Descripción del proyecto
Inteligencia artificial para unos combustibles sintéticos derivados del hidrógeno con cero emisiones netas
Las políticas de la Unión Europea prevén la transición del sector del transporte de los combustibles fósiles a la energía limpia. Los combustibles sintéticos obtenidos del hidrógeno con cero emisiones netas producidos empleando fuentes de energía renovable presentan una menor huella de CO2 del ciclo de vida que los vehículos eléctricos y son aptos para los motores de combustión. Sin embargo, los métodos experimentales existentes de pulverización del combustible impiden un uso generalizado de los electrocombustibles. El proyecto AI-FIE, financiado con fondos europeos, es una beca de investigación de las Acciones Marie Skłodowska-Curie que tiene como objetivo desarrollar un algoritmo de inteligencia artificial y aprendizaje profundo basado en datos para predecir, con resolución espacial y temporal, la estructura del pulverizador y los parámetros críticos para el diseño de motores. Este proyecto innovador basará la formación en la base de datos experimental de acceso público más amplia sobre pulverización de combustible de la Engine Combustion Network.
Objetivo
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.
Ámbito científico
- engineering and technologyenvironmental engineeringenergy and fuelsliquid fuels
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineering
- engineering and technologyenvironmental engineeringenergy and fuelssynthetic fuels
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Programa(s)
Régimen de financiación
MSCA-IF-GF - Global FellowshipsCoordinador
EC1V 0HB London
Reino Unido