Data-driven fluid dynamics

The aim of this project is to harness the power of statistical analysis to maximise the information we obtain about the dynamics of a fluid flow. In fluid dynamics, simulations and experiments are often carried out with the intention of obtaining as much information about the velocity field as possible. However, fluid flows are difficult to study and control. Having a simplified model of a flow can lead to better understanding of its governing physics and can also be utilised to facilitate flow control.
The main focus of this project is to develop tools for reduced-order modelling and flow feature identification based on data obtained from experiments or numerical simulations. To this end various statistical and optimisation based tools including modal analysis, graph theory, pattern recognition and machine learning will be used. In this project we try to see the extent to which these tools can be used to maximise the amount of information one can extract from the data, despite the limitations of the experimental methods used to acquire them. We also investigate the best ways to model the dynamics of the flow and the extent to which a model based on such analysis can be relied on, in applications such as flow control and state estimation. As a dataset captures certain information about the flow, we also explore the possibility of using these methods to design an experiment such that it maximises the captured information.
The findings of this project can be used in any field where acquiring large datasets is easier than discovering or simplifying the governing equations of the system.