Fully funded PhD studentship in modelling for energy-efficient and climate-responsive buildings

About the project

Project title: A high-fidelity coupled urban-building and microclimate modelling approach for energy-efficient and climate-responsive buildings
Project supervisors: Dr Dimitrios Rovas, Dr Rui Tang
Project ID: 2228bd1208 (You will need this ID for your application)

Climate change instils urgency in the need to decarbonise buildings and empower them to be more resilient towards extreme weather to protect human health and comfort. Urban-building modelling tools can help understand the impact of energy-efficient and climate-responsive building technologies across a large part of building stock. However, such tools often rely on static, pre-generated typical-year weather files and do not consider realistic weather conditions and the local microclimate. This can lead to modelling discrepancies to the ground truth and misguide the subsequent decision-making process. This problem is more intense in megacity centres where a highly dense building morphology and complicated outdoor environment could significantly influence model prediction quality.

This project aims to develop a novel modelling approach, coupling urban building energy models with microclimate models. The weather variables of an urban-building model at each timestep will be determined by an urban canopy microclimate model formulated based on the identified urban-building morphology. The 3D morphology will be modelled using open-source LiDAR data, incorporating satellite images analysed by deep learning techniques. A hybrid physics-machine learning modelling approach developed previously by the supervisory team will be used to establish the urban-building model and compute the dynamic response of individual buildings. We will use a university campus in central London as a case study to demonstrate the performance of the proposed modelling approach and analyse its ability to support practical scenarios under the current circumstance and future climate change for reducing energy consumption and mitigating overheating risks of buildings.

This project will benefit policymakers, local authorities, urban planners and building owners to make informed decisions facing climate change and urbanisation. The student will deepen their understanding of developing sustainable, low-carbon and resilient buildings and grasp skills in modelling energy systems and leveraging machine learning techniques.

About the Supervisory Team

Proposed supervisory team is Dr Dimitrios Rovas as Primary Supervisor and Dr Rui Tang as Secondary Supervisor.

Dr Dimitrios Rovas is an Associate Professor of Building Simulation and Optimisation at UCL Institute for Environmental Design and Engineering. He received his PhD in Mechanical Engineering from MIT. His work contributed to developing and validating energy modelling tools across scales in the context of whole-building energy modelling approaches, with applications, e.g. to retrofitting decision support. He has extensively worked on multi-model coupling and co-simulation approaches. He has led more than 16 projects in domains related to this project, with his work resulting in more than 100 academic publications. He has supervised more than 20 PhD students and postdocs.

Dr Rui Tang is Lecturer in Smart Buildings and Digital Engineering at UCL Institute for Environmental Design and Engineering and leads the MSc Module of Machine Learning in Smart Buildings. Before joining UCL, he was a Senior Research Associate at Berkeley. His expertise includes energy system modelling, machine learning applications, and energy-efficient and climate-responsive buildings. He has worked on the dynamic modelling of urban microclimate, especially in the compact city form, to analyse its impacts on the development of sustainable buildings. To date, he has published over 40 peer-reviewed articles and has supervised nearly 10 PhD students and graduated 8 MSc students.

Key information

Funder: UCL ESPRC DTP studentship
Value: Fees, Stipend (at least £20,668 per year), Research Training Support Grant
Duration: Up to 4 years (thesis to be submitted within funded period)
Eligible Fee Status: Home, International (EPSRC caps the total number of funded International fee status students across UCL for this award at 30%)
Study Mode: Full or Part time (at least 50% FTE) [Note: Part time is not available to International students]
Primary Selection Criteria: Academic merit
Project ID: 2228bd1208 (You will need this ID for your application)
Application Deadline: 12:00 on 26 January 2023

How to apply

his PhD Studentship topic is one of 19 proposed by The Bartlett School of Environment, Energy and Resources to a competition for approximately 60 studentships that will be awarded across UCL as part of the UCL EPSRC DTP. Prospective students are welcome to apply for up to 5 potential studentships - see the full list of projects from our department and the UCL project database for a comprehensive list across the university. The 60 successful proposals will be chosen following applicant interviews.

Before applying, all applicants must read the full eligibility criteria and application guidance on the UCL EPSRC DTP website. There is a 3-part application process, with a deadline of the 26 January 2023 to complete the third part of the application.