http://bura.brunel.ac.uk/handle/2438/22 Sat, 20 Jun 2026 12:56:03 GMT 2026-06-20T12:56:03Z http://bura.brunel.ac.uk/handle/2438/33446 Title: Editorial 'Multi-Scale Simulation of Metallic Materials (2nd Edition)' Authors: Fang, C Abstract: Metallic materials are some of the most important engineering materials. Current developments in our society are leading to an increasing demand for diverse novel metallic materials with desirable properties. ... Sat, 28 Feb 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33446 2026-02-28T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33445 Title: Multi-Scale Simulation of Metallic Materials (2nd Edition) Authors: Fang, C Editors: Fang, C Abstract: Metallic materials include elemental metals and compounds or alloys. They are important engineering materials and are additionally widely utilized in many new fields. Present developments have led to an increasing demand for diverse new metallic materials in addition to sustainable recycling, digital manufacturing, and environment- and climate-friendly production of devices and parts. Therefore, obtaining comprehensive knowledge regarding metallic materials on scales ranging from the atomic, micro-, meso-, and macroscopic levels has gained importance as of late. Correspondingly, multiscale simulations that combine existing and emerging methods are being employed to incorporate the wide range of time and space scales that are inherent to various disciplines. This Reprint aims to improve our understanding of the structural, microstructural, and physical properties of complex metallic materials via multiscale approaches, including thermodynamics, finite element methods, and ab initio molecular dynamics simulations. Fri, 24 Apr 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33445 2026-04-24T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33444 Title: Physics-informed data-driven modelling of aluminium processing Authors: Alizadeh, Amir Abstract: In this thesis, the integration of physics-based insights and experimental data in the modelling of precipitation hardening (PH) is investigated. To set up the context, a fitting parameter is calibrated in a partial differential equation (PDE) using Physics-Informed Neural Network (PINN), a machine learning (ML) framework that integrates physical laws in a data-driven neural network (NN). A one-dimensional Burgers equation is employed as a representative test case. PINN merges data-driven and physics-based pathways, where the loss function penalises deviations from experimental data and physics-based PDE residual. Through systematic studies, the effects of NN hyperparameters (depth, width), training dataset size, and parameter initialisation are examined. Results demonstrate PINN’s strengths in robust convergence to the true parameter values even under data-scarce conditions. Gradient-based optimisation ensures physical plausibility, especially in cases of poor or even physically incorrect initial guesses, where gradient-free methods often fail. These findings highlight the advantages of a well-established integration framework, a reliable model structure, and experimental data as important tools for parameter estimation, setting the foundation for the subsequent part of the thesis. PH is the primary mechanism for strengthening 6xxx series aluminium alloys. The characteristics of the precipitates play a crucial role in determining the mechanical properties. Particularly, predicting yield strength (YS) based on microstructure is experimentally complex and costly because its key variables such as precipitate radius, spacing, and volume fraction are difficult to measure. Physics-based modelling (PBM), such as Kampmann–Wagner Numerical (KWN), has emerged to tackle these complications utilising advancements in simulation environments. Nevertheless, these approaches require numerous free parameters and ongoing debates over governing equations. Conversely, purely data-driven models struggle with insufficient datasets and physical interpretability. Moreover, the complex dynamics between internal model variables have led both approaches to adopt heuristic optimisation methods, such as Powell or Nelder-Mead, which fail to exploit valuable gradient information. To overcome these issues, a gradient-based optimisation for KWN is proposed, incorporating CALPHAD (CALculation of PHAse Diagrams) and a strength model. The novelties include facilitating differentiability via smoothed approximations of conditional logic, adding regularisation terms to the loss function to maintain the physical relationship between parameter instances, and using per-parameter learning rates for each instance of the fitting parameters. To reduce the computational complexity, a single size-class and a single precipitate type are assumed. Model calibration is guided by a mean squared error (MSE) loss function that aligns the YS predictions with interpolated experimental data using L2 regularisation for penalising deviations from a purely PBM structure. A comparison shows that gradient-based ADAptive Moment Estimation (Adam) outperforms gradient-free Powell and Nelder-Mead by converging faster, requiring fewer PBM evaluations, and yielding more physically plausible parameters. Finally, the model output is validated with a second set of composition and process parameters, with microstructure data, and a unit test, highlighting the power of gradient-based calibration techniques in the modelling of 6xxx series PH. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London Wed, 01 Jan 2025 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33444 2025-01-01T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33443 Title: First-principles Study on Silicon Stabilization of the Cubic α- and Hexagonal α’-FeAl Phases Authors: Fang, C; Que, Z; Fan, Z Abstract: Commercial aluminum (Al) metals contain unavoidably iron (Fe) and silicon (Si) as impurities. Due to its low solubility and high chemical affinity to Al, Fe exists in the form of Fe-containing intermetallic compounds (Fe-IMCs) which act crucially in solidification processes, determining the micro-structure and consequently the mechanical performance of the cast parts. Meanwhile, Si as impurity or addition may join the binary Fe-IMCs. Here, we investigate the Si stabilization effects on the frequently observed Al-rich Fe-IMCs in a comprehensive and systematical way using a first-principles density-functional theory (DFT) approach. The study revealed different Si stabilization effects on the cubic α- and hexagonal αʹ-phase, as well as other binaries: Al₁₂Fe, η-Al₆Fe, τ₄-, β- and θ-phases. The enhancement of stability for the α-phase is moderate while it is strong for the αʹ-phase. For the stability series (from higher to lower) is θ-Al1₃Fe₄ > η-Al₆Fe >α-Al₄.₇₅Fe in the binary system, while it becomes τ₄-(Al,Si)₅Fe>β-Al₄.₅SiFe >αʹ-(Al,Si)₄.₁₇₄Fe for the ternary Fe-IMCs. The information obtained here helps understand the formation of Fe-IMCs particles during casting of Al-Si alloys, and design of novel Al alloys of fine micro-structures and desired mechanical performances of the products from the primary Al and the scraps and wastes. Description: A preprint version of the article is available at https://www.preprints.org/manuscript/202605.1023 under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.. It has not been certified by peer review. Thu, 01 Jan 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33443 2026-01-01T00:00:00Z