Head of Research Division, School of Engineering, University of Glasgow
Biography
Professor David Flynn is internationally recognised and award-winning expert in the field of cyber physical systems and digital twinning for future energy networks and markets. He is an Honorary Professor of Heriot-Watt University, Fellow of the Royal Society of Edinburgh (FRSE) and an Eminent Overseas Professor of Nagasaki University. His expertise in cyber physical systems (CPS) and digitalisation, underpins strategic UK and global ambitions in future energy networks and digital energy markets. Prof. Flynn leads one of the UKs largest R&D portfolios in future energy networks and enabling technologies. He is the joint Director of the UKs National Digital Twinning hub for Decarbonisation (TransiT, £46M), and an Associate Director of two other EPSRC National Hubs for UK Critical National Infrastructure (CNI) -Decarbonised, Adaptive, Resilient future Transport Infrastructure (DARe, £10.5M) and Hydrogen Integration for Accelerated Energy Transitions (HI-ACT, £10.5M). Providing leadership in digitalisation and CPS for accelerating secure, sustainable, equitable and resilient whole system reform of CNI.
Selected publications
D. Flynn et al (2025). A systematic review of modelling methods for studying the integration of hydrogen into energy systems, Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2024.114964
D. Flynn et al (2023). Responsive FLEXibility: A smart local energy system, Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2023.113343
D. Flynn et al (2021). Modelling the redistribution of benefits from joint investments in community energy projects. https://doi.org/10.1016/j.apenergy.2021.116575
D. Flynn et al (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review, Renewable and Sustainable Energy Reviews, https://doi.org/10.1016/j.rser.2020.109899
Roman, D., Saxena, S., Robu, V., Pecht, M., & Flynn, D. (2021). Machine learning pipeline for battery state-of-health estimation. Nature Machine Intelligence. https://doi.org/10.1038/s42256-021-00312-3
