Understanding Air Lubrication Systems through Machine Learning

Supervisory Team:   Adam Sobey (CMEE/ME), Dominic Hudson (CMEE/ME)

Project description

Despite being an efficient form of transport, shipping causes between 2-3% of world emissions. To reduce these emissions Silverstream Technologies have developed an Air Lubrication System shown to reduce fuel consumption by 5-8%. The system releases a carpet of bubbles along a ship’s hull, providing a separation between the hull and the water, reducing the frictional drag. The quality of the carpet determines the potential fuel savings and this is determined by the power used to pump the air into the water, creating the carpet, the operating modes of the ship and the environmental conditions it is sailing in. This forms a complex optimisation problem that is difficult/impossible to model and where it’s difficult to replicate realistic operating conditions through experiments.

To help understand the performance of the system, this PhD will focus on using Machine Learning to understand the sensitivity of the Air Lubrication System to these different input variables. It will take data from ship’s operating with the Silverstream Air Lubrication System and correlate the input power, operating mode and environmental conditions with either high or low performance, determining where improvements can be made. This information will be passed on to experts in fluid dynamics to help inform the modelling and tests that will benefit our understanding of the system. In a second stage the knowledge will then be used to determine how to reduce the required data for accurate predictions using a fusion of data and to automate the learning in the system. This will reduce the expert knowledge required to update the system during operation, allowing an efficient update of a fleet of models.

Supervision will be provided by Adam Sobey and Dominic Hudson in the Maritime Engineering group at the University of Southampton. In addition you will work closely with the Marine and Maritime group, in the Data-Centric Engineering Programme in The Alan Turing Institute, the UK’s national AI institute and the Applied Research Group at Silverstream Technologies.

We are looking for a driven candidate with expertise in, or interest in learning about: Maritime Engineering, and Machine Learning. We’re more interested in candidates that show an interest for this area of research and that can show an aptitude for research, over current knowledge in either of these areas. We’d particularly like to see candidates interested in making real world changes to reduce maritime emissions through development of world-leading fundamental approaches in maritime Machine Learning.  

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: 25 June 2023 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: For UK students, Tuition Fees and a stipend of £17,668 tax-free p.a for 3 years. this scheme will NOT support non-UK/EU students.

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 Adam Sobey

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk