New PhD studentship in modelling digital twins

Internet of Things technologies can improve the operational efficiency of buildings, creating “digital twins”. This project assesses the whole lifecycle cost of data capture, analysis and storage to deliver sustainable digital twins for decarbonisation and demand management. 

Data driven approach to modelling the resource footprints of live data streams in digital twins

Details

Supervisors:

Duncan Wilson, CASA
Cliff Elwell, UCL Energy Institute

Funding

The studentship will cover UK course fees and an enhanced tax-free stipend of approx. £18,000 per year for 4 years along with a substantial budget for research, travel, and centre activities.

Start date

September 2021

Industry sponsor

Arup is an independent firm of designers, planners, engineers, architects, consultants and technical specialists, working across every aspect of today’s built environment. The Building Performance and Systems team consists of Controls, Mechanical and Electrical, Building Performance and FM specialists who offer total solutions for new design and post occupancy services.

About the studentship

Intelligent Buildings have been in operation for decades however it is only in recent years that data from these systems is starting to surface beyond building management systems. The ubiquity of the Internet of Things is changing how we sense and interact with our environment and has led to the emergence of the Digital Twin - a virtual model of a real-life operational entity. Driven by information, these models allow us to monitor operations to head off issues before they arise, or optimise operations based on ever changing human-environment interaction. This research will explore the hidden value of “data as a material” in two new campus buildings to improve efficiency and demand flexibility.  

At a global level, urgent action is required to decrease the carbon intensity of our buildings and to support a transition to a net zero carbon operation. As the potential volume of data grows exponentially it is essential that we strategically look at the whole life cost of this information to ensure the digital footprint cost is optimised compared to the benefit accrued.

The successful candidate will be trained within our vibrant ERBE CDT community and the Connected Environments Lab in the Future Living Institute at UCL East.

Studentship aims

This PhD aims to develop a new method to quantify the social, economic and environmental benefits of capturing, analysing and storing information generated in a digital twin based on analysis of demand management and post occupancy evaluation. Analysis will be conducted on the digital footprint of two new campus buildings at UCL East (opening 2022 and 2023) which have the capacity to generate about 30 million data points per day. The research will explore the buildings in the context of operational factors such as improving facilities and the role of a Living Lab environment supporting research and teaching.

Person specification

This is an exciting and challenging project, suited to a candidate with a physical science or engineering background and interested in an applied PhD in the area of the Internet of Things, Digital Twins and Living Labs. An interest in collecting, visualising and analysing spatio-temporal data is beneficial. Experience or qualifications in a subject associated with the built environment are welcome, but not required – training and support will be provided to the successful candidate.

A minimum of an upper second-class UK Bachelor's degree and a Master's degree, or an overseas qualification of an equivalent standard, in a relevant subject, is essential. Exceptionally: where applicants have other suitable research or professional experience, they may be admitted without a Master's degree; or where applicants have a lower second-class UK Honours Bachelor's degree (2:2) (or equivalent) they must possess a relevant Master's degree to be admitted.

Applicants must also meet the minimum language requirements of UCL.

Application procedure

Eligibility and how to apply

Please submit a pre-application by email to the UCL ERBE Centre Manager (bseer.erbecdt@ucl.ac.uk) with Subject Reference: 4 year PhD studentship in Data driven approach to modelling the resource footprints of live data streams in digital twins.

The application should include the following:

• A covering letter clearly stating why you wish to apply for the project outlining how your interests and experience relate to it, and confirm your understanding of Changes to EU and International Eligibility for UKRI funded studentships
• CV
• Complete the CDT recruitment EPSRC fees eligibility and EDI questionnaire via the corresponding Microsoft Forms linked.

Deadline for applications: Sunday 21 March 2021 23:59PM (UK Time)

Interview process

Only shortlisted applicants will be invited for an interview.

For the interview shortlisted candidates will be required to show proof of their degree certificate(s) and transcript(s) of degree(s), and proof of their fees eligibility

The interview panel will consist of the project’s academic supervisor at UCL, a representative of the industrial sponsor and a representative of the ERBE CDT Academic management.

The interview will include a short presentation from the candidate on their ideas of how to approach this PhD project.

Following the interview, the successful candidate will be invited to make a formal application to the UCL Research Degree programme for ERBE CDT. . 

For further details about the admission process, please contact: bseer.erbecdt@ucl.ac.uk

For any further details regarding the project, contact

Dr Cliff Elwell, clifford.elwell@ucl.ac.uk  or Prof Duncan Wilson, d.j.wilson@ucl.ac.uk 

You will be undertaking this project in UCL at the main (Bloomsbury) campus as part of the new EPSRC-SFI Centre for Doctoral Training in Energy Resilience and the Built Environment (ERBE CDT). This is a collaboration between UCL, Loughborough University and Marine and Renewable Energy Ireland (MaREI). For more information please visit the ERBE website.