Data-Driven Approaches to Understanding and Predicting Aircraft Icing Effects
Not stated
- Funding
- Self-Funded PhD Students Only
- Application deadline
- Year-round applications
About the project
About the Project Aircraft icing is a well-recognised hazard that occurs when supercooled water droplets in the atmosphere freeze upon contact with aircraft surfaces. The accumulation of ice on wings and control surfaces can significantly alter aerodynamic characteristics, leading to increased drag, reduced lift, and potential loss of control. Despite ongoing research efforts, ice formation remains difficult to predict accurately due to its inherently complex and variable nature, influenced by a wide range of environmental and operational factors. Experimental studies and numerical simulations have provided valuable insights, but they are often limited in scope and can be costly and time-consuming to conduct. As aviation systems continue to evolve, there is a growing need for improved understanding and more efficient approaches to assessing icing effects across a wider range of conditions. This PhD project aims to explore advanced computational and data-driven approaches to improve the understanding and prediction of aircraft icing phenomena. The research will involve the use of numerical simulation tools to examine ice formation and its impact on aerodynamic performance under representative conditions. In addition, existing experimental and computational datasets will be reviewed and analysed to identify key trends and relationships. Building on this knowledge, the project will investigate the potential of modern data analysis and machine learning techniques to support the development of predictive capabilities for icing behaviour and its aerodynamic consequences. The emphasis will be on creating approaches that are both practical and adaptable, with potential applications in aircraft design, performance assessment, and operational decision-making. The ideal candidate should have a strong background in aerospace engineering, mechanical engineering, or a related discipline, with a solid understanding of fluid dynamics and aerodynamics. Experience with computational methods and programming is desirable, and an interest in data analysis or machine learning techniques would be beneficial. The project is suitable for a motivated and curious individual who is keen to work across traditional disciplinary boundaries, combining engineering knowledge with emerging data-driven approaches. Previous experience in aircraft icing is not required, and appropriate training will be provided. The candidate will be expected to develop independent research skills and contribute to academic publications and dissemination activities.