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Field
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
at scale? Digital twins offer a promising foundation, but to truly support engineering decisions, they need to go beyond simulation and begin to interpret and reason about the systems they represent
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comparatively strong interaction between bilayers of these materials offers interesting perspectives for materials design. In this spirit, the successful applicant will use state-of-the-art atomistic simulation
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, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
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experiments; supporting other group members with data analysis and interpretation from both simulations and experimental data; and use the developed framework to design new materials with optimised performance
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only for up to 4 years full-time or up to a maximum of 6 years if studying on a part-time (0.5 FTE) basis How to apply: Send a copy of your CV and a 300-word statement about why you are interested in
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experiments; supporting other group members with data analysis and interpretation from both simulations and experimental data; and use the developed framework to design new materials with optimised performance
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that simulates real-world environmental conditions to test the durability and longevity of these materials and products made thereof, is also required. This PhD project aims to investigate novel high-performance
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model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas film flow within the microscopic seal gap. Couple CFD with Structural Models: Study the fluid-structure
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4-year D.Phil. studentship Supervisors: Dr Simone Falco, Prof Daniel Eakins The ability to simulate initiation and detonation effects within energetic materials is a significant capability gap