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Joint PhD with RWTH Aachen: Use of machine learning to optimise turbulence modelling

  • Full or part time
  • Application Deadline
    Thursday, February 20, 2020
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Industry relies heavily on turbulence models to make numerical predictions sufficiently affordable. However, in many situations the lack of predictive accuracy of the underlying models limits the impact that computational methods can have on technology development.

In this project, a novel machine learning approach will be applied to translate the physics contained in data into tangible turbulence models with improved accuracy. In particular, this will be done by fusing the machine learning process with an a-posteriori evaluation of the novel models into a single, integrated framework. This will ensure that the models are useable, robust, and can be implemented easily.

Supervisors:

The University of Melbourne: Richard Sandberg and Mohsen Talei.

RWTH Aachen: Jens Göbbert, Antonio Attili, and Heinz Pitsch.


If successful, the PhD candidate will be enrolled at both the University of Melbourne and RWTH Aachen. The candidate will be co-supervised by supervisors at both institutions, will be based at the University of Melbourne and will spend at least twelve months at RWTH Aachen.

Funding Notes

The application process is competitive, and the applicant will need to meet the entry requirements of both institutions.

Information on the University of Melbourne entry requirements: View Website.

Information about the Joint PhD Program between the University of Melbourne and RWTH Aachen: View Website

The candidate's scholarship will include a 100% fee remission scholarship, a living allowance for three years with a possible 6-month extension, and funding for travel.

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