Sparsity-promoting reduced order modelling techniques for separated turbulent flows

The main objective of this PhD project is to develop a novel model sparsification technique that produces sparsely-connected ROMs incorporating the key features of high Reynolds number turbulence. In a sparsely-connected ROM only the dynamically relevant energy transfers are retained, hence the computational cost associated to simulation can be significantly reduced. The focus of the research will be on 1) developing data-driven methods to unravel the sparsity, using well-established statistical and machine learning techniques, 2) correlate these methods to the underlying physics of the problem and 3) perform an extensive parametric investigation on the interaction between Reynolds number, sparsity and computational costs. The modelling technique will be demonstrated in a test case involving a representative turbulent separated flow, for which fully-resolved numerical simulations will be performed on IRIDIS, the high performance computing facility of the University of Southampton.