arXiv subjects

Sébastien Kiesgen de Richter

Publications and source records attributed to Sébastien Kiesgen de Richter.

5 recordsLinked to original sources

arXiv2025

Internal Waves Control Bulk Flow in Silos

We experimentally measure paticle acceleration within the bulk during the discharge of a granular silo. We highlight the existence of a deceleration wave emerging at the outlet level near the dead zone and propagates toward the top of the medium. The wave emission frequency is extracted from spatiotemporal diagrams of the Eulerian instantaneous acceleration profiles. Surprisingly, we find that this frequency decreases with the cohesion of the medium and is independent of the outlet size.

cond-mat.soft↗

arXiv2025

Rheological Parameter Identification in Granular Materials Using Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have recently emerged as a promising tool for fluid dynamics, particularly for flow reconstruction and parameter identification. In the context of granular media, accurately estimating rheological parameters remains a major challenge, as it typically requires complex and costly experimental setups. In this work, we propose a PINN-based approach to identify key rheological parameters of granular materials using a simple experiment: the granular column collapse. A proof of concept is presented using synthetic data, where the PINN is trained to infer the flow fields while simultaneously recovering the rheological parameters. Beyond parameter identification, the method also enables reconstruction of the pressure field, which is difficult to access experimentally. The results highlight the potential of PINNs for data-driven rheometry of granular materials and open perspectives for future applications with real experimental data.

cond-mat.dis-nn↗

arXiv2025

Structural Order Drives Diffusion in a Granular Packing

We investigate how structural ordering, i.e. crystallization, affects the flow of bidisperse granular materials in a quasi-two-dimensional silo. By systematically varying the mass fraction of two particle sizes, we finely tune the degree of local order. Using high-speed imaging and kinematic modeling, we show that crystallization significantly enhances the diffusion length $b$, a key parameter controlling the velocity profiles within the flowing medium. We reveal a strong correlation between $b$ and the hexatic order parameter $\left<|ψ_6|\right>_t$, highlighting the role of local structural organization in governing macroscopic flow behavior. Furthermore, we demonstrate that pressure gradients within the silo promote the stabilization of orientational order even in the absence of crystallization, thus intrinsically increasing $b$ with height. These findings establish a direct link between microstructural order, pressure, and transport properties in granular silo flows.

cond-mat.soft↗

arXiv2023

Physics-informed neural networks for gravity currents reconstruction from limited data

The present work investigates the use of physics-informed neural networks (PINNs) for the 3D reconstruction of unsteady gravity currents from limited data. In the PINN context, the flow fields are reconstructed by training a neural network whose objective function penalizes the mismatch between the network predictions and the observed data and embeds the underlying equations using automatic differentiation. This study relies on a high-fidelity numerical experiment of the canonical lock-exchange configuration. This allows us to benchmark quantitatively the PINNs reconstruction capabilities on several training databases that mimic state-of-the-art experimental measurement techniques for density and velocity. Notably, spatially averaged density measurements by light attenuation technique (LAT) are employed for the training procedure. An optimal experimental setup for flow reconstruction by PINNs is proposed according to two criteria : the implementation complexity and the accuracy of the inferred fields.

physics.flu-dyn↗

arXiv2016

Shear-banding and Taylor-Couette instability in thixotropic yield stress fluids

In the present work, we study the flow of thixotropic yield stress fluids between two concentric cylinders. In order to take into account the thixotropy, the constitutive relation uses a structural parameter which is driven by a kinetic equation. Here, the Houska's model is considered. Depending on the breakdown rate of the structural parameter, localization or shear-banding are observed. We show that for fragile structures, a shear-banding flow may be observed although for stronger structures, only localisation of the flow is observed such as in Bingham fluids. Physical explanations of the shear-banding discussed by several authors in the literature highlight that the shear-banding may be associated with a discontinuity into the structure of the material and a non-monotonic evolution of the stress according to the constitutive relation with the strain rate. Solving numerically the flow, we show that such a rheological model based on the existence of a structural parameter is able to predict shear-banding. Moreover, the consequences of the thixotropy on the linear stability of the azimuthal flow is studied in a large range of parameters. Although the thixotropy allows shear banding for the base flow, it does not modify fundamentaly the stability of the Couette flow compared to a simple yield stress fluid. The apparent shear-thinning behaviour depends on the thixotropic parameters of the fluid and the results about the onset of the Taylor vortices in shear-thinning fluids are retrieved. Nevertheless, the shear-banding modifies the stratification of the viscosity in the flowing zone such that the critical conditions are mainly driven by the width of the flowing region.

physics.flu-dyn↗