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cost-effectiveness of forever chemicals removal from water. The model will use experimental data gathered during the project through testing. The successful candidate will work closely with the project
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image correlation is an effective tool to characterize material properties. The analysis of the images can provide a fair assessment about the changes in material behaviour under different operational
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profoundly affect their mechanical properties and overall performance. Therefore, understanding the temperature field and developing effective thermal control techniques are vital to ensuring a high-quality WA
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introduce the requirement for giving focused and increased attention to hot corrosion phenomenon. Contrary to oxidation, hot corrosion can result in erratically rapid material loss, compromising
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or second order effects. By utilising existing forensic techniques alongside engineering approaches, this project will provide previously unidentified insights into the relationships between UAS damage types
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usability and accuracy, as well as conducting field tests to validate their effectiveness. Additionally, the research will explore the economic viability of these sensors to enhance real-time data collection
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has a dramatic effect on the fate of a component—a part may survive millions of cycles in vacuum and last only a few hundreds of thousands in air and much lower in actively corrosive environments
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can be costly, energy-intensive, and increase carbon emissions. Nature-based solutions (NbS), such as integrated constructed wetlands (ICWs), offer sustainable alternatives that combine effective
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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fibre reinforced composites are weak in their through thickness direction. This weakness can result in parts failing by delamination in service, either from external loads or impact events. The presence