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Artificial Intelligence for optimisation of Fuel Injection Equipment suitable fo...

Artificial Intelligence for optimisation of Fuel Injection Equipment suitable for carbon neutral synthetic fuels 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 s... ver más

31/01/2025

CITY

272K€

Presupuesto del proyecto: 272K€

Líder del proyecto

CITY UNIVERSITY OF LONDON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2025-01-31

MSCA-IF-2020: Individual Fellowships Objective:The goal of the Individual Fellowships is to enhance the creative and innovative potential...

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

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3 Participantes

CITY

271.73K€ | Lider

Sandia Corporation

Sandia national Laboratories

N.D. | Participante

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Duración del proyecto: 46 meses Fecha Inicio: 2021-03-24
Fecha Fin: 2025-01-31

Líder del proyecto

CITY UNIVERSITY OF LONDON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 272K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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