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supported by the Enhanced Composite and Structures Centre at Cranfield. About the sponsor We will work in collaboration with Cambridge Nanosystems, which is a world leading high quality, high performance
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Energy storage and harvesting and Dr Lorenzo Conti , granular locomotion pioneer, will provide support across heat transfer modelling, computational simulation, microbial risk assessment and low-carbon
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Verification Tools: Develop AI algorithms that automate the verification process, ensuring systems meet required safety and performance standards. Health Monitoring Algorithms: Implement AI-based monitoring
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temperatures in modern Gas Turbines. During the operation of gas turbines, such high temperatures are coupled with the impurities or ash compounds like Sulphur, halides, sodium and vanadium. In certain cases
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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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The research in this doctoral opportunity will develop a failure model that can represent the combined effect of surface and bending failures in gears to perform reliable health prognostics. Lack
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partners, (Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD and Alstom); and from EPSRC. The investment, over the first 5 years of operation, was approaching £10M. We are now in our eighth year of
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due to a lack of resource. With some water-hungry sectors (such as data centres and other high-tech industries) prioritised for significant growth in water stressed regions, these challenges are set to
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integrated with human expertise, leading to enhanced system performance and sustainability. The project aims to create a foundation for systems that can evolve autonomously while benefiting from continuous
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for the collection of data to develop and validate prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection