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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
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maintenance. However, current technologies are relatively slow and not capable enough to provide quick performance, diagnostic and prognostic predictions for real time applications. With the rapid development
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on previous research at Cranfield, which has demonstrated the benefits, the project investigates the impact of various porous structures on aerodynamic performance. Focus is placed on the entire incompressible
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explore the nonlinear structural dynamics of LGSs to fully understand the complexity of their control. They will use this foundation to explore idealised and realistic control laws to virtually “stiffen
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intelligent systems aim to optimize power usage without compromising performance, employing strategies like power-aware computing and thermal-aware optimization. These systems are crucial in extending
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FPGA/ASIC architectures that dynamically reconfigure based on AI workloads, optimizing performance, energy efficiency, and functionality. 3- Intelligent Interface Design: Create smart interfaces
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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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. Offering fully funded, multidisciplinary PhD
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through advanced modelling and simulation. A key objective is to validate and optimize poroelastic finite element models of brain tissue, making them more accurate and clinically relevant. Additionally
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scalable surface engineering methods and state-of-the-art permeation analysis techniques, the project will optimize coatings for alloys such as steel, aluminium, titanium, and nickel. The project will use a