Skip to main content
European Commission logo print header

Artificial Intelligence for optimisation of Fuel Injection Equipment suitable for carbon-neutral synthetic fuels

Project description

AI for hydrogen-derived carbon-neutral synthetic fuels

EU policies envisage the transition of the transport sector from fossil fuels to clean energy. Hydrogen derived carbon-neutral synthetic fuels produced using renewable energy sources present less life-cycle CO2 footprint than electric vehicles and are suitable for combustion engines. However, existing fuel spray experimental methods prevent the wide use of e-fuels. The EU-funded AI-FIE project is a Marie Skłodowska-Curie Actions fellowship aiming to develop a data-driven, deep learning and AI algorithm to predict the spatially and temporally resolved spray structure and critical parameters for engine design. The innovative project will base training on the most extensive publicly available experimental database for fuel sprays of the Engine Combustion Network.

Objective

Current EU policies mandate the gradual disengagement of the transport sector from fossil fuels. In order for such a transition to become a reality, hydrogen-derived carbon-neutral synthetic fuels produced using renewable energy sources (e-fuels), have overall less life-cycle CO2 footprint than their counterpart electric vehicles while they are suitable for use over the wide range of combustion engines. However, today’s fuel spray experimental methods are compromised by the long time needed for the characterisation of the effect of new fuel molecules; similarly, relevant predictive models that can address in detail the effect of the wide range of fuel chemical composition at time scales relevant to industry are not available. The main objective of the proposed MSCA fellowship is the development of a data-driven deep learning (DL) Artificial Intelligence (AI) algorithm able to predict the spatially and temporally resolved spray structure, as well as critical air / fuel mixture parameters for engine design. Training of the AI model will be based on the largest publicly available experimental database for fuel sprays of the Engine Combustion Network; this covers a wide range of injector configurations, air thermodynamic conditions and liquid fuels. The training matrix of the AI algorithm will be complemented by relevant computational fluid dynamics simulations for operating conditions and fuel composition for which experimentation is not possible. For this purpose, a state-of-the-art CFD model of the compressible Navier-Stokes and energy conservation equations employing elaborate real-fuel thermodynamic closures based on the PC-SAFT equation of state will be employed. The project innovative nature spans across diverse research aspects with emphasis on renewable alternatives of Diesel and gasoline. As such, it is expected to assist EU energy, marine, aviation and automotive industries to meet the goals imposed regarding the utilisation of renewable fuels.

Coordinator

CITY UNIVERSITY OF LONDON
Net EU contribution
€ 271 732,80
Address
NORTHAMPTON SQUARE
EC1V 0HB London
United Kingdom

See on map

Region
London Inner London — East Haringey and Islington
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 271 732,80

Partners (1)