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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
intelligent reasoning and feedback mechanisms into digital twin environments, enabling them to interpret complex maintenance data more effectively. Using AI techniques, such as large language models, knowledge
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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collaboration in time-critical tasks. By integrating foundation models like large language models (LLMs) with physically embodied agents (e.g., drones or vehicles), the research focuses on enabling adaptive
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the operational life of battery-powered devices and reducing the environmental impact of large-scale deployments. Advancements in this area support the development of sustainable technologies across various
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areas. Cranfield is part of the national testbed for 6G, researching in the following areas of interest: Real-time specification of 6G telecommunication and edge computing services using Large Language
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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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coefficients. This strategy carries large uncertainty and requires vast amount of expensive and time-consuming experimental data. Worse, sometimes the experimental data is simply inaccessible. The need for cost
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methods in the past. A piece of comprehensive computer software, Pythia with the corresponding capabilities have been developed and tested successfully in several industrial applications. The software can
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and Security (CDS) in Cranfield University is known for its world-class expertise and unrivalled large-scale facilities and partnerships with industry, government and business allowing to create, curate
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised