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This PhD project will focus on developing AI-based methods to accelerate the Swansea University in-house discontinuous Galerkin (DG) finite element solver for the Boltzmann-BGK (BBGK) equation
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-cycle fatigue. The research methods are based on both small-scale and full-scale experimental testing and on Finite Element Modelling. Are you motivated to take a step towards a doctorate and open
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aluminium in high-value products produced by mega-casting. The main objective of the PhD project is to develop a finite element analysis (FEA) framework that can accurately predict the mechanical properties
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Loadability and robustness under dynamic or off-axis forces You will combine analytical modeling, finite element simulations, and experimental validation to explore new coupling topologies and materials
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publication Strong programming skills and familiarity with machine learning or finite element modelling Not currently receiving another scholarship of equal or higher value Application process Future student
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coupled computational framework capable of predicting crack initiation, propagation, and component failure under realistic operating conditions. Key Objectives: - Develop a finite element-based chemo-thermo
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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of the particle fuel, crack initiation/propagation and failure mechanisms in relation to test temperature. Finite element (FE) modelling using FE tools such as Abaqus, (or) Ansys, (or) COMSOL is optional
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fields, and risk damaging the part during fabrication. Finite element analysis (FEA) models, while capable of delivering detailed spatiotemporal distributions of thermal variables, suffer from limited
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or replace established methods from computational engineering and computer simulation (such as the finite element method) to represent and exploit relationships along the composition-process-structure-property