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will dynamically adjust turbine parameters such as yaw, pitch, and torque to maximize Annual Energy Production (AEP) while minimizing component stress. Additionally, a hybrid predictive maintenance model
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opportunity for personal and professional growth. As part of the research, the student will have opportunities to attend leading international conferences, where they can present findings, engage with experts
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elements like Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) to secure hardware components. Embedded Trust Protocols: Design protocols that establish and maintain trust within
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systems that continuously assess the health of components, predicting failures before they occur. Compliance Assurance Techniques: Design AI-driven methods to ensure ongoing compliance with industry
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that facilitate seamless integration between AI hardware components and embedded systems, ensuring efficient data flow and processing. Cranfield University offers a distinctive research environment renowned for its
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and hyper-velocity impact protection which can be used for satellite structural components. This project will develop novel composite structures using combination of high energy absorbing textile fibres
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, or marine applications. The critical components of sCO2 power generation systems, such as heat exchangers, compressors, turbines, condensers, and the whole system integration will be simulated
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Join our diverse and inclusive team to transform the future of aviation as part of the UK’s EPSRC Centre for Doctoral Training in Net Zero Aviation. The CDT in Net Zero Aviation is the world’s first
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. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network
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utilise numerical techniques including the finite element method to describe biofluid flow and deformation in the human brain tissue. Parameters are inferred from clinical data including medical images