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Machine learning aided engine exhaust tomographic imaging

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  • Full or part time
    Dr C Liu
  • Application Deadline
    No more applications being accepted
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Aero gas turbine engines are meeting ever-increasingly stringent emission requirements on the concentrations of toxic compounds, minimising the adverse environmental effects of civil aviation activities. Since accessing to the combustion chamber on a running gas turbine is severely limited, combustion diagnosis on the gas turbine exhaust plumes, which provides direct information of engine emission, is highly demanded for characterising and understanding the combustion system performance.

Tomographic Absorption Spectroscopy (TAS), using multi-path measurement in the form of Tunable Diode Laser Absorption Spectroscopy (TDLAS), has afforded for robust and high-speed imaging of the gas concentration within the combustion platforms. The large amount of tomographic data, from both the spectral and spatial aspects, poses a significant challenge for rapid image reconstruction. Therefore, machine learning aided algorithms are good candidates to effectively process the TAS data and accelerate the image reconstruction of engine exhaust.

The objectives of this PhD projects are:
1. Develop machine learning aided TAS algorithms for rapid image reconstruction.
2. Validation of the developed algorithm on the simulated flame phantom.
3. Diagnosis of the engine thrust using the developed algorithms.

During the project, the PhD candidate will be trained to develop machine learning and deep learning image reconstruction algorithms for engine diagnosis. Most critical process in the algorithm development is the determination of the parameters of the learning process. The candidate should also be fully motivated and confident with trouble shooting in the experiments.

In addition, the successful candidate will have the opportunity to work closely with industrial and academic partners, to present innovative results in international conferences, to publish high-impact journal papers, and, eventually, to deliver advanced machine learning aided algorithms to engine diagnosis.

Funding Notes

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree.

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

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

Research output data provided by the Research Excellence Framework (REF)

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