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.
