A multiple mapping conditioning mixing model with a mixture-fraction like reference variable. Part 2: RANS implementation and validation against a turbulent jet flame

Varna, Achinta; Cleary, M. J.; Hawkes, Evatt R.

doi:10.1016/j.combustflame.2017.03.017

Cited by 14 publications

(11 citation statements)

References 30 publications

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“…The mesh is nonuniform with 96 and 40 cells along axial and radial direction, respectively, with local refinement near the axis of symmetry and the fuel inlet. Testing has confirmed that predictions do not substantially change with further refinements of the grid[29]. A variable time step scheme based on a maximum Courant number of 0.8 is used in the present work and it has also been confirmed that results do not substantially change if lower values are used.…”

supporting

confidence: 67%

“…where r t is a target correlation function between f and Z. For r t close to unity these settings for b 0 and C min also work well in inhomogeneous flows and it was subsequently shown that r t = 0.935 produces good predictions for both reactive and non-reactive scalars in jet diffusion flames [29].…”

Section: Mmc-ransmentioning

confidence: 93%

“…The MMC-LES and MMC-RANS computational models in mmcFoam have been tested against various experimental [19,20,21,22,29] and DNS [17] databases previously. The contribution of the present paper is to validate the numerical implementation with a focus on mass consistency between the Eulerian and Lagrangian schemes and numerical convergence with respect to the number of Pope particles and synthesised particulate sections used in the stochastic simulations.…”

Section: Validationmentioning

confidence: 99%

“…MMC-RANS is applied to Sandia flame D [34] which is a piloted methane/air flame exhibiting moderate levels of local extinction. In a previous MMC-RANS study [29] this flame, along with Sandia flames E and F which have higher levels of local extinction, was modelled with a focus on presenting the sensitivity of predictions to variations in the three primary model constants with C φ = 2, b o = 1/2 and r t = 0.935 being suggested as the most suitable values. It was also shown that flame F predictions did not vary substantially between a base case with N pc = 70, corresponding to N p = 268, 800 over the entire domain, and a refined case with twice that number.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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A stochastic multiple mapping conditioning computational model in OpenFOAM for turbulent combustion

et al. 2018

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Highlights• Stochastic MMC-LES and MMC-RANS are implemented into OpenFOAM.• Code architecture is based on layered template classes and abstract submodels.• Mass consistency of the hybrid Eulerian and Lagrangian schemes is demonstrated.• Numerical convergence with increasing stochastic particles is demonstrated.• Numerical convergence with increasing aerosol species sections is demonstrated. AbstractComputational models for combustion must account for complex and inherently interconnected physical processes including dispersion, mixing, chemical reactions, particulate nucleation and growth and, critically, the interactions of these with turbulence. The development of affordable and accurate models that are widely applicable is a work in progress. Stochastic multiple mapping conditioning (MMC) is a fast-emerging approach that has been successfully applied to non-premixed, premixed and partially premixed flames as well to the modelling of liquid and solid particulate synthesis. The method solves the conventional PDF transport equation but incorporates an additional constraint in that the mixing is localised in a reference space. This paper describes the numerical implementation of stochastic MMC in an OpenFOAM compatible code called mmcFoam. The model concepts and equations along with alternative submodels, code structure and numerical schemes are explained. A focus is placed on validation of the computational methods in particular demonstrating numerical convergence and mass consistency of the hybrid Eulerian/Lagrangian A C C E P T E D M A N U S C R I P T schemes. Four validation cases are selected including a combustion direct numerical simulation (DNS) case, two combustion experimental jet flame cases and a non-combusting particulate synthesis case. The results show that the total mass and mass distribution of Eulerian and Lagrangian schemes are consistent and confirm that the solutions numerically converge with increasing number of stochastic computational particles and sections for describing particulate size distribution.

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supporting

confidence: 67%

Section: Mmc-ransmentioning

confidence: 93%

Section: Validationmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

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A stochastic multiple mapping conditioning computational model in OpenFOAM for turbulent combustion

et al. 2018

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“…This is founded on the principles of ordinary and doubly-conditioned Conditional Moment Closures (CMC) [15] applied in previous studies [16,17,18]. The probabilistic approach [19] (similar to second-order CMC) provides an alternative [20], has been implemented in a variety of ways [21,22,23], and forms the basis for the MMC component of the current hybrid model. Surrogate reference variables have been developed for MMC to avoid difficulties in specifying model coefficients: some quantity (preferably already solved as part of the calculation procedure) is used as the reference variable.…”

Section: Introductionmentioning

confidence: 99%

A mixture-fraction-based hybrid binomial Langevin-multiple mapping conditioning model

Wandel

Lindstedt

2019

Proceedings of the Combustion Institute

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Generalized Multiple Mapping Conditioning (MMC) allows for the use of any physical quantity to represent the required reference variable provided that it delivers the desired behavior. The binomial Langevin model (BLM) has been shown to predict higher statistical moments with good accuracy. However, joint-scalar modeling for many scalars becomes problematic because scalar bounds must be specified as conditional on other scalars to preserve elemental balances. The resulting volumes in state space become exceptionally complex for realistic problem sizes. In the current work, this central difficulty is avoided by using only velocity and mixture fraction statistics from the BLM with the latter used as the MMC reference variable. The principal advantage of this method is that the implementation of the binomial Langevin mixture fraction is relatively straightforward and provides a direct physical link to MMC. The MMC model is closed using an augmented modified Curl's model where the selection of particle pairs for (turbulent) mixing ensures proximity in reference space and a corresponding closeness in physical space. The method is evaluated for a lifted methane jet flame undergoing auto-ignition in a vitiated coflow. Most of the major features of the flow are well reproduced and found to generally outperform other modeling approaches, including Large Eddy Simulations using simplified treatments of turbulence-chemistry interactions such as unsteady flamelet/progress variable descriptions.

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Theory and Application of Multiple Mapping Conditioning for Turbulent Reactive Flows

Ghai

Vogiatzaki

et al. 2017

Energy, Environment, and Sustainability

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A multiple mapping conditioning mixing model with a mixture-fraction like reference variable. Part 2: RANS implementation and validation against a turbulent jet flame

Cited by 14 publications

References 30 publications

A stochastic multiple mapping conditioning computational model in OpenFOAM for turbulent combustion

A stochastic multiple mapping conditioning computational model in OpenFOAM for turbulent combustion

A mixture-fraction-based hybrid binomial Langevin-multiple mapping conditioning model

Theory and Application of Multiple Mapping Conditioning for Turbulent Reactive Flows

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