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Using Machine Learning to train a Digital Test Pilot for missions in turbulent environments (CDT in Distributed Algorithms)

   EPSRC CDT in Distributed Algorithms

About the Project

This PhD project is part of the CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.

The University of Liverpool’s Centre for Doctoral Training in Distributed Algorithms (CDT) is working in partnership with the STFC Hartree Centre and 20+ external partners from the manufacturing, defence and security sectors to provide a 4-year innovative PhD training programme that will equip up to 60 students with: the essential skills needed to become future leaders in distributed algorithms; the technical and professional networks needed to launch a career in next generation data science and future computing; and the confidence to make a positive difference in society, the economy and beyond.

The safety limits of a helicopter operating to a ship is defined by the aircraft’s characteristics, the pilot’s capabilities, the wind strength and direction, ship deck-motion, and the visual environment. The safe operational limits are established in trials where test pilots fly repeated missions to a ship in as wide a range of environmental conditions as can be realised in the trials period. The outcome is usually an incomplete and restricted operational envelope, achieved at very high financial cost and at significant risk to the crew. Modelling and simulation can be used to reduce cost and risk involved in real-world testing and potentially enable a wider operational envelope than is obtained using ‘traditional’ test and declare techniques; the PhD student will develop new simulation models to support this. 

The project partner is Nova Systems who are a global engineering services and technology solutions company. The company has extensive experience in flight test, certification and acceptance of aircraft and flight simulators.

The primary supervisor is Prof Mark White, Head of the Aerospace Division in the School of Engineering. Mark has 30 years of research experience publishing over 100 conference and journal papers in the field of mechanical, material and aerospace engineering.

Whilst piloted flight simulations in a full-motion simulator can be used to reduce costs by supporting the at-sea trials, the simulated trials are still expensive and are limited by simulator availability. The aim of this project is to develop a mathematical model of a human pilot, a Digital Test Pilot, which can be used to conduct multiple virtual deck landings in order to establish the likely boundaries of the safe operational envelope. The pilot will be ‘trained’ using the wealth of untapped data that is gathered by the test aircraft during the real-world sea trials. The ability to exploit this data could reap large cost-benefits and extract further capability in optimised design, maintenance and usage. The project will examine the suitability of methods for ‘intelligent’ use and fusion of available data to develop the Digital Test Pilot.

Desktop-based predictive simulation tools that use an objectively optimized human pilot modelling technique within an integrated pilot–vehicle–environment are available. A multi-loop pursuit pilot model, using a linearized helicopter flight dynamics model with a spatial air turbulence model, has shown potential for use as a predictive tool for operational clearances. In recent discussions with the UK MoD and Nova Systems, a more advanced modelling method has been proposed to use a non-linear helicopter flight model with an unrestricted turbulent air flow-field which the aircraft can ‘explore’ at will. The intention is to ‘train’ the Digital Test Pilot model using machine learning and data from the aircraft states and pilot control inputs derived from at-sea trails and from simulated flight trials undertaken within the University’s own HELIFLIGHT-R simulator. The Digital Test Pilot model training will involve fusing data from a range of input sources e.g. aircraft state’s, pilot control inputs, shipboard motion, to determine an appropriate response. Nova are prepared to financially support the project, and MoD have indicated they are prepared to provide real-world data; both are keen to provide expertise.

It has also been recognised that the process could be applied to non-military ship/helicopter operations as well as helicopters operating to offshore platforms and those in medical and rescue services. These avenues of further exploitation will also be examined in the project.

Students will be based at the University of Liverpool and will be part of the CDT and Signal Processing  research community - a large, social and creative research group that works together solving tough research problems. Students have two academic supervisors and an industrial partner who provide co-supervision, placements and the opportunity to work on real world challenges. In addition, students attend technical and professional training to gain unparalleled expertise to make a difference now and in the future.

The CDT is committed to providing an inclusive environment in which diverse students can thrive. The CDT particularly encourages applications from women, disabled and Black, Asian and Minority Ethnic candidates, who are currently under-represented in the sector. We can also consider part time PhD students. We also encourage talented individuals from various backgrounds, with either an UG or MSc in a numerate subject and people with ambition and an interest in making a difference. 

This project is due to commence on 1 October 2022.

The studentship is open to UK students who are able to gain a UK security clearance.

For any enquiries please contact Professor Mark White:  

Visit the CDT website for application instructions, FAQs, interview timelines and guidance.

When applying, you must enter the following information:

·        Admission Term: 2022-23

·        Application Type: Research Degree (MPhil/PhD/MD) – Full time

·        Programme of Study: Electrical Engineering and Electronics – Doctor in Philosophy (PhD)

The remainder of the guidance is found in the CDT application instructions on our website.

Visit the CDT website for further funding and eligibility information.

Funding Notes

This project is a funded Studentship for 4 years in total and will provide UK tuition fees and maintenance at the UKRI Doctoral Stipend rate (£16,062 per annum, 2022/23 rate): View Website

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