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Completed H2020 European Commission

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

€271.7K EUR

Funder European Commission
Recipient Organization City University of London
Country United Kingdom
Start Date Jan 01, 2022
End Date Jan 31, 2025
Duration 1,126 days
Number of Grantees 2
Roles Coordinator; Partner
Data Source European Commission
Grant ID 101028449
Grant Description

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.

All Grantees

City University of London; Sandia Corporation

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