SMDE5241021
October, February and April
Applications accepted all year around
The PhD will be based in the School of Mechanical and Design Engineering and will be supervised by Dr Ya Huang.
The work on this project could involve:
- Interpreting visual cues of oncoming wave features with respect to human biomechanical (for traumatic injuries) and physio-psychological (for discomfort and motion sickness) responses to mechanical shocks on fast crafts.
- Developing a memory-based reasoning method for close-range path planning of surface vessels for seakeeping criteria.
- Evaluating nonlinear model predictive controllers (NMPC) and sliding mode controls (SMC) based on inputs of visual wave feature inputs, and output of hydrodynamic and dynamic loads transmitted to the vessel structure, and biomechanical loads measured on lifeboat crew.
- Fuzzy logic and sliding mode control Correlate hydrodynamic loads from simulation with biomechanic loads derived from the inverse dynamics computation of the musculoskeletal pipeline using six typical wave encounters extracted from collected data.
The project intents to develop a human response to motion inspired intelligent controller to improve the seakeeping performance and safety of human occupants on fast surface vessels. Seakeeping, concerning the control of vessel motion when subjected to waves and the resulting effects on humans, systems, and mission capacity, remains one of the biggest challenges in maritime safety. In heavy seas each ‘wave slam’ induces high-acceleration mechanical shocks. Storms are expected to become more common and severe due to climate change. To preserve the maritime industry, offshore wind farms and rescue services, the marine systems will need to adapt.
To make control decisions that mitigates detrimental effects on seakeeping requires interpretation, derivation and validation of the multiple-input-multiple-output system of oncoming wave features, hydrodynamic loading on the vessel, dynamic loading on machines and biomechanical loading on occupants.
The School of Mechanical and Design Engineering has collaborated with the RNLI engineering team to establish the fundamental loading patterns of typical slam encounters on its fleet of lifeboats. Laboratory based dynamic sitting experiments conducted by experienced lifeboat coxswains has provided insights of the main biomechanical loads. A set of primary visual cues for wave encounters are derived from a questionnaire study with a group of experienced RNLI coxswains. The project will start to design path planning based on interpreted wave visual cues using rule-based systems.
The memory-based reasoning approach will resolve overlaps of the different categories of experts. The new project will significantly impact on the human factors, future transport system designs, and particularly the marine industry. The project is aligned with the University’s vision to build global and national partnership through the boundary-breaking themes of future transportation and intelligent systems. The student is expected to collaborate with project partners from areas of mechanical engineering, computing, intelligent systems, biomechanics and industrial partners in the maritime industry. Furthermore, the student is expected to attend multiple events such as conferences, project meetings, and workshops.
Fees and funding
Visit the research subject area page for fees and funding information for this project.
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK (UK and EU students only).
Bench fees
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
Entry requirements
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
Applicants should hold an undergraduate Masters first class degree or MSc distinction (or non-UK equivalent) in Engineering, Mathematics, Physics or a similar discipline. Experiences in programming, intelligent systems, numeric modelling and signal processing are desirable.
When you are ready to apply, please follow the 'Apply now' link on the Mechanical and Design Engineering PhD subject area page and select the link for the relevant intake.