Improvement of Calculations for Turbulent Premixed Flame Characteristics Determination using PDF Monte Carlo Simulation

This study aims at simulating turbulent premixed flame in a constant-pressure vessel (P = 1 atm) where the turbulence is supposed to be homogeneous and isotropic. The mixture of gas is composed by iso-octane-air. The realized CFD were based on Lagrange approach in Monte Carlo simulations. We focused on calculations of; flame radii R F , the flame propagation velocity S t , flame-brush thick ness  t an d flammability limit. During the study, influencing crucial parameters such as, the equivalence ratio  and the turbulence intensity u’ were considered. Results show that the equivalence ratio enhances the flame propagation when passing from lean to stoichiometric flames. Also, the turbulence intensity yields a notable growth for the flame characteristics mentioned above. Moreover, we noticed that the flammability limit is strongly depending of the turbulence intensity and the equivalence ratio. More precisely, we remarked that the minimum ignition energy (MIE) was situated quite smaller than the stoichiometric condition. But, it increased with the turbulence intensity.

Data-Driven Identification of Nonlinear Flame Models

This paper presents a data-driven identification framework with the objective to retrieve a flame model from the nonlinear limit cycle. The motivation is to identify a flame model for configurations, which do not allow the determination of the flame dynamics: that is commonly for industrial applications where (i) optical access for nonintrusive measurements of velocity and heat release fluctuations are not feasible and (ii) unstable combustion is monitored via multiple pressure recordings. To demonstrate the usefulness of the method, we chose three test cases: (i) a classical Rijke tube; (ii) an experiment of a laminar flame (EM2C case), (iii) and a high-pressure, turbulent premixed flame (German Aerospace Center (DLR) case). The procedure is as follows: First, acoustic network models for the three cases are generated for which the in-house software taX is employed. Next, the acoustic network models are embedded in an optimization framework with the objective to identify flame parameters that match the experimental limit cycle data: based on the instability frequency and pressure amplitudes, we formulate physical constraints and an objective function in order to identify the flame model parameters gain nopt and time delay τopt in the nonlinear regime. We demonstrate for the three cases that the identified flame parameters reproduce the unstable combustion processes and highlight the usefulness of the method for control purposes.