arXiv subjects

Michael R. Tonks

Publications and source records attributed to Michael R. Tonks.

7 recordsLinked to original sources

arXiv2026

Mechanisms of Microstructural Evolution and Degradation in Aluminum under High-Damage Irradiation

Aluminum alloys are widely used in research reactor systems, yet the mechanisms governing irradiation-induced degradation remain poorly understood. Here we combine conventional cascade-overlap molecular dynamics simulations with an accelerated Iterative Kinetic Approach (IKA) to investigate defect evolution in single-crystal Al subjected to 50 keV He irradiation. Benchmarking shows that IKA reproduces the essential defect kinetics of cascade simulations while enabling access to substantially higher accumulated damage. By extending the IKA to higher damage levels, we identified three distinct regimes governing radiation-induced degradation in Al: recombination-driven annihilation, defect accumulation, and sink-controlled absorption. At higher damage, Frank loops dissociate into Shockley partials and stair-rod loops, ultimately driving the nucleation and growth of stacking-fault tetrahedra (SFTs). These transformations progressively convert mobile defects into SFTs. Ultimately, the synergistic effect of interstitial and vacancy loops and SFTs increases irradiation hardening in Al at 300 K. This work provides insight into irradiation-induced degradation in aluminum reactor materials.

cond-mat.mtrl-sci↗

arXiv2025

Effect of Magneto-Mechanical Synergism in the Process-Structure Correlation in Fe-C Alloys: A Phase-Field Modeling Approach

Applied magnetic fields can alter phase equilibria and kinetics in steels; however, quantitatively resolving how magnetic, chemical, and elastic driving forces jointly influence the microstructure remains challenging. We develop a quantitative magneto-mechanically coupled phase-field model for the Fe-C system that couples a CALPHAD-based chemical free energy with demagnetization-field magnetostatics and microelasticity. The model reproduces single- and multi-particle evolution during the alpha to gamma inverse transformation at 1023 K under external fields up to 20 T, including ellipsoidal morphologies observed experimentally at 8 T. Chemically driven growth is isotropic; a magnetic interaction introduces an anisotropic driving force that elongates gamma precipitates along the field into ellipsoids, while elastic coherency promotes faceting, yielding elongated cuboidal or ``brick-like" particles under combined magneto-elastic coupling. Growth kinetics increase with C content, and decrease with field strength and misfit strain. Multi-particle simulations reveal dipolar interaction-mediated coalescence for field-parallel neighbors and ripening for field-perpendicular neighbors. Incorporating field-dependent diffusivity from experiment slows kinetics as expected; a first-principles-motivated anisotropic diffusivity correction is estimated to be small (<2%). These results establish a process-structure link for magnetically assisted heat treatments of Fe-C alloys and provide guidance for microstructure control via chemo-magneto-mechanical synergism.

cond-mat.mtrl-sci↗

arXiv2025

Influence of temperature, initial grain-boundary bubble density and grain structure on fission gas behaviour in UO$_2$: a 3D hybrid multiscale study

Fission gas swelling and release in UO$_2$ are governed by the coupled evolution of intragranular clusters and bubbles, migrating grain boundaries (GBs), triple junctions (TJs), and their eventual connection to a free surface (FS). We extend a hybrid multiscale framework that couples cluster dynamics (Xolotl) with a phase-field model (MARMOT) to large 3D polycrystals with heterogeneous GB and surface diffusion and evolving GB networks. We simulate 10- and 100-grain UO$_2$ microstructures at 1200 and 1600 K, with and without a FS, to interrogate bubble growth, coalescence, GB/TJ coverage, gas arrival at interfaces, and fission gas release (FGR). At 1200 K, both GB mobility and gas transport are low, yielding negligible bubble and GB evolution. At 1600 K, intergranular bubbles rapidly become lenticular and coalesce into networks while unpinned GBs migrate; fewer initial bubbles reduce coalescence but enhance GB migration due to less pinning and produce spikes in interfacial gas arrival rate due to GB sweeping. Bubble density versus mean projected area agrees with White's (2004) coalescence trend and remains on the left side of the analytical curve, in contrast to several prior simulations, likely due to the inclusion of GB migration. In domains with a FS, early release is rapid and bubbles near the FS collapse to form a denuded zone, suppressing local network connectivity; GB coverage rises and approaches but does not exceed 50%. TJ coverage remains low without preferential nucleation at TJs. To our knowledge, these are the first large-scale 3D mesoscale simulations of intergranular fission gas behavior that provide mechanistic insight and quantitative metrics to inform engineering-scale FGR models.

cond-mat.mtrl-sci↗

arXiv2023

Modeling Fission Gas Release at the Mesoscale using Multiscale DenseNet Regression with Attention Mechanism and Inception Blocks

Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool for understanding how microstructure evolution impacts FGR, but they are computationally intensive. In this study, we present an alternate, data-driven approach, using deep learning to predict instantaneous FGR flux from 2D nuclear fuel microstructure images. Four convolutional neural network (CNN) architectures with multiscale regression are trained and evaluated on simulated FGR data generated using a hybrid phase field/cluster dynamics model. All four networks show high predictive power, with $R^{2}$ values above 98%. The best performing network combine a Convolutional Block Attention Module (CBAM) and InceptionNet mechanisms to provide superior accuracy (mean absolute percentage error of 4.4%), training stability, and robustness on very low instantaneous FGR flux values.

cond-mat.mes-hall↗

arXiv2022

A Novel Physics-Regularized Interpretable Machine Learning Model for Grain Growth

Experimental grain growth observations often deviate from grain growth simulations, revealing that the governing rules for grain boundary motion are not fully understood. A novel deep learning model was developed to capture grain growth behavior from training data without making assumptions about the underlying physics. The Physics-Regularized Interpretable Machine Learning Microstructure Evolution (PRIMME) model consists of a multi-layer neural network that predicts the likelihood of a point changing to a neighboring grain. Here, we demonstrate PRIMME's ability to replicate two-dimensional normal grain growth by training it with Monte Carlo Potts simulations. The trained PRIMME model's grain growth predictions in several test cases show good agreement with analytical models, phase-field simulations, Monte Carlo Potts simulations, and results from the literature. Additionally, PRIMME's adaptability to investigate irregular grain growth behavior is shown. Important aspects of PRIMME like interpretability, regularization, extrapolation, and overfitting are also discussed.

physics.comp-ph↗

arXiv2022

Thermal Energy Transport in Oxide Nuclear Fuel

To efficiently capture the energy of the nuclear bond, advanced nuclear reactor concepts seek solid fuels that must withstand unprecedented temperature and radiation extremes. In these advanced fuels, thermal energy transport under irradiation is directly related to reactor performance as well as reactor safety. The science of thermal transport in nuclear fuel is a grand challenge due to both computational and experimental complexities. Here, we provide a comprehensive review of thermal transport research on two actinide oxides: one currently in use in commercial nuclear reactors, uranium dioxide (UO2), and one advanced fuel candidate material, thorium dioxide (ThO2). In both materials, heat is carried by lattice waves or phonons. Crystalline defects caused by fission events effectively scatter phonons and lead to a degradation in fuel performance over time. Bolstered by new computational and experimental tools, researchers are now developing the foundational work necessary to accurately model and ultimately control thermal transport in advanced nuclear fuel. We begin by reviewing research aimed at understanding thermal transport in perfect single crystals. The absence of defects enables studies that focus on the fundamental aspects of phonon transport. Next, we review research that targets defect generation and evolution. Here, the focus is on ion irradiation studies used as surrogates for damage caused by fission products. We end this review with a discussion of modeling and experimental efforts directed at predicting and validating mesoscale thermal transport in the presence of irradiation defects. While efforts into these research areas have been robust, challenging work remains in developing holistic tools to capture and predict thermal energy transport across widely varying environmental conditions.

cond-mat.str-el↗

arXiv2017

Rapid multiphase-field model development using a modular free energy based approach with automatic differentiation in MOOSE/MARMOT

We present a novel phase-field model development capability in the open source MOOSE finite element framework. This facility is based on the 'modular free energy' approach in which the phase-field equations are implemented in a general form that is logically separated from model-specific data such as the thermodynamic free energy density and mobility functions. Free energy terms contributing to a phase-field model are abstracted into self-contained objects that can be dynamically combined at simulation run time. Combining multiple chemical and mechanical free energy contributions expedites the construction of coupled phase-field, mechanics, and multiphase models. This approach allows computational material scientists to focus on implementing new material models, and to reuse existing solution algorithms and data processing routines. A key new aspect of the rapid phase-field development approach that we discuss in detail is the automatic symbolic differentiation capability. Automatic symbolic differentiation is used to compute derivatives of the free energy density functionals, and removes potential sources of human error while guaranteeing that the nonlinear system Jacobians are accurately approximated. Through just-in-time compilation, we greatly reduce the computational expense of evaluating the differentiated expressions. The new capability is demonstrated for a variety of representative applications.

cond-mat.mtrl-sci↗