is strongly dependent on the packing size is observed, which suggests that the model requires further improvement.

cond-mat.mtrl-sci↗

arXiv2021

Data-driven analysis of grain-growth kinetics in duplex and triplex systems

During grain growth in multiphase system, a convincing understanding on how the evolution kinetics of an individual phase-grains relates to the growth rate of entire microstructure is yet to be achieved. Therefore, in this work, comfortably comprehensible statistical tools are employed to realise the influence of the evolving phase-grains on the overall grain-growth kinetics of multiphase systems, both duplex and triplex, with varying phase-fractions. A dataset capable of rendering statistically significant, and accessible, information is built by modelling, and analysing, grain growth in twenty different polycrystalline systems, comprising of fourteen triplex and five duplex microstructures with varying phase-fractions, and one homogeneous single-phase system, in multiphase-field framework. Statistical analyses of these wide-range of microstructures unravel that, during isotropic grain-growth, the overall evolution kinetics of the duplex and triplex systems are principally governed by the growth-rate of the major-phase grains, irrespective of the phase-fractions. Furthermore, through multiple non-linear regression, it is realised that with increase in the volume-fraction of the major-phase grains, the overall isotropic grain-growth rate exhibited by duplex and triplex microstructures increase in accordance with a definite exponential relation. Additionally, the present investigation unravels that, while the evolution of individual phase-grains during grain growth in triplex microstructures exhibit a level of interdependency, the least interaction is observed between the grains of minor-phases with relatively low volume-fraction.

cond-mat.mtrl-sci↗

arXiv2021

Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models

(Artificial) neural networks have become increasingly popular in mechanics to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of neural networks remains: their numerous parameters are challenging to interpret and explain. Thus, neural networks are often labeled as black boxes, and their results often elude human interpretation. In mechanics, the new and active field of physics-informed neural networks attempts to mitigate this disadvantage by designing deep neural networks on the basis of mechanical knowledge. By using this a priori knowledge, deeper and more complex neural networks became feasible, since the mechanical assumptions could be explained. However, the internal reasoning and explanation of neural network parameters remain mysterious. Complementary to the physics-informed approach, we propose a first step towards a physics-informing approach, which explains neural networks trained on mechanical data a posteriori. This novel explainable artificial intelligence approach aims at elucidating the black box of neural networks and their high-dimensional representations. Therein, the principal component analysis decorrelates the distributed representations in cell states of RNNs and allows the comparison to known and fundamental functions. The novel approach is supported by a systematic hyperparameter search strategy that identifies the best neural network architectures and training parameters. The findings of three case studies on fundamental constitutive models (hyperelasticity, elastoplasticity, and viscoelasticity) imply that the proposed strategy can help identify numerical and analytical closed-form solutions to characterize new materials.

cs.LG↗