Sparsification of reduced order models for fluid and fluid-structure problems

Numerical modelling of complex engineering problems has become one of the most important steps in efficient design and analysis of aerospace systems. However, due to the complexity of the physics and the computational modelling of these large-scale dynamical systems, computational costs may be prohibitive. Consequently, predictive models and control schemes that cannot account for or take advantage of efficient algorithms have very limited success. The central question posed in this PhD project is: Can we develop a sparsely-interconnected reduced order model, combining data-driven learning with a physics-based nonlinear reduced order modelling technique?
The PhD project builds on the methodology developed by Dr Da Ronch for coupled, non-linear systems. The resulting nonlinear reduced order model contains a quadratic tensor, with size growing as the cube of the selected modes. The overarching idea is to develop a framework, which is both model- and data-driven, to extract a compact, reduced representation of the reduced order model. Sparsity features of the model are maximised by appropriate machine learning algorithms that identify the relevant interactions. The work will consider fluid and fluid-structure problems.

The successful applicant will be encouraged to further develop analytical and computational skills, work closely with team members, and submit the research results to high-quality journals. The applicant is also expected to visit one or more overseas institutions throughout the duration of the PhD programme. After three years of study, the successful applicant will be well-prepared for a rewarding industrial or academic career, leveraging on the network of contacts created as part of the research project.