Developing an intelligent framework for integrating energy-saving devices on coastal ships

A fully funded PhD studentship in the Department of Mechanical Engineering

PhD project description

Background

Coastal ships operate in complex environments that can significantly impact their performance, leading to increased energy consumption and greenhouse gas emissions. While alternative propulsion systems, such as fuel cells and energy storage systems using alternative fuels, show promise for decarbonising certain coastal ship types, their limited energy density and high costs restrict wider adoption. Enhancing the efficiency of existing propulsion systems by integrating energy-saving devices, such as air lubrication and wind-assisted propulsion, within the constraints of current shipboard systems is essential. However, environmental uncertainties in marine settings can affect the performance of these devices and may even lead to increased energy consumption in certain conditions.

Traditional methods for assessing and optimising propulsion efficiency rely on empirical models and historical data but are often limited by their inability to adapt to varying operational and environmental conditions. Recent advancements in data-driven modelling offer a transformative approach by leveraging real-time data that captures a wide range of parameters. Using machine learning and advanced analytics, data-driven models are expected to predict propulsion performance and optimise operations.

This project focuses on developing a novel, adaptive framework for integrating energy-saving devices on coastal ships. By leveraging modern data-driven propulsion models and advanced control systems, this framework will address environmental uncertainties and advance the decarbonisation of coastal shipping.

Aim

This research aims to develop a novel framework for integrating energy-saving devices on coastal ships, focusing on data-driven ship propulsion performance modelling, optimal integration of advanced energy-saving technologies, and intelligent operation of these devices (e.g., wind-assisted propulsion) using machine learning to minimise energy consumption in complex marine environments. The project will employ a multidisciplinary approach and involve close collaboration with the marine industry. The candidate is expected to develop data-driven ship performance models and novel machine learning algorithms to address complex decision-making challenges associated with operating energy-saving technologies under the uncertainties of marine environments, while also accounting for operational requirements and constraints.

The studentship also offers opportunities to engage in teaching assistant activities and collaborate with researchers from the marine research group in the department. As a PhD student at UCL, the candidate will benefit from training for conducting high-impact research. The candidate will be encouraged to publish work in leading journals and present findings at national/international conferences.

Person specification

• Applicants are preferred to have first-class undergraduate and master’s degrees (or equivalent) in Marine Engineering or Electrical Engineering or Computer Science or a related discipline with interest in ships, marine power and propulsion systems, machine learning, simulation/mathematical modelling and experimental evaluation.
• Excellent organisational, interpersonal and communication skills, along with an interest in interdisciplinary research, are essential.
• Excellent skills in computer programming and data analytics are essential.
• Experience in deep learning and deep reinforcement learning is desirable.
• Fluency and clarity in spoken English as well as good written English in accordance with UCL English requirements (TOEFL>92 or IELTS>6.5).

Eligibility

Please note that the available funding supports tuition fees at the Home/UK rate (currently £6,035 per year). Students who are eligible to pay fees at the UK rate are welcome to apply (e.g. UK students or EEA or Swiss nationals who are “settled” or “pre-settled” within the UK in accordance with the EU Settlement Scheme). Please refer to our website for further information about Home tuition fee eligibility.

International students who are eligible to pay tuition fees at the Overseas rate (currently £31,100 per year) are also welcome to apply, however the tuition fees covered by the studentship will be limited to the Home/UK level. International students will be required to find additional funding for the remaining Overseas tuition fees.

Applicants whose first language is not English are required to meet UCL's English language entry requirements.

Please refer to this webpage for full eligibility criteria: Mechanical Engineering MPhil/PhD

How to apply

Eligible applicants should first contact Dr Peng Wu (wu.peng@ucl.ac.uk). Please enclose the following documents:

• A maximum two-page research proposal that addresses the selected research topic, demonstrates an understanding of the background to the area and outlines the questions that the candidate is interested in researching.
• Two-pages curriculum vitae (including contact details of two referees).
• Cover letter (one page) explaining why the candidate is interested in applying for this studentship.

After discussing the project with Dr Wu, eligible applicants should also submit a formal PhD application via the UCL website.

The supervisory team will arrange interviews for short-listed candidates.