Digital Twin Computer Science Collaboratory
Our mission is to develop a collaboratory that brings together computer science expertise to drive the necessary computing developments for digital twins. We aim to support society’s major initiatives, such as the UN’s 17 sustainable development goals, through investigating digital twins for massive infrastructure systems, including transport, power, and water networks, as well as airports and even the geography of nations. The functioning of these physical systems will be driven by analysis and simulation on their digital twins, which closely reflect their current state through sensor data collection.
University of Auckland Digital Twin Proof-of-Concept
Over the summer of 2021/2022 the university explored the potential of a digital twin to address its sustainability goals. Working with Nextspace, a digital twin of Building 302 was created incorporating a wide variety of information flows (from power consumption through to waste flows) to investigate how sustainability impacts could be visualised and controlled by building occupants through to university management.
Urban Flood Digital Twin
The MBIE-funded Building Innovation Partnership undertook an exploration of a common reference framework to bring together all the disparate information sources needed for flood modelling to test the resilience of infrastructure to flood events. A scenario of the Kaipoi River catchment was used by all agencies involved to test their ability to work together. This identified a range of issues in data matching, interoperability and simulation ability to be addressed to help move towards a national urban flood model.
Wifi-based occupancy estimation for a building-level digital twin
Over the summer of 2021/2022 Mengdi Liu investigated the use of Wi-Fi connection data to estimate occupancy levels within a building to inform sustainability metrics. Using data streams collected for the university proof of concept she was able to estimate daily occupancy levels and movement of individuals through the building.