arXiv subjects

Sylvie Aubry

Publications and source records attributed to Sylvie Aubry.

7 recordsLinked to original sources

arXiv2026

Precipitation strengthening: a collective multi-dislocation phenomenon

Precipitation strengthening is a cornerstone of physical metallurgy, delivering otherwise unattainable combinations of strength and ductility. The approach relies on nanoscale precipitates that impede the motion of dislocations, the primary carriers of plastic deformation. Historically, precipitation strengthening has been rationalized via two idealized, limiting mechanisms: dislocations either cut through or bow around precipitates. However, in situ experiments cannot yet resolve the coupled, real-time evolution of dislocation networks and nanoprecipitates, leaving these atomic-scale dynamics inaccessible to direct observation. Here, using large-scale atomistic simulations that fully capture these dynamics, we demonstrate that the classical cutting-versus-bowing dichotomy is incomplete. Instead, strengthening arises as an emergent collective phenomenon driven by concurrent, multi-dislocation interactions. These interactions simultaneously induce dislocation accumulation at interfaces, storage within precipitates, and precipitate-mediated multiplication inside the matrix. These findings establish a mechanistic framework that transcends traditional models and provides a new foundation for predicting strengthening behavior.

cond-mat.mtrl-sci↗

arXiv2025

A Hereditary Integral, Transient Network Approach to Modeling Permanent Set and Viscoelastic Response in Polymers

An efficient numerical framework is presented for modeling viscoelasticity and permanent set of polymers. It is based on the hereditary integral form of transient network theory, in which polymer chains belong to distinct networks each with different natural equilibrium states. Chains continually detach from previously formed networks and reattach to new networks in a state of zero stress. The free energy of these networks is given in terms of the deformation gradient relative to the configuration at which the network was born. A decomposition of the kernel for various free energies allows for a recurrence relationship to be established, bypassing the need to integrate over all time history. The technique is established for both highly compressible and nearly incompressible materials through the use of neo-Hookean, Blatz-Ko, Yeoh, and Ogden-Hill material models. Multiple examples are presented showing the ability to handle rate-dependent response and residual strains under complex loading histories.

cs.CE↗

arXiv2025

Atomistic insights into solid solution strengthening: size misfit versus stiffness misfit

Used for centuries to enhance mechanical properties of materials, solid solution strengthening (SSS) is a classical metallurgical method in which small amounts of impurity elements are added to a base metal. Developed for dilute alloys, classical theories of SSS are presently challenged by the ongoing explosive development of complex concentrated alloys (CCA) in which all component elements are present in nearly equal fractions. Here we develop a method of computational alchemy in which interatomic interactions are modified to continuously and systematically vary two key parameters defining SSS, atomic size misfit and elastic stiffness misfit, over a maximally wide range of misfit values. The resulting model alloys are subjected to massive Molecular Dynamics simulations reproducing full complexity of plastic strength response in concentrated single-phase body-centered cubic solid solutions. At variance with views prevailing in the literature, our computational experiments show that stiffness misfit can contribute to SSS on par if not more than size misfit. Furthermore, depending on exactly how they are combined, the two misfits can result in synergistic or antagonistic effect on alloy strengthening. In contrast to real CCAs in which every constituent element comes with its specific combination of atomic size and elastic stiffness, our alchemical model alloys sample the space of misfit parameters continuously thus augmenting the much more constrained and inevitably spotty experimental exploration of the CCA design space. Taking advantage of unique to our approach ability to define alloy misfit parameters, our computational study demonstrates how useful insights can be gained from intentionally unrealistic alchemical models. Rather than practical recommendation for alloy design, our computational experiments should be regarded as a proving ground for further SSS theory development.

cond-mat.mtrl-sci↗

arXiv2024

Enhanced mobility of dislocation network nodes and its effect on dislocation multiplication and strain hardening

Understanding plastic deformation of crystals in terms of the fundamental physics of dislocations has remained a grand challenge in materials science for decades. To overcome this, the Discrete Dislocation Dynamics (DDD) method has been developed, but its lack of atomistic resolution leaves open the possibility that certain key mechanisms may be overlooked. By comparing large-scale Molecular Dynamics (MD) with DDD simulations performed under identical conditions we uncover significant discrepancies in the predicted strength and microstructure evolution in BCC crytals under high-strain rate conditions. These are traced to unexpected behaviors of dislocation network nodes forming at dislocation intersections, that can move in ways not previously anticipated as revealed by MD. Once these newfound freedoms of nodal motion are incorporated, DDD simulations begin to closely match plastic evolution observed in MD. This additional mechanism of motion whereby non-screw dislocations can change their glide plane profoundly affects fundamental processes of dislocation multiplication, recovery and storage that define strength of metals.

cond-mat.mtrl-sci↗

arXiv2023

A Novel Mechanism for the Formation of Dislocation Cell Patterns in BCC Metal

In this study, we present the first simulation results of the formation of dislocation cell wall microstructures in tantalum subjected to shock loading. Dislocation patterns and cell wall formation are important to understanding the mechanical properties of the materials in which they spontaneously arise, and yet the processing and self-assembly mechanisms leading to their formation are poorly understood. By employing transmission electron microscopy and discrete dislocation dynamics, we propose a new mechanism involving coplanar dislocations and pseudo-dipole mixed dislocation arrays that is essential to the pattern formation process. Our large-scale 3D DDD simulations demonstrate the self-organization of dislocation networks into cell walls in deformed BCC metal (tantalum) persisting at the strain 20%. The simulation analysis captures several crucial aspects of how the dislocation cell pattern affects metal plasticity, as observed in experiments. Although experimental evidence is inconclusive regarding whether cell wall formation takes place at the shock front, after the shock, during release, or when the sample has had enough time to relax post-recovery, our simulations indicate cell wall formation occurs after the shock and before release. The extended Taylor hardening composite model effectively considers the non-uniform dislocation density when cell walls form and accurately describes the corresponding flow stress.

cond-mat.mtrl-sci↗

arXiv2022

Using Conservation Laws to Infer Deep Learning Model Accuracy of Richtmyer-meshkov Instabilities

Richtmyer-Meshkov Instability (RMI) is a complicated phenomenon that occurs when a shockwave passes through a perturbed interface. Over a thousand hydrodynamic simulations were performed to study the formation of RMI for a parameterized high velocity impact. Deep learning was used to learn the temporal mapping of initial geometric perturbations to the full-field hydrodynamic solutions of density and velocity. The continuity equation was used to include physical information into the loss function, however only resulted in very minor improvements at the cost of additional training complexity. Predictions from the deep learning model appear to accurately capture temporal RMI formations for a variety of geometric conditions within the domain. First principle physical laws were investigated to infer the accuracy of the model's predictive capability. While the continuity equation appeared to show no correlation with the accuracy of the model, conservation of mass and momentum were weakly correlated with accuracy. Since conservation laws can be quickly calculated from the deep learning model, they may be useful in applications where a relative accuracy measure is needed.

physics.flu-dyn↗

arXiv2016

Computing forces on interface elements exerted by dislocations in an elastically anisotropic crystalline material

Driven by the growing interest in numerical simulations of dislocation-interface interactions in general crystalline materials with elastic anisotropy, we develop algorithms for the integration of interface tractions needed to couple dislocation dynamics with a finite element or boundary element solver. The dislocation stress fields in elastically anisotropic media are made analytically accessible through the spherical harmonics expansion of the derivative of Green's function, and analytical expressions for the forces on interface elements are derived by analytically integrating the spherical harmonics series recursively. Compared with numerical integration by Gaussian quadrature, the newly developed analytical algorithm for interface traction integration is highly beneficial in terms of both computation precision and speed.

cond-mat.mtrl-sci↗