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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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attracted intense interest from both academia and industry recently for its attempt to extract more useful information and knowledge from Big Data. Big Data analytics will help to develop more advance
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performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in
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propulsion systems. You’ll join the wider CDT multidisciplinary cohort that values equity, diversity, and inclusion, while gaining expertise in aero-engine aerodynamics, analysis of advanced experimental data
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
amounts of maintenance and operational data, from sensor streams to technical logs, yet much of it remains unstructured, fragmented, and underused. Hidden within these records are insights that could help
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application form. For further information please contact: Professor Weisi Guo weisi.guo@cranfield.ac.uk
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existing data analytics tools will help deploy these technologies in the industry context without the need for big datasets. Predictive Maintenance (PdM) is one of the maintenance strategies that has
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with a wealth of social and networking opportunities. How to apply For further information please contact: Name: Professor David MacManus Email: d.g.macmanus@cranfield.ac.uk If you are eligible to apply
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information please contact: Name: Professor Daniel Auger Email: d.j.auger@cranfield.ac.uk If you are eligible to apply for this studentship, please complete the online application form. This vacancy may be
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flow visualisation and measurement techniques to study droplet impact under icing conditions to improve icing codes that aid in design and development of ice detection and mitigation system