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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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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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research opportunity focuses on advancing large-scale additive manufacturing using metal wire as feedstock and electric arc as the heat source. The project aims to develop an innovative and efficient method
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will
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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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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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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