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Visualizing architectural inter-relationships and uncertainties between constituent systems within a systems of systems


Project Description

Application details:
Reference number: RKWS2019
Start date of studentship: 01 January 2020
Closing date of advert: 01 Nov 2019

Supervisors:
Primary supervisor: Prof Roy S. Kalawsky
Secondary supervisor: Dr Melanie King

This studentship is a terrific opportunity to research exciting new aerospace digitalisation techniques that will support Industry-4.0 concepts, including digital-twin and next generation interactive visualization. This will involve developing new systems of systems engineering techniques with interactive visual analytics techniques to aid systems trade-off and optimisation of performance objectives for future aircraft design and manufacture. The research will involve working with world-leading academics and industry experts. On successful completion of the PhD there are excellent career prospects.

Loughborough University

Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career.
Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/

Full Project Detail:

This PhD is concerned with undertaking novel research into new digital techniques that will help designers understand the complex relationships that exist within the context of a systems of systems (SoS) where a product relies on many interconnected systems. Model-based systems engineering (MBSE) methodologies will be researched by exploring new interactive visualization techniques for parameter trade-off/optimisation for a complex product. An important aspect of the research is development of novel visualization techniques to deal with uncertainties in key design parameters and how these are visualised. Recent advances in artificial intelligence/machine learning may be considered as an enabling technology.

Proof of concept demonstrations will be used to focus the research by showing how visualization of parameter dependencies/uncertainties can be used for improved traceability of the evolving product architecture from early digital-twins to its physical twin.

Key themes

• Research and develop new MBSE methodologies that represent parameter uncertainty.
• Develop novel visualization techniques that highlight critical inter-parameter dependencies.
• Create the visualization framework that can be applied to MBSE and digital-twin.
• Creation of visualization tool that allows interactive visualization of parameter trade-off.
• Artificial intelligence/machine learning.
• To investigate how to determine validity of the techniques at different levels of granularity – from digital-twin to physical product.
• Application of technique to negotiated Airbus USE case to confirm applicability.

Find out more: http://www.lboro.ac.uk/research/avrrc/

Entry requirements:

Applicants should have, or expect to achieve, at least a 2:1 Honours or a relevant Master’s degree (or equivalent) in any of the following: computer science, data science, systems engineering, human-computer interaction, software engineering, or a related engineering subject

How to apply:

Interested candidates are welcome to contact Professor Kalawsky to find out more. However, all applications should be made online at http://www.lboro.ac.uk/study/postgraduate/apply/ Under programme name, select ‘Mechanical, Electrical and Manufacturing Engineering’.

Please quote reference number: RKWS2019

Funding Notes

This PhD project is sponsored through a highly prestigious EPSRC I-Case award with an enhanced annual stipend of £18k/year for 4 years, plus a generous annual allowance to cover attendance at international conferences and an industrial placement with Airbus.
In order to qualify for a full award, all applicants must meet the EPSRC eligibility criteria including the minimum UK residency requirement View Website

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