47 molecular-modeling-or-molecular-dynamic-simulation PhD positions at Cranfield University
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limitations in both measurement and modelling techniques. Current in-process measurement methods are restricted to surface-only monitoring devices (e.g., cameras and pyrometers), which fail to capture
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Computing Strategies: Develop algorithms that dynamically adjust computing resources to minimize energy usage without compromising performance. Thermal Management Techniques: Design systems that effectively
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for the collection of data to develop and validate prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection
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for the collection of data to develop and validate prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection
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The research in this doctoral opportunity will develop a failure model that can represent the combined effect of surface and bending failures in gears to perform reliable health prognostics. Lack
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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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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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. The project is co-sponsored by Spirent Communications, a world leader in navigation and testing technology. Spirent will provide advanced simulation tools, expert support, and industry placements to help make
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. The project delves into areas such as hardware-based security measures, tamper detection, and the integration of explainable AI models within embedded platforms. Situated within the esteemed IVHM Centre and
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modelling to provide a robust framework for integrating nature-based solutions into SO management. This can alleviate the pressure on treatment infrastructure and reduce dependence on grey infrastructure