Scalable digital twin for improved safety and availability of complex engineering assets PhD

Cranfield University is offering an exciting opportunity for a fully-funded PhD studentship in the field of digital twin technology for complex engineering assets. This project aims to develop a scalable digital twin that can improve the safety and availability of complex engineering assets and the complex systems in which they are integrated.

The successful candidate will receive a competitive stipend of £19,000 per annum for three years, along with tuition fees covered by EPSRC. Additionally, the candidate will receive a stipend top-up from an industry sponsor in the aerospace and defence sector. The sponsor brings extensive domain expertise and real-world applications, enabling the research to have a significant impact on industrial safety and productivity.

Digital twin technology has emerged as a transformative approach in the context of complex engineering systems, enabling the creation of virtual replicas that mirror and control the behaviour and performance of physical assets. Digital twins hold immense relevance today as industries strive to enhance safety, efficiency, and sustainability across various sectors. By bridging the physical and digital worlds, digital twins can facilitate data-driven decision-making, predictive maintenance strategies, and optimised asset management. The ability to simulate, monitor, and optimise critical assets through their virtual counterparts contributes to sustainable development goals, extending asset lifespan, reducing downtime, and minimising environmental impact. 

The primary focus of this PhD project is to develop a scalable digital twin architecture for complex engineering assets. The aim is to enable accurate monitoring, fault diagnosis, and self-optimisation, ultimately improving the safety and availability of these critical assets. The research will investigate methods to facilitate bi-directional data flow between the physical asset and its digital counterpart, enabling real-time feedback and mitigation of faults. The objectives of the PhD include: 

• Develop a scalable digital twin architecture for complex engineering assets, considering data capturing, transmission and storage, development of simulation model as exact replica of the actuator, analysis of the simulation results and self-optimisation to mitigate faults.
• Design a data architecture that enables seamless information exchange between different systems and functional units, providing a template for the proposed digital twin’s development.
• Develop and validate the proposed digital twin of complex engineering assets, enabling accurate monitoring of their operation, faults’ diagnosis and self-optimisation.
• Investigate methods to enable bi-directional data flow between the physical asset and its digital counterpart. This feedback could be enabled as recommendations where actions will be taken by humans and/or programmatically by controlling the asset in a fully automated way.
• Validate the scalability and effectiveness of the digital twin in real-world scenarios, assessing its impact on improving safety and availability of high-value assets.

Cranfield University is wholly postgraduate, and is famous for its applied research in close collaboration with Industry. At Cranfield, the candidate will be based within the Manufacturing theme at the Centre for Digital Engineering and Manufacturing (CDEM). The Centre hosts cutting-edge simulation and visualisation facilities. The student will have access to high-end computers and digital technologies in the Centre for ontology-based and knowledge-based systems development, Digital twin development, advanced dynamic modelling and simulations, AI, VR, AR developments. The candidate works on his/her research individually and collaborates with other researchers in the field at the Centre. This project is sponsored by a leading company in the aerospace and defence sector, bringing extensive domain expertise and real-world applications 

The successful development of a scalable digital twin for complex engineering assets holds the potential to significantly enhance workplace safety and industrial productivity across various sectors. By simulating and optimising the behaviour of these critical assets, the risk of failures and accidents can be reduced, leading to improved safety standards. Additionally, the prompt identification and mitigation of potential faults will contribute to increased availability and efficiency of industrial processes, resulting in cost savings and sustainability benefits. 

This funded PhD program offers a range of compelling advantages. It centres on applied research that not only advances your academic journey but also contributes to solving real-world challenges. The programme offers diverse training experiences, both internally and externally, enriching your skill set and expanding your knowledge base. Pursuing this PhD at Cranfield University, renowned for its academic excellence, holds the potential to unlock promising career pathways. Moreover, the opportunity to interact with experts from academia and industry not only fosters extensive networking but also offers exposure to cutting-edge insights. This collaborative environment nurtures personal growth and equips you with valuable connections within your field. Additionally, the project provides opportunities for industry placements, conferences, and external training, further enhancing the student’s professional development and employability. 

The student will gain from the experience in numerous ways, whether it be transferable skills in the technical areas of digital twin development, optimisation, and machine learning, or soft skills including presentation skills, project management, and communication skills. There are also numerous employability opportunities that the PhD will offer, whether it be in industry or in academia. The hands-on experience with cutting-edge digital twin technology, combined with the interdisciplinary nature of the project, will equip the student with a highly sought-after skill set in the rapidly evolving field of Industry 4.0 and digital transformation.

Entry requirements

We are inviting applicants with a First or upper Second Class degree equivalent qualification in an engineering background, or an alternative quantitative focused discipline.

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

To apply for this PhD opportunity please complete the application form using the button below. 

Apply now

For further information please contact Christina Latsou

Email: Christina.latsou@cranfield.ac.uk