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

  • Full or part time
  • Application Deadline
    Monday, August 31, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Supervisor: Andrea Da Ronch

Project description

This is a unique opportunity for an enthusiastic and self-driven student to join a vibrant team with a well-proved expertise in multi-disciplinary computational aero-sciences driven by the technologies needed for next-generation aerospace systems.

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.

The successful applicant will have an excellent background in physics, engineering or applied mathematics, and programming experience. It is desirable to have familiarity with a computational fluid dynamics software or experimental fluid mechanics. The three-year studentship is available to UK/EU students only and the stipend is at the standard EPSRC-level. Applications are welcome from outstanding overseas applicants. However, the applicants must make appropriate arrangements to cover the difference between the overseas and UK tuition fees.

If you wish to discuss any details of the project informally, please contact Dr Andrea Da Ronch, Atmospheric Flight Mechanics Research Group, Email: , Tel: +44 (0) 2380 59 4787.

Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 31 August 2020 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: full tuition fees for EU/UK students plus for UK students, an enhanced stipend of £15,009 tax-free per annum for up to 3.5 years.

How To Apply

Applications should be made online here selecting “PhD Eng & Env (Full time)” as the programme. Please enter Andrea Da Ronch under the proposed supervisor.

Applications should include:
Research Proposal
Curriculum Vitae
Two reference letters
Degree Transcripts to date
Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page

For further information please contact:

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

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