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  Dynamic modelling to generate improvement recommendations for future road systems PhD

   School of Aerospace, Transport and Manufacturing (SATM)

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  Prof John Ahmet Erkoyuncu, Dr Pavan Addepalli  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

UK road infrastructure will need to update and adapt in future years to adjust for new vehicle types (e.g. electric, hydrogen, electric overhead), travel behaviour and differing vehicle volumes. However, to achieve UK zero carbon commitments, this will have to be achieved with little to no new major construction.

Dynamic modelling uses digital representations of a real system, which can include physical, social and process characteristics, to reach an optimisation of complex engineering assets and processes. This project will exploit data on road systems, including physical infrastructure geometry, road user routing and vehicle type scenarios, to create a dynamic model for future planning – identifying infrastructure improvement recommendations and analysing “what if” scenarios.

Optimisation is achievable through the interaction between the physical system, including the physical infrastructure, road users and control systems, and the high-fidelity digital twin model, which assesses the system throughout the continuous evolution over time. In order to achieve this aim, two contributions to knowledge will be achieved during this PhD: (1) consolidation of a data management methodology for road system dynamic models using the ontology approach; and (2) dynamic modelling platform for future insights.

A significant limitation of complex engineering systems is the interoperability of tools for stakeholders. While this project aims to extract insight from the dynamic model for future development of the system, equally other stakeholders may be using the same data to create “Digital Twins” (i.e. present-day dynamic models) of the current system for monitoring, active day-to-day management and maintenance of the system. This project aims to develop a dynamic model through the ontology approach to enhance the flexibility of those systems and establish common data requirements for interoperability. The ontology is a collection of terms, relational expressions, and definitions, which allows a high-fidelity description of the system of interest including its operation.

This PhD will aim to develop a dynamic analysis platform for data management, where the boundaries of data are removed and communication between different sources and users of data can be enabled in an adaptive manner. The research will involve creating a novel mechanism to access data, to structure it, to federate it, and to share it with relevant stakeholders in an efficient and effective manner using ontologies linked to dynamic models.

The objectives to the PhD include: 

  • Establish an optimisation framework to trade-off between data requirements, data accessibility and availability, data connectivity and value generation. 
  • Develop an ontology architecture that is used to provide a template for information exchange between different systems/functional units. 
  • Investigate a blockchain module to enable secured data flow within complex systems. 
  • Build the data backbone within dynamic models with autonomous and adaptive data feed for alternative stakeholder requirements.

PhD timeline

The PhD can start at any point across the year. The PhD will be conducted in three phases: 

Phase 1: Capture current practice and develop conceptual solution.

  • Deliverable 1: Literature review, and conceptualised data configuration platform.

Phase 2: Solution development.

  • Deliverable 2: Ontology and blockchain enabled software platform for data management in dynamic models.

Phase 3: Validation, verification and dissemination.

  • Deliverable 3: Finalised software solution and handover with training of relevant stakeholders.

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 for simulating the complex nature of maintenance. There will be relevant visits to Costain (as the Industrial Sponsor of the project) in particular but also various other organisations throughout the PhD to develop and demonstrate the research. This PhD is offering a fully funded PhD thanks to the EPSRC ICASE scheme, and Costain.

There are numerous benefits proposed through this PhD: 

  • Establish interoperability of software systems by establishing flexible data connectivity.
  • Enhanced confidence in simulations and associated decision-making capability.
  • Reduced costs with improved understanding of resources and mechanisms for value delivery.

There are numerous unique selling points for this PhD, 1) fully funded PhD with extensive funding available for travel not only in the UK but also internationally; we also have an exciting consumables budget to conduct the research, 2) applied research, which brings together Costain and Cranfield University to address significant and current challenges around digital twins, and 3) we have allocated funding to enable training through internal and external courses, which will substantially enhance the PhD experience,, and 4) Both Cranfield and Costain are leading organisations in their fields, and the PhD will open up numerous career opportunities.

The student will gain from the experience in numerous ways, whether it be transferable skills in the technical area of optimisation, 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.

How to apply

If you are eligible to apply for this studentship, please complete the online application form.

Business & Management (5) Computer Science (8) Engineering (12) Mathematics (25)

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 About the Project