Degree Type


Date of Award


Degree Name

Doctor of Philosophy


Aerospace Engineering


Aerospace Engineering

First Advisor

Paul Durbin


Predicting drag over complex bodies plays a crucial role in the design of high performance engineering applications such as aircraft and naval vessels. Current turbulence models are known to give erroneous predictions of onset of separation and reattachment lengths. Recent years have seen an increase in availability of high fidelity data sets; and thus, data driven modelling is now being tested as a potential tool to improve turbulence closure models. In line with this goal, the present study aims to evaluate the machine learning as a means to augment turbulence modelling.Empirical data is obtained for a series of increasingly high bumps by Large Eddy Simulation.A patch of high turbulent kinetic energy forms in the lee of the bump and extends into the wake.It originates near the surface and has a significant influence on flow development. The highest bumps create a small separation bubble. Over the bump the log-law is absent, evidencing strong disequilibrium. The data set is created to be used in data-driven modelling.An optimization method is used to extract fields of variables that are used in turbulence closure models. From this, it is shown how these models fail because they predict near-wall eddy viscosity erroneously. Machine learning is used to generalize the optimized field variables such that existing turbulence models can produce more accurate results on different test cases. It is shown that these machine learning augmented closure models result in a modest improvement in test cases.

Copyright Owner

Racheet Matai



File Format


File Size

105 pages