Bayesian Digital Twinning of Gradient Materials Tests for Accelerated Alloy Qualification

About the Project

This project is part of cohort 3 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute.

This PhD project will create a Bayesian framework that is guided by physics for identifying parameters and simulating gradient-based materials experiments. The technical starting point is a clear limitation in high-temperature qualification of new alloys. Currently, constitutive parameters are often obtained from serial isothermal tests, where one specimen corresponds to one condition. This method is slow and results in sparse datasets. In contrast, electro-thermal mechanical testing with controlled spatial temerature gradients allows a single specimen to capture various local thermo-mechanical states. When combined with synchronised machine signals, digital image correlation, thermal imaging, and microstructural characterisation after testing, such experiments produce dense multimodal datasets. The unresolved scientific issue is that the related inverse problem is not well defined. It is unclear which parameters can be uniquely recovered, under what conditions, and with what uncertainty.

The project will tackle this issue by linking finite element forward models with Bayesian inference. The forward model will enforce thermo-mechanical balance under measured boundary conditions and prescribed temperature fields. It will predict displacement, strain, and stress fields for a given set of constitutive parameters. The inverse problem will compare these predictions with measured full-field data to draw inferences about elastic and creep parameters. The Bayesian approach is crucial because the goal is not only to find best-fit values but also to derive posterior distributions, parameter correlations, and credible intervals. This will help determine if a gradient experiment truly constrains the unknown parameters or if an apparent fit stems from poor identifiability.

The project is supported by both the National Physics Laboratory (NPL) through Dr. Abdo Koko as the industry lead but also the UK nuclear fusion programme through UKAEA (Alex Dr. Dickinson-Lomas). Where appropriate we will provide training through spending time at both NPL and UKAEA with opportunities for the student to take part in NEURONE programme meetings (https://www.ukaea.org/work/neurone/)to help understand and accelerate the thermomechanical processing and development of reduced activation ferritic-martensitic (RAFM) steels, as a future fusion blanket material. At NPL the student will be trained in electro-thermal mechanical testing, synchronised data acquisition, digital image correlation, thermal imaging, multimodal data registration, experiment metadata and ontology design. In addition, there are various undergraduate courses that might be appropriate such as the Nuclear Fusion module or Nuclear Materials modules at Imperial College London that will be available to the student.

Funding Notes

This is a fully-funded project, part of cohort 3 of the EPSRC CDT in Developing National Capabilities in Materials 4.0. The studentship covers home fees, a tax-free stipend of at least £20,780 plus London allowance if applicable, and a research training support grant.

Enquiries

For general enquiries, please contact doctoral-training@royce.ac.uk.

For application-related queries, please contact a.neri14@imperial.ac.uk.

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Prof Mark Wenmann (m.wenmann@imperial.ac.uk).

Application Process

Please note that each partner of the CDT in Materials 4.0 will have its own application process.

The Materials 4.0 CDT is committed to Equality, Diversity and Inclusion. We strongly encourage applications from underrepresented groups.

Application Web Page

https://myimperial.powerappsportals.com/

Click on 'Make a new application', and then type 'Materials 4.0' in the search box.