|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||From £17,668 For UK students, Tuition Fees and a stipend, tax-free, per annum for up to 3.5 years.|
|Placed On:||20th February 2023|
|Closes:||5th April 2023|
Project title: Characterisation of cast austenitic stainless steels using ultrasonic backscatter and artificial intelligence
Supervisory Team: M.K.Kalkowski and T.Blumensath
Do you enjoy numerical modelling? Would you like to apply artificial intelligence to solving engineering problems? If positive, this is a project for you. Its main aim is to combine the power of finite element modelling with artificial intelligence to develop ultrasonic characterisation of castings in-situ. While helping improve the way the industry manages safety-critical assets, you will develop a transferrable skillset comprising numerical modelling, large data analysis and AI, which would prove useful on many potential future career paths.
Stainless steel castings possess outstanding corrosion resistance and mechanical properties. For these reasons, they underpin numerous safety-critical installations and are ubiquitous in, e.g. nuclear power plants. However, their excellent performance comes at a cost - they are challenging to inspect for damage in situ. Ultrasound - the preferred inspection method - suffers from the coarse-grained microstructure, which scatters and attenuates injected acoustic energy making reflections from defects illegible. The knowledge about the microstructure before testing offers game-changing possibilities. For instance, it helps select the correct procedure for a specific defect which is a crucial maintenance challenge. Unfortunately, such information is currently only available from destructive tests.
Your PhD will pave the way towards in-situ microstructure characterisation using ultrasound and help transform common inspection practice. You will employ artificial intelligence to support developing the physical understanding of how microstructure reveals itself in ultrasonic backscatter. Learning from data, including choosing a suitable AI methodology, and proposing in-situ characterisation methods is the promise of this project. You will use Southampton's High-Performance Computing facility, and advanced material characterisation techniques in the School of Engineering. The true potential of the methods you propose will be showcased using ultrasonic measurements on samples of industrial relevance.
As a PhD student, you will be a part of the Dynamics Group within the University's Institute of Sound and Vibration Research. ISVR is a member of the UK's Research Centre for Non-Destructive Evaluation (RCNDE - https://rcnde.ac.uk/) and runs projects in both ultrasonic and X-ray imaging (including iWeld - https://iweld-euratom.org/ - recently funded by EURATOM). You will contribute to developing the exciting capability in AI-powered NDT, working alongside researchers using acoustics to interrogate and characterise structures of different scales and complexities.
Feel free to contact Michal Kalkowski (email@example.com) for an informal chat about the project and pursuing a PhD in Southampton. We look forward to hearing from you!
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). Background in finite element modelling and Python is highly desirable.
Closing date: applications should be received no later than 05 April 2023 for standard admissions, but the ad may close earlier if a suitable candidate is found.
Funding: For UK students, Tuition Fees and a stipend of £17,668 tax-free per annum for up to 3.5 years.
How To Apply
Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2023/24, Faculty of Physical Sciences and Engineering, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Michal Kalkowski
Applications should include:
For further information please contact: firstname.lastname@example.org
Type / Role: