Sparse-Lagrangian particle methods for solid fuel combustion

Research project: Sparse-Lagrangian particle methods for solid fuel combustion

The combustion of pulverised solid fuels, particularly biomass and coal, is a major contributor to global energy consumption and pollutant emission. For increased process efficiency and to reduce pollution, reliable predictive tools for solid fuel conversion are needed. The aim of the proposed project is an efficient, yet very accurate model for predicting solid fuel flames in the framework of sparse-Lagrangian particle methods for large eddy simulation (LES). The model will be developed for Euler/Lagrange/Lagrange methods, which solve the Navier-Stokes equations in the Eulerian framework and use a first Lagrangian particle set to model the inertial solid fuel particles. A second particle cloud will contain massless stochastic particles that represent the probability density function (PDF) of the reacting scalars. To model the interphase heat and mass transfer, the two particle sets must be suitably coupled, which is the major focus of this work. Coupling methods will first be developed for the individual heterogeneous processes of devolatilisation and char conversion separately, followed by a generalised model for solid fuel combustion. The development of the new PDF-LES model will be supported by direct numerical simulations (DNS) that consider the relevant physics in a step-by-step approach. The generalised model will be validated by comparison of prediction results with state-of-the-art (laser-)measurements. This work shall considerably advance the fidelity of existing prediction methods for turbulent reacting gas-solid flows. 

Related publications

  1. L. Zhao, M. J. Cleary, O. T. Stein, and A. Kronenburg, “A two-phase MMC-LES model for pyrolysing solid particles in a turbulent flame,” Combust. Flame, vol. 209, pp. 322–336, (2019).


This image shows Oliver T. Stein

Oliver T. Stein

Deputy director

This image shows Tien Duc Luu

Tien Duc Luu

Scientific staff

This image shows Shiqi Meng

Shiqi Meng

Scientific Staff

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