Launched in September 2021 for a period of 4 years, the ANR ALEKCIA project brings together 3 academic partners: PPRIME, PRISME and IFPEN.
Electrification of vehicles and improved efficiency of internal combustion engines (ICE) are the main levers to reduce greenhouse gas emissions. One of the priorities of the European and National strategies is the development of hydrogen as a renewable energy source, via a fuel cell or via its combustion in a spark-ignition engine (SIE). However, technological challenges must be tackled before meeting real driving emissions expectation due to the diversification and complexity of hybrid applications as well as to the deployment of carbon-free fuels like hydrogen which receives a great deal of attention as future sustainable energy carrier. For flow aerodynamics, mixing and combustion down to the individual engine cycle, challenges are for example associated to robustness of concepts on a cycle basis, rapid variations of engine loads observed in hybrid technologies during transients, the occurrence of extreme cycles for a wider range of operating conditions. Numerical, experimental and analyzing tools have made significant progress in recent years for the analysis of spatial and temporal scales of the unsteady in-cylinder flows. Large-Eddy Simulation (LES) is an essential tool for the design of robust concepts. While LES has been validated against well-defined experiments, the prediction of internal turbulent dynamics and combustion during a cycle is affected by epistemic uncertainties. A problematic aspect is that it is difficult to prescribe boundary conditions that can reproduce the fine disturbances observed in real systems. These disturbances, which are uncertain in an epistemic sense, can lead to the evolution and emergence of rare events that can in turn affect the overall performance of the engine. Therefore, progress is still needed to obtain optimal and robust design.
More precisely, the major scientific challenges addressed by ALEKCIA are to:
- quantify and reduce uncertainties (UQ) due to model parameters and BCs,
- develop new Data Assimilation (DA) approaches for coupling LES with experimental measurements,
- develop new decomposition methods to analyze massive data generated by instantaneous LES and high-speed PIV fields,
- combine them with UQ and DA methods for detailed analysis of individual SIE cycles during steady operations and fast transients. The detailed analysis of results will determine which key parameters are responsible for sporadic phenomena and cyclic variability.
A strength of the project is that the developed tools and methodologies could also benefit to a range of applications well beyond internal combustion engines, in situations where complex turbulent flows need to be mastered to achieve higher efficiency such as in the fields of propulsion or energy generation.
Contact :Karine TRUFFIN