Development of an AI-based strategy to accelerate the computation of chemical kinetics in Sustainable Aviation Fuel spray combustion

Air traffic is responsible for an increasingly significant share of global CO₂ emissions. The use of Sustainable Aviation Fuels (SAFs) offers a promising path toward reducing the carbon footprint of the aviation sector. Although SAFs exhibit physico-chemical properties similar to conventional kerosene, their combustion behavior can differ significantly, requiring adjustments and optimization of current gas turbines (GT). In this context, numerical simulation plays an essential role for the aerospace industry, enabling cost-effective design and optimization of GT systems. However, several challenges remain, particularly in achieving both accurate and computationally efficient simulations. One of the main bottlenecks is the numerical integration of chemical kinetics, which is computationally expensive due to the large number of species and reactions involved in SAF combustion. Recent advances have demonstrated that machine learning techniques, particularly neural networks, can significantly accelerate chemical kinetics computations. Nevertheless, most of these developments have focused on conventional hydrocarbons under purely gaseous conditions. In contrast, SAF combustion in GTs occurs in a multiphase regime, where complex interactions between liquid fuel droplets and the flame must be taken into account. The objective of this thesis is to extend machine learning-based chemical kinetics acceleration methods to the multiphase combustion of SAFs injected as sprays. A key challenge lies in generating a suitable training dataset that accurately reflects the operating conditions of industrial systems. The work will build upon a methodology previously developed at CORIA and IFPEN, based on the coupled simulation of interacting 0D reactors, which will be adapted to account for spray combustion dynamics. The research will initially focus on a canonical laboratory flame, before being applied to a configuration representative of an actual SAF-fueled aero-engine burner.
 

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site. 
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions. 
 

Academic requirements    University Master degree involving CFD, physics and/or numerical modelling
Language requirements    English level B2 (CEFR)    
Other requirements    Programming skills (Python, C++)