arXiv subjects

Carsten Schilde

Publications and source records attributed to Carsten Schilde.

3 recordsLinked to original sources

arXiv2026

Meshfree Snow Modelling using a Modified Cam-Clay Approach

Snow is a complex geomaterial whose macroscopic response is governed by density, temperature, and the topology of its evolving microstructure. Its mechanical behavior spans elastic, plastic, viscous, and failure dominated regimes, imposing significant challenges for numerical methods, which intends to simulate large deformations, evolving free surfaces, and complex boundary interactions. This work presents the first integration of a Modified Cam-Clay constitutive formulation for snow into a purely meshfree strong-form collocation framework based on the Generalized Finite Difference Method. The main methodological contribution is a numerical coupling that combines a global implicit mixed formulation for pressure and velocity with a constitutive return-mapping algorithm. The hydrostatic pressure contribution is obtained from a Poisson equation and subsequently corrected through the Modified Cam-Clay return-mapping procedure, while the deviatoric response is treated semi-implicitly using a numerical viscosity formulation. This partitioned treatment of the volumetric and deviatoric stress contributions enables stable simulations with comparatively large time steps while producing smooth spatial pressure fields. As a result, forces on complex boundary geometries can be evaluated accurately. Numerical results of this coupling illustrate the algorithmic stability of the framework, the effective imposition of boundary conditions, and the suitability of local spatial refinement. The feasibility of applying the framework to vehicle-snow interaction through rigid-body coupling is also illustrated. The presented formulation provides a robust basis for future simulations of dynamic snow loading on vehicle structures.

math.NA↗

arXiv2025

Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling

The 3D microstructure of porous media, such as electrodes in lithium-ion batteries or fiber-based materials, significantly impacts the resulting macroscopic properties, including effective diffusivity or permeability. Consequently, quantitative structure-property relationships, which link structural descriptors of 3D microstructures such as porosity or geodesic tortuosity to effective transport properties, are crucial for further optimizing the performance of porous media. To overcome the limitations of 3D imaging, parametric stochastic 3D microstructure modeling is a powerful tool to generate many virtual but realistic structures at the cost of computer simulations. The present paper uses 90,000 virtually generated 3D microstructures of porous media derived from literature by systematically varying parameters of stochastic 3D microstructure models. Previously, this data set has been used to establish quantitative microstructure-property relationships. The present paper extends these findings by applying a hybrid AI framework to this data set. More precisely, symbolic regression, powered by deep neural networks, genetic algorithms, and graph attention networks, is used to derive precise and robust analytical equations. These equations model the relationships between structural descriptors and effective transport properties without requiring manual specification of the underlying functional relationship. By integrating AI with traditional computational methods, the hybrid AI framework not only generates predictive equations but also enhances conventional modeling approaches by capturing relationships influenced by specific microstructural features traditionally underrepresented. Thus, this paper significantly advances the predictive modeling capabilities in materials science, offering vital insights for designing and optimizing new materials with tailored transport properties.

cond-mat.mtrl-sci↗

arXiv2012

Colloidal aggregates tested via nanoindentation and simultaneous 3D imaging

The mechanical properties of aggregated colloids depend on the mutual interplay of inter-particle potentials, contact forces, aggregate structure and material properties of the bare particles. Owing to this variety of influences experimental results from macroscopic mechanical testings were mostly compared to time-consuming, microscopic simulations rather than to analytical theories. The aim of the present paper was to relate both macroscopic and microscopic mechanical data with each other and simple analytical models. We investigated dense amorphous aggregates made from monodisperse poly-methyl methacrylate (PMMA) particles (diameter: 1.6μm) via nanoindentation in combination with confocal microscopy. The resulting macroscopic information were complemented by the three dimensional aggregate structure as well as the microscopic strain field. The strain field was in reasonable agreement with the predictions from analytical continuum theories. As a consequence the measured force-depth curves could be analyzed within a theoretical framework that has frequently been used for nanoindentation of atomic matter such as metals, ceramics and polymers. The extracted values for hardness and effective elastic modulus represented average values characteristic of the aggregate. On base of these parameters we discuss the influence of the strength of particle bonds by introducing polystyrene (PS) between the particles.

cond-mat.soft↗