H2020Индивидуална стипендия2022–2025

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2022-01-01 → 2025-01-31
Финансиране от ЕС: 271 733 €
Участници: 2
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Изкуствен интелект и компютърни модели се използват за оптимизиране на системите за впръскване на синтетични еко-горива. Това помага за намаляване на парниковите газове от транспорта, като позволява използването на съществуващите двигатели без нужда от нова инфраструктура.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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

Decarbonizing the transport sector is vital for addressing climate change. While electric vehicles contribute to reducing Green House Gas (GHG) emissions, they are not entirely zero-emission due to the embedded emissions in battery production and the use of non-renewable electricity. Hydrogen-derived CO2-neutral synthetic fuels (e-fuels) present a promising alternative, as they can be used in existing internal combustion engines (ICE) and jet engines, avoiding the need for new infrastructure. This project supports the European Green Deal's goal of achieving net-zero GHG emissions by 2050. Overall Objectives: 1.Modeling Fuel Properties: Develop a computational framework using PC-SAFT Equations of State (EoS) to predict the physical and transport properties of various conventional and e-fuels under different pressures and temperatures. This includes evaluating the performance of fuels like oxymethylene dimethyl ethers (OME3–4), gasoline, diesel, aviation fuel, and alcohol blends. 2.Creating the Specialized CFD Tool: Develop a specialized CFD code to generate training data for the AI, validated against experimental data, to accurately simulate complex flow dynamics within fuel injection systems. 3.AI Algorithm Development: Create a deep learning AI algorithm using deep autoencoders and neural networks to predict in-nozzle flow and spray characteristics of vaporizing liquid fuels. This algorithm aims to accelerate the simulation process by 3-4 orders of magnitude faster than current experimental and CFD methods, considering e-fuel composition, fuel injection equipment (FIE) design, and varying P-T conditions in combustion systems. 4.Application to E-Fuels: Apply the AI-driven methods to design and optimize new synthetic e-fuels, leveraging the specialized CFD code to simulate a broad range of scenarios and capture intricate behaviors of e-fuels in injection systems. Conclusions of the Action: The project successfully developed a predictive thermodynamic model and a specialized CFD code, significantly reducing the need for physical experimentation. These tools were validated against experimental data, demonstrating high fidelity. However, certain aspects, such as integrating a neural network to connect the latent space of the encoder-decoder with the fuel composition, remain unfinished. The overall progress supports the rapid development and optimization of e-fuels, contributing to the reduction of GHG emissions and advancing the EU's decarbonization goals.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

CITY ST GEORGES UNIVERSITY OF LONDON · LONDONКоординаторОбединеното кралство
Sandia Corporation · AlbuquerqueСъединени щати