This is an exciting opportunity to studying a PhD degree in the area of The Utilization of Digital Twins in Condition Monitoring for Aerospace Systems. The research would focus on developing Digital Twins for optimising maintenance and condition monitoring for the next generation of unmanned and more-electric aircraft (UAV/MEA) configurations. Read more Read less
Recent improvements in software and simulation timeframes allow verified, accurate digital twins of an aerospace subsystem to be developed. This affords the aircraft designer and operator many advantages. In the design process, various scenarios may be played out; from helping to undertake a thorough safety review, develop system redundancy to playing out unforeseen events. Thus, allowing the designer to optimise the system, reduce testing and certification costs.
From the operations side, it allows advanced condition monitoring and maintenance strategies to be developed. By examining how different faults may affect the system performance, strategies to aid faster diagnosis as well as training data may be produced. This training data can also be combined with artificial intelligence and used to classify faults based on the patterns of behaviour. Optimisation in sensing may also enable an overall reduction in sensors currently employed. This will enable a reduction in turn-around and maintenance times, overall leading to improved safety and more cost-effective aircraft.
This project will involve the development of a digital twin for the landing gear system in a commercial aircraft, encompassing structural, hydraulic and actuation components accurately modelled utilising state of the art simulation methodologies. This work will primarily focus on using the digital twin to demonstrate how an improved maintenance strategy may be developed. The work is based upon the landing gear of a commercial aircraft currently already in service.
By analysing the operation of the system under a variety of scenarios, data is generated showing how the system performs under various loadings and environmental stresses. Fault scenarios such as pressure losses in the hydraulics and actuator failure will also be demonstrated.
Cranfield is a unique learning environment with world-class programmes, unrivalled facilities and close links with business, industry and governments, all combining to attract the best students and teaching staff from around the world. In 2014, 81% of research at Cranfield was rated as world-leading or internationally excellent in the Research Excellence Framework (REF).
The Integrated Vehicle Health Management (IVHM) Centre is in its 12th year of operation. Founded by Boeing and a number of aerospace partners (BAE Systems, Rolls-Royce, Meggitt and Thales) in 2008, it has grown to perform work in sectors such as transport, aerospace, and manufacturing. The Centre integrates a multidisciplinary research effort to develop cost-effective component and system health management technologies capable of supporting ground and on-board applications of high-value, high-complexity systems.
IVHM Centre is a member of Digital Aviation Research and Technology Centre (DARTeC), which focuses its researchon aircraft maintenance, connected systems, unmanned traffic management, seamless journey, distributed airport/airspace management, and conscious aircraft. Research England, Thales, Saab, Aveillant IVHM Centre and Boxarr are some of the prominent members of DARTec. IVHM Centre also works in close collaboration with Aerospace Integration Research Centre (AIRC), founded in partnership with Airbus and Rolls-Royce. The potential PhD candidate will have access to the facilities held by AIRC and DARTeC in addition to having interactive sessions with experts at AIRC and DARTeC.
The successful culmination of this project will enable the simulation of faults that may occur during the life of the system. Condition monitoring and Prognostic Health Management (PHM) strategies will also be proposed. To illustrate this, training data will be used to learn the health of the system under a multitude of scenarios. Subsequently allowing for AI based algorithms to be used in the monitoring and classification of the fault, demonstrating how the digital twin may be used in the automation of the maintenance programme/reduction in fault diagnosis time and cost.
The project will provide active collaboration and exchange of ideas and knowledge with key stakeholders within different centres of the Cranfield University and industrial partners in the aircraft industry. The combined development of modelling using Digital Twins and Machine Learning based applications within IVHM centre and across other research centres would be helpful for the potential researcher in acquiring essential knowledge and building skills (e.g., Mathematical Modelling and AI algorithms’ formulation) required for this specific research project.
The IVHM Centre encourages and supports ample opportunities for disseminating individual research through reputed journals and presenting papers in high profile and well-known IEEE conferences within the UK and across the globe. More significantly, you will have the opportunity to present your research work during quarterly technical reviews to the wider research community from within the university and the industrial partners. It also provides a networking platform for promising researchers to lay the foundations of their professional relationship with key representatives from various companies.
Upon successful completion of the project, the potential candidate will be able to carry out research activities independently and more vigorously. This research will be formative for the potential candidate in building his/her analytical logic and mathematical modelling skills. The understanding of the essence and application of futuristic digital twins for aircraft systems would broaden the employability scope appreciably, including automotive, energy and manufacturing sectors.
At a glance
- Application deadline30 Apr 2021
- Award type(s)PhD
- Start dateAs soon as possible
- Duration of award3 years
- EligibilityUK, EU, Rest of World
- Reference numberSATM181
Entry requirementsGraduate and post-graduate students with a degree in engineering, data science, or any other related physical sciences subject, and Researchers and Engineers with a background/interest in Systems Engineering and System Simulation. PhD candidates must have familiarity with AI and Machine Learning methods. Above all, research aspirants with an innovative approach, high motivation and willingness to learn are welcomed.
FundingThis is a self-funded opportunity.
About the sponsorThis is a self-funded opportunity.
Cranfield Doctoral Network
Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.
How to apply
For further information please contact:
Name: Suresh Perinpanayagam
T: (0) 1234 750111 Ext: 2377
If you are eligible to apply for this studentship, please complete the online application form.