Improved heat transfer understanding via conjugate heat transfer, co-simulation and AI approaches

Research has shown that the development of gas turbines is critical to the success and environmental impact of future passenger aircraft concepts. The understanding of convective heat transfer at conditions such as those experienced in a gas turbine continues to be a challenge, with adverse consequences for overall thermal efficiency and component life (environmental impact, sustainability and safety). While standard analytical models for heat transfer exist for classical cases, their validity often does not extend to the operating environment of a gas turbine, often neglecting important engine-realistic geometric features and transient operation. 

In this PhD project, you will apply Computational Fluid Dynamics (CFD) and Conjugate Heat Transfer (CHT) modelling, which captures both the fluid & solid domains, as required to develop this understanding for engine-representative geometries and where the conventional fluid-only approach becomes limited. You will validate your models against a range of test cases and engine data and develop heat transfer correlations for instances where in-engine heat transfer behaviour is known to depart from standard analytical correlations. Under the combined supervision of academics in mechanical engineering and computer science, you will consider the use of data-driven approaches within these multi-parameter models to produce faster and more robust correlations and tools that can be incorporated within industrial methods and have an impact on future designs.

You will be part of the Rolls-Royce University Technology Centre in Thermofluid Systems. You will have the opportunity to interact directly with engineers at Rolls-Royce plc and to share your research findings at leading international conferences.

Open to candidates who pay UK/home rate fees. See UKCISA for further information.

You will be an enthusiastic and self-motivated individual with a 1st class or 2:1 honours degree in mechanical/aerospace engineering or a related subject. You will demonstrate disciplined and consistent working habits, independent thinking, and adopt a rigorous and detail-oriented approach to research. Experience with numerical modelling and simulation techniques and software packages would be an advantage. Programming skills in languages such as Python, C++, MATLAB, are desirable, as is an awareness of machine learning or other AI methods, or willingness to develop these skills.