Epsrc Icase

By 2035, UK's building stock is expected to deliver 78% reduction in total carbon emissions from 2019 levels. Implementation of effective retrofits that yield both embodied and operational energy savings is thus critical. Glazing systems constitute a key element of a building as they are essential for a visual, safe, and thermally comfortable indoor environment. At the same time, they are also responsible for significant heat gains/losses. Indeed, there have been wide-ranging advancements in highly efficient and responsive glazing systems that harness new materials and IoT. The energy saving potential of these advancements has been evidenced across new-build but is yet to be leveraged in building retrofits - partly because design solutions must conform to additional constraints of an existing structure, but also because lack of their integration and harmonisation with the wider energy and environmental management of buildings.

The principal objective of this project is to optimize responsive glazing systems for retrofitting buildings. The optimization will consider glazing systems as a connected intelligent component of a wider and integrated retrofit strategy, and optimize for cost, operational energy savings, embodied carbon, and human comfort. A second objective is to investigate the concept of 'intelligence through glass' and 'transparent intelligence' with respect to enabling technologies (eg. electronic interaction and sensing capability in conjunction with Artificial Intelligence) in the context of energy retrofitting.

Project Tasks

A. Explore sensing and AI technologies that connect the glazing system to the wider building systems and provide additional functionality to the glass (eg. heating/cooling and ventilation). Work with industry partner (NSG) and co-design configurations for experimental testing.

B. Develop a suitable simulation-based optimization methodology for optimizing intelligent glazing systems at different scales. Test and demonstrate methodology in terms of computational tractability, reproducibility, and usability. Investigate and implement suitable methods to quantify and propagate uncertainties as risks of underperformance.

C. Design and test scenarios across different scales of retrofits and as a result, develop a set of decision-making tools.

Applicants must demonstrate advanced understanding of building physics and numerical modelling. Good programming skills and experience in using Machine learning will be deemed advantageous.