This research is a part of the 'Horizon 2020' project 'ESTiMatE' funded by the European Commission within the 'Clean Sky' programme. The main objective of ESTiMatE is to develop a modeling strategy using CFD simulations for the prediction of soot in terms of chemical evolution and particle formation in conditions relevant to the aero-engine operation. Specifically, the project aims to extend and improve the computational modeling platform developed at TU/e for effective soot modeling capabilities by combining a discrete sectional method with FGM tabulated chemistry.The project is divided into three main stages. The initial stage of the project is focused on the development of advanced sectional methods-based soot model for capturing soot concentration and soot size distribution in laminar flames. The model will be implemented in the one-dimensional flame code CHEM1D, coupled with detailed gas-phase chemical kinetics for kerosene-like surrogates, and validated for experimental measurements available in the literature.In the second stage, the developed sectional model will be coupled with the Flamelet Generated Manifolds (FGM) tabulated chemistry for application to laminar and turbulent flames. A multistage FGM approach will be developed which uses separate control variables for different stages of combustion to accurately parameterize the concentration of soot precursors and soot source terms. In this stage, different FGM-soot coupling strategies will be evaluated in terms of accuracy and computational efficiency.The final stage of the project is dedicated to carrying out numerical simulations with the LES approach for the developed soot model in combination with FGM tabulated chemistry. The assessment of the predictive capabilities of the soot model in a gas turbine relevant configuration will be conducted. Besides, the performance of the different models and the impact of LES on the prediction capabilities respect to RANS models will be studied based on the experimental measurements acquired through the project collaborators.
People involved in this project: Jeroen van Oijen & Abhijit Kalbhor