Sparse-Lagrangian MMC modelling of the Sandia DME flame series

Neuber, Gregor; Fuest, Frederik; Kirchmann, Jonas; Kronenburg, A.; Stein, Oliver T.; Galindo-Lopez, S.; Cleary, M. J.; Coriton, Bruno; Frank, Jonathan H.; Sutton, Jeffrey A.

doi:10.1016/j.combustflame.2019.06.026

Cited by 22 publications

(5 citation statements)

References 32 publications

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“…The particle pairs are selected such that they are local in reference mixture fraction space and the mixing extent is controlled by a mixing time scale, L . For a detailed discussion on the modelling of the mixing time scale the reader is referred to Vo et al (2017a, b) and Neuber et al (2019b).…”

Section: Methodsmentioning

confidence: 99%

“…For the Lagrangian scheme, 230 000 stochastic particles have been used to compute the subgrid distribution of composition and particulate number density, which corresponds to one particle for every 6.5 LES cells. All MMC modelling parameters are standard as defined in earlier publications (Neuber et al 2017;Cleary and Klimenko 2011;Vo et al 2017b;Neuber et al 2019b).…”

Section: Numerical Configurationmentioning

confidence: 99%

“…The MMC model has been validated widely for different turbulent flames, e.g. see (Cleary and Klimenko 2011;Vo et al 2017a;Galindo-Lopez et al 2018;Neuber et al 2019b). Recent publications have shown that the method can also be applied to particulate nucleation and growth processes (Neuber et al 2017;Vo et al 2017a) where interactions with turbulence are important.…”

Section: Introductionmentioning

confidence: 99%

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Sparse-Lagrangian PDF Modelling of Silica Synthesis from Silane Jets in Vitiated Co-flows with Varying Inflow Conditions

Neuber

Kronenburg

Stein

et al. 2020

Flow Turbulence Combust

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This paper presents a comparison of experimental and numerical results for a series of turbulent reacting jets where silica nanoparticles are formed and grow due to surface growth and agglomeration. We use large-eddy simulation coupled with a multiple mapping conditioning approach for the solution of the transport equation for the joint probability density function of scalar composition and particulate size distribution. The model considers inception based on finite-rate chemistry, volumetric surface growth and agglomeration. The sub-models adopted for these particulate processes are the standard ones used by the community. Validation follows the "paradigm shift" approach where elastic light scattering signals (that depend on particulate number and size), OH-and SiO-LIF signals are computed from the simulation results and compared with "raw signals" from laser diagnostics. The sensitivity towards variable boundary conditions such as co-flow temperature, Reynolds number and precursor doping of the jet is investigated. Agreement between simulation and experiments is very good for a reference case which is used to calibrate the signals. While keeping the model parameters constant, the sensitivity of the particulate size distribution on co-flow temperature is predicted satisfactorily upstream although quantitative differences with the data exist downstream for the lowest coflow temperature case that is considered. When the precursor concentration is varied, the model predicts the correct direction of the change in signal but notable qualitative and quantitative differences with the data are observed. In particular, the measured signals show a highly non-linear variation while the predictions exhibit a square dependence on precursor doping at best. So, while the results for the reference case appear to be very good, shortcomings in the standard submodels are revealed through variation of the boundary conditions. This demonstrates the importance of testing complex nanoparticle synthesis models on a flame series to ensure that the physical trends are correctly accounted for.

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Section: Methodsmentioning

confidence: 99%

Section: Numerical Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sparse-Lagrangian PDF Modelling of Silica Synthesis from Silane Jets in Vitiated Co-flows with Varying Inflow Conditions

Neuber

Kronenburg

Stein

et al. 2020

Flow Turbulence Combust

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“…Sparse MMC-LES has been extensively validated with a range of subsonic experimental combustion configurations of practical relevance, e.g. piloted methane / air jet diffusion flames [25], methane / air swirl flames [26], Sandia DME flame series [27] and turbulent spray flames [28]. In our recent work, sparse MMC-LES was extended to highly-compressible conditions for the first time [29] and demonstrated good accuracy against experimental data [30] for velocity, pressure and temperature data in a model supersonic combustor.…”

Section: Introductionmentioning

confidence: 99%

Large eddy simulation of a supersonic lifted hydrogen flame with sparse-Lagrangian multiple mapping conditioning approach

Huang

Cleary

Ren

et al. 2022

Combustion and Flame

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“…The low number of particles is realizable, because mixing is localised in physical space and the reference space. In non-premixed flames, mixture fraction usually characterizes the local species composition well and using the LES filtered mixture fraction as a reference variable has led to good results for a number of applications (Neuber et al 2017;Huo et al 2019;Neuber et al 2019). Specific implementation issues are summarized by Galindo-Lopez et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Mixing Time Scale Models for Multiple Mapping Conditioning with Two Reference Variables

Straub

Kronenburg

Stein

et al. 2020

Flow Turbulence Combust

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A novel multiple mapping conditioning (MMC) approach has been developed for the modelling of turbulent premixed flames including mixture inhomogeneities due to mixture stratification or mixing with the cold surroundings. MMC requires conditioning of a mixing operator on characteristic quantities (reference variables) to ensure localness of mixing in composition space. Previous MMC used the LES-filtered reaction progress variable as reference field. Here, the reference variable space is extended by adding the LES-filtered mixture fraction effectively leading to a double conditioning of the mixing operator. The model is used to predict a turbulent stratified flame and is validated by comparison with experimental data. The introduction of the second reference variable also requires modification of the mixing time scale. Two different mixing time scale models are compared in this work. A novel anisotropic model for stratified combustion leads to somewhat higher levels of fluctuations for the passive scalar when compared with the original model but differences remain small within the flame front. The results show that both models predict flame position and flame structure with good accuracy.

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Sparse-Lagrangian MMC modelling of the Sandia DME flame series

Cited by 22 publications

References 32 publications

Sparse-Lagrangian PDF Modelling of Silica Synthesis from Silane Jets in Vitiated Co-flows with Varying Inflow Conditions

Sparse-Lagrangian PDF Modelling of Silica Synthesis from Silane Jets in Vitiated Co-flows with Varying Inflow Conditions

Large eddy simulation of a supersonic lifted hydrogen flame with sparse-Lagrangian multiple mapping conditioning approach

Mixing Time Scale Models for Multiple Mapping Conditioning with Two Reference Variables

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