The regulation of carbon and pollutant emissions from energy-generation systems has been increasingly tightened in recent years. To realise more environmentally friendly energy generation, it is necessary to monitor reactive-flow characteristics, such as temperature, multi-species concentrations, pressure and velocity. These data can be used as feedbacks to predict and actively adjust the operating conditions of the energy-generation systems.
As a sensitive, fast-response and cost-effective opto-electronic sensing technique, chemical species tomography (CST) is one of the most promising technology to apply on flow diagnosis in practical operating conditions. The amount of CST raw data is enormously large, requiring the data to be processed on the system-on-a-chip in real time for rapid imaging of the dynamic process of the reactive flows.
The objectives of this PhD projects are:
- Develop deep learning aided algorithms for CST towards high spatiotemporal imaging.
- Design system-on-a-chip acceleration of the CST imaging techniques.
- Experimental validation of the sensor on reactive flow imaging.
During the project, the PhD candidate will be trained to develop novel deep-learning algorithms to achieve high-fidelity CST image reconstruction and its hardware acceleration. In collaboration with academic and industrial partners, the designed sensor and system will be finally employed for reactive flow imaging. The candidate should also be confident with trouble-shooting and collaborating with academic and industrial partners in the experiment tests.
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 laser-based technology.
Technical Queries directed to Dr Chang Liu on C.Liu@ed.ac.uk
Further Information:
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
Closing Date:
Principal Supervisor:
Assistant Supervisor:
Eligibility:
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. Further information on English language requirements for EU/Overseas applicants.
Funding:
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
