The ability to simulate engineering outputs in an industrially relevant time scale is of critical importance. Often, industry requires an understanding of how specified output measures change under the variation of problem parameters. However, performing parameter sweeps is often computational prohibitive, particularly in an industrial design environment and when considering three-dimensional problems. To address this issue there is considerable academic and industrial interest in the provision of fast and computationally inexpensive reduced order models, which can be certified with credibility bounds to ensure reliability in the predictions.

In this PhD project the chosen application of interest is a clinical MRI system. Siemens Magnet Technology are interested in understanding the coupled electro-magneto- mechanical response of the conducting components making up an MRI scanner and the effects that changes in its design have on its performance and vibrations. To do this, the coupled set of elasticity equations, for describing the mechanical response, and Maxwell equations, for electromagnetic response, must be solved.

The PhD will first develop a computational framework based on advanced finite element technology and efficient algorithms for the solution of the resulting non-linear equation system. Given the physics involved and the complexity of the problem at hand, this tool will then be used for generating solution “snapshots” for certain designs. The PhD will then develop a low-fidelity reduced order modelling technique capable of rapidly producing simulation outputs for new design parameters of a realistic three-dimensional configuration. The resulting algorithm will then be transferred to Siemens Magnet Technology for use in an industrial context. The PhD will become a member of an active research group working on the development and application of computational techniques in coupled problems.


PhD candidate: Guillem Barroso (Swansea University, Siemens, Universitat Politècnica de Catalunya)

Academic supervisors: Prof. A.J. Gil, Dr. P. Ledger (SU), Prof. A. Huerta, Dr. S. Zlotnik (UPC)

Industrial supervisor: Dr. M. Mallet (Siemens)