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can improve the temperature prediction, the predictive accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including
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spatiotemporal distributions of thermal variables, suffer from limited predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence
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computational approaches to validate a computational predictive tool amenable to engineering applications. The candidate will cooperate with leading industrial partners to link fundamental materials research
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. Computational modelling, digital twin development and experimental validation will be combined to support more sustainable and resilient offshore wind systems. The project is hosted by Cranfield
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research will be conducted at Cranfield University within a supportive, cross-disciplinary environment spanning energy systems engineering, computational modelling, bioinformatics and food safety
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intake flow distortion based on synchronous pressure and velocity measurements and computational datasets. The research will focus on the flow distortion that arises in a complex aero-engine intake
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equivalent in a related discipline. This project would suit students with an aerospace or mechanical engineering background. Experience of icing science and engineering, computational fluid dynamics
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. The need for cost-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on
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laboratories and research facilities will support both computational and experimental research. The student will develop advanced skills in digital twins, AI, physics modelling, structural health monitoring
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. Applicants who are curious, creative, eager to learn and interested in addressing complex engineering challenges through experimental and computational research are welcome. We value diverse perspectives and