PINN for prognosis of zero-carbon reactive-flow instability via laser imaging

This PhD project aims to develop Physics-Informed Neural Networks (PINNs) for the prognosis and understanding of instabilities in zero-carbon reactive flows, with a particular focus on hydrogen and/or ammonia combustion. The project will integrate advanced machine learning techniques with laser imaging to create predictive, data-efficient models capable of capturing the complex, multi-scale dynamics of reacting flows. This project is under the Royal Society International Exchange Programme in collaboration with Shanghai Jiao Tong University.

Imaging methods such as absorption or emission tomography provide rich, high-resolution spatio-temporal data on flow characteristics. These measurements will be combined with governing physical laws embedded within PINN frameworks to infer hidden flow states, identify instability precursors, and forecast the onset and evolution of reactive-flow instabilities.

The research will involve developing tailored PINN architectures for reactive flows, designing strategies to assimilate experimental laser-imaging data, and validating models against laboratory-scale combustion experiments. Emphasis will be placed on uncertainty quantification, robustness to sparse and noisy data, and real-time or near-real-time predictive capability.

The project is highly interdisciplinary, spanning laser imaging, applied mathematics, and machine learning, and is suited to candidates with strong interests in both imaging and data-driven methods.

Primary objectives:

1. Develop PINN frameworks tailored to zero-carbon reactive-flow systems.
2. Integrate imaging diagnostics with PINNs for model training.
3. Identify and characterise precursors to instabilities from spatio-temporal flow fields.
4. Create predictive models for early-warning prognosis of instability.
5. Validate PINN predictions against controlled laboratory combustion experiments.
6. Investigate uncertainty quantification and robustness.

Required skills: 

1. Matlab/Python/Tensor-flow coding (mandatory)

2. Machine learning algorithms and coding (mandatory)

3. Tomography and inverse problems

4. Flow-field mechanics and dynamics

5. Optical/electronic experimental skills.

Please note that this advert will close as soon as a suitable candidate is found.

Applicants should have an Undergraduate degree in Electronic and Computer Science or Mechanical Engineering, possibly supported by an MSc Degree

Please also refer to the University’s English language requirements.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere

Further information and other funding options.