Real-time digital optimisation and decision making for energy and transport systems

Completed

Other Cross-Cutting Technologies or Research(Energy Models)
Other Cross-Cutting Technologies or Research(Energy system analysis)
Renewable Energy Sources(Wind Energy)
Energy Efficiency(Transport)
Hydrogen and Fuel Cells(Hydrogen)

Basic and strategic applied research

PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics)
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics)
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering)
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering)

Not Cross-cutting
Systems Analysis related to energy R&D (Other Systems Analysis)
Sociological economical and environmental impact of energy

Dr G Rigas
Aeronautics
Imperial College London

Standard

EPSRC

01 May 2023

31 March 2025

23 months

£1,414,614

Energy

London

Technology Missions Fund

Dr G Rigas, Aeronautics, Imperial College London

Dr A Borovykh, Mathematics, Imperial College London
Dr S Laizet, Aeronautics, Imperial College London
Dr L Magri, Aeronautics, Imperial College London

Project Contact, Catesby Projects
Project Contact, NVIDIA Corporation, USA
Project Contact, Engys Ltd (UK)
Project Contact, Atkins

In this project, we will seamlessly combine two disciplines that have been historically received continuous government and industrial funding: physics-based modelling, which is generalisable and robust but may require tremendous computational cost, and machine learning, which is adaptive and fast to be evaluated but not easily generalisable and robust. The intersection of the two spawns scientific machine learning, which maximises the strengths and minimises the weaknesses of the two approaches. The data will be provided by high-fidelity simulations and experiments, from the UK state-of-the-art facilities and software. The efficiency of the machine learning training will be maximised for the algorithms to require minimal energy (thereby, producing minimal emissions by minimising electricity consumption). This project builds upon large UK and EU funded expertise in scientific machine learning and simulation, which will be generalised to fast, real-time decision making. The most significant bottleneck of most scientific machine learning is that they need time to be re-trained offline when new data becomes available. We will transform offline paradigms into real-time approaches for the models to re-adapt and provide accurate estimates on the fly. This project will culminate into the delivery of practical digital twins (defined as digital counterparts of real world physical systems or processes that can be used for simulation, prediction of behaviour to inputs, monitoring, maintenance, planning and optimisation) to solve currently intractable problems in wind energy, hydrogen, and road transportation. This project will transfer the technical achievements and real-time digital twin to policy-making

14/06/23