A forced ignition probability analysis method is developed for turbulent combustion,in which kernel formation is analyzed with local kernel formation criteria,and flame propagation and stabilization are simulated with...A forced ignition probability analysis method is developed for turbulent combustion,in which kernel formation is analyzed with local kernel formation criteria,and flame propagation and stabilization are simulated with Lagrangian flame particle tracking.For kernel formation,the effect of turbulent scalar transport on flammability is modelled through the incorporation of turbulenceinduced diffusion in a spherically outwardly propagating flame kernel model.The dependence of flammability limits on turbulent intensities is tabulated and serves as the flammability criterion for kernel formation.For Lagrangian flame particle tracking,flame particles are tracked in a structured grid with flow fields being interpolated from a Computational Fluid Dynamics(CFD)solution.The particle velocity follows a Langevin model consisting of a linear drift and an isotropic diffusion term.The Karlovitz number is employed for the extinction criterion,which compares chemical and turbulent timescales.The integration of the above two-step analysis approach with non-reacting CFD is achieved through a general interpolation interface suitable for general unstructured CFD grids.The method is demonstrated for a methane/air bluff-body flame,in which flow and fuel/air mixing characteristics are extracted from a non-reacting simulation.Results show that the computed ignition probability map agrees qualitatively with experimental results.A reduction of the ignition probability in the recirculation zone and a high ignition probability on the shear layer of the recirculation zone near the mean stoichiometric surface are well captured.The tools can facilitate optimization of spark placement and offer insights into ignition processes.展开更多
Reduced order models for ignition analysis can offer insights into ignition processes and facilitate the combustor optimization.In this study,a Pairwise Mixing-Reaction(PMR)model is formulated to model the interaction...Reduced order models for ignition analysis can offer insights into ignition processes and facilitate the combustor optimization.In this study,a Pairwise Mixing-Reaction(PMR)model is formulated to model the interaction between the flame particle and the surrounding cell mixture during Lagrangian flame particle tracking.Specifically,the model accounts for the two-way coupling of mass and energy between the flame particle and the surrounding shell layer by modelling the corresponding turbulent mixing,chemical reaction and evaporation process if present.The state of a flame particle,e.g.,burnt,hot gas or extinguished,is determined based on particle temperature.This model can properly describe the ignition process with a spark kernel being initiated in a nonflammable region,which is of practical importance in certain turbine engines and has not been rigorously accounted for by the existing models based on the estimation of local Karlovitz number.The model is integrated into an ignition probability analysis platform and is demonstrated for a methane/air bluff-body flame with the flow and fuel/air mixing characteristics being extracted from a non-reacting simulation.The results show that for the spark location being at the extreme fuellean outer shear layer of the recirculation zone,PMR can yield ignition events with a significant number of active flame particles.The mechanisms for the survival of the initial flame particles and the entrainment of the survived flame particles into the recirculation zone are analyzed.The results also show that the ignition probability map from PMR agrees well with the experimental observation:a high ignition probability in the shear layer of the recirculation zone near the mean stoichiometric surface,and low ignition probabilities inside the recirculation zone and the top stagnation region of the recirculation zone.The parametric study shows that the predicted shape of the ignition progress factor and ignition probability is in general insensitive to the model parameters and the model is展开更多
基金the National Natural Science Foundation of China(No.91841302)National Major Science and Technology Project(No.2017-Ⅲ-0007-0032)Research Fund from Tsinghua University(No.2019Z08YJL03)。
文摘A forced ignition probability analysis method is developed for turbulent combustion,in which kernel formation is analyzed with local kernel formation criteria,and flame propagation and stabilization are simulated with Lagrangian flame particle tracking.For kernel formation,the effect of turbulent scalar transport on flammability is modelled through the incorporation of turbulenceinduced diffusion in a spherically outwardly propagating flame kernel model.The dependence of flammability limits on turbulent intensities is tabulated and serves as the flammability criterion for kernel formation.For Lagrangian flame particle tracking,flame particles are tracked in a structured grid with flow fields being interpolated from a Computational Fluid Dynamics(CFD)solution.The particle velocity follows a Langevin model consisting of a linear drift and an isotropic diffusion term.The Karlovitz number is employed for the extinction criterion,which compares chemical and turbulent timescales.The integration of the above two-step analysis approach with non-reacting CFD is achieved through a general interpolation interface suitable for general unstructured CFD grids.The method is demonstrated for a methane/air bluff-body flame,in which flow and fuel/air mixing characteristics are extracted from a non-reacting simulation.Results show that the computed ignition probability map agrees qualitatively with experimental results.A reduction of the ignition probability in the recirculation zone and a high ignition probability on the shear layer of the recirculation zone near the mean stoichiometric surface are well captured.The tools can facilitate optimization of spark placement and offer insights into ignition processes.
基金supported by the National Natural Science Foundation of China(No.91841302)the National Science and Technology Major Project(No.2017-III-0007-0032)。
文摘Reduced order models for ignition analysis can offer insights into ignition processes and facilitate the combustor optimization.In this study,a Pairwise Mixing-Reaction(PMR)model is formulated to model the interaction between the flame particle and the surrounding cell mixture during Lagrangian flame particle tracking.Specifically,the model accounts for the two-way coupling of mass and energy between the flame particle and the surrounding shell layer by modelling the corresponding turbulent mixing,chemical reaction and evaporation process if present.The state of a flame particle,e.g.,burnt,hot gas or extinguished,is determined based on particle temperature.This model can properly describe the ignition process with a spark kernel being initiated in a nonflammable region,which is of practical importance in certain turbine engines and has not been rigorously accounted for by the existing models based on the estimation of local Karlovitz number.The model is integrated into an ignition probability analysis platform and is demonstrated for a methane/air bluff-body flame with the flow and fuel/air mixing characteristics being extracted from a non-reacting simulation.The results show that for the spark location being at the extreme fuellean outer shear layer of the recirculation zone,PMR can yield ignition events with a significant number of active flame particles.The mechanisms for the survival of the initial flame particles and the entrainment of the survived flame particles into the recirculation zone are analyzed.The results also show that the ignition probability map from PMR agrees well with the experimental observation:a high ignition probability in the shear layer of the recirculation zone near the mean stoichiometric surface,and low ignition probabilities inside the recirculation zone and the top stagnation region of the recirculation zone.The parametric study shows that the predicted shape of the ignition progress factor and ignition probability is in general insensitive to the model parameters and the model is
