Sparsity-promoting reduced order modelling techniques for separated turbulent flows

Lead Research Organisation: University of Southampton
Department Name: Faculty of Engineering & the Environment


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.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509747/1 01/10/2016 30/09/2021
1939255 Studentship EP/N509747/1 25/09/2017 30/09/2020 Riccardo Rubini
Description We developed a mathematical framework that is able to identify the most relevant term in the dynamical system representing the reduced order model of a turbulent flow. We have shown that this framework is able to identify the most relevant interactions according the established picture of energy interactions of two and 3 dimensional flows.
In addition, we have shown that this methodology works regardless the mathematical representation chosen of the flow field.
Exploitation Route Provide a solid mathematical and conceptual background to extend this framework to more and more complex flow configurations. In addition, we showed that energetic interactions in turbulent flows are intrinsically sparse. This could lead to the development of a new family of modal decompositions able to generate reduced order models that automatically incorporates this physical features in the mathematical formulation of the model.
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Education