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. Project Overview The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower
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them for specific tasks. The project will combine: Mathematical modelling of dynamical systems; Computational photonics simulations; Comparison with real physical systems (especially photonic systems
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Rolls-Royce and EPSRC funded PhD - Experimental and numerical studies into the wear of articulating spline couplings for aeroengine applications Applications are invited for an EPSRC Industrial
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theory and framework to study and explain how different reservoir systems work and how to design them for specific tasks. The project will combine: Mathematical modelling of dynamical systems
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. Skills in numerical tools and programming are desirable (MATLAB, python, C++ etc). Any experiences with engineering design, structural/aerodynamic/aeroelasticity modelling, manufacturing/assembly process
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for applications across automotive, aerospace, and power generation. Starting from modelling and parametric design of complex 3D laminated and hybrid cores, the PhD student will design and develop new motor
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couplings, this project will seek to further the fundamental understanding the wear behaviour of such components through both experimental and numerical studies. Experimental work will be carried out using a
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steel technology enabled by advanced manufacturing processes and emerging magnetic materials for applications across automotive, aerospace, and power generation. Starting from modelling and parametric
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). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall
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the manufacturing process. The embedded optical fibre sensing will be used in conjunction with advanced numerical models for monitoring of composites manufacturing and structural performance. Motivation High