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Integrating numerical modelling with artificial intelligence for the monitoring of systems and structures, PhD Engineering – (Funded)

  • Full or part time
  • Application Deadline
    Monday, April 08, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, is inviting applications for a fully-funded PhD studentship to commence in September 2019 or as soon as possible thereafter. For eligible students the studentship will cover full tuition fees plus an annual tax-free stipend of at least £15,009 for 3.5 years full-time, or pro rata for part-time study. The student will be based in Engineering in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter.

Project Description

Structural Health Monitoring (SHM) is a highly active research area which aims at using different technologies to monitor the health of a structure. This is usually done by the collection and subsequent processing of data through networks of sensors. The applications of SHM can be numerous and the cost and safety benefits significant for various industries including aerospace (civil and military), civil infrastructure, and renewable energies (e.g. wind turbines) among others. Ultimately, the vision for the use of SHM technologies may be formulated as an intelligent maintenance strategy for structures, rather than the currently outdated, costly, and often unreliable scheduled maintenance routine.

This project will focus primarily on vibration-based SHM with a machine learning approach. Such an approach will require the availability of reliable training data which may be hard to acquire when dealing with expensive structures – such as aircraft. The project will thus aim to develop solutions to the lack of training data. A combination of suitable experimental strategies and numerical modelling, such as Finite Element Analysis (FEA) along with artificial intelligence is expected to be a key to the project’s success. The algorithms developed in the project are expected to have a general application on a range of different structures or systems, but the primary focus will be on aerospace structures or wind turbines.

The ideal candidate should have a suitable engineering degree (e.g. mechanical/aerospace engineering) or equivalent, and strong numerical modelling (FEA) skills. Experience or familiarity with machine learning approaches or with experimental testing, e.g. vibration testing, will be an advantage. Candidates with a more mathematical or computer science background, but with an interest on the practical application of machine learning on real systems, and the drive to learn about numerical modelling are also encouraged to apply.
The successful applicant will be embedded in a thriving research environment, which includes the Vibration Engineering Section (VES) at the University of Exeter. VES has extensive laboratory and field testing experimental facilities, which have been significantly upgraded in the last three years through extensive investment by the University. This includes a range of actuators, sensors and data acquisition and control systems that are also suitable for use in this research project. In addition, VES has state-of-the-art analytical facilities, including FE codes, numerical analysis and control simulation.

The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence in September 2019.

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