MMC-LES modelling of droplet nucleation and growth in turbulent jets

Neuber, Gregor; Kronenburg, A.; Stein, Oliver T.; Cleary, M. J.

doi:10.1016/j.ces.2017.04.008

Cited by 28 publications

(29 citation statements)

References 51 publications

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“…Complex physics, including mixing, reaction and particle synthesis, and robust numerical schemes, including alternative chemical integrators, are implemented using a hierarchical and nested template structure and submodel classes. mmcFoam predictions have been compared to various detailed experimental [19,20,21,22,23] and DNS [17,24] databases.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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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Section: Accepted Manuscriptmentioning

confidence: 99%

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

et al. 2018

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“…Fluid flows, heat and mass transfer, as well as chemical reactions are common transport processes. Among them, turbulent shear flows often occur in chemical engineering including turbulent jet flows, turbulent reactions and combustions, and so on. Studying turbulent shear flows enables the comprehension of fluid flows in chemical engineering.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of turbulent shear flows using production‐limited delayed detached‐eddy simulation

2020

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In chemical engineering, turbulent shear flows are often encountered. The grid induced separation (GIS) and the slow RANS-LES transition issues should be alleviated when the delayed detached-eddy simulation (DDES) is used to simulate turbulent shear flows. This paper studies the performance of the production-limited DDES (PL-DDES) model in improving the GIS and the slow RANS-LES transition issues. Since the simplified IDDES (S-IDDES) model is proposed to improve the GIS issue, the S-IDDES model is chosen as the model for comparison. The simulation results show that the PL-DDES model with constant C d1 = 14 alleviates the GIS issue better than the S-IDDES model and the PL-DDES model with C d1 = 8. The results of the free shear layer show that the PL-DDES model can switch RANS to LES more rapidly and unlock the Kelvin-Helmholtz instability more effectively than the S-DDES model. For the backward-facing step flow, the S-IDDES model performs poorly when unlocking the Kelvin-Helmholtz instability in the separation zone. On the other hand, the PL-DDES model has a rapid RANS-LES transition after the step and produces a significant transport of momentum in the shear layer, leading to reasonable separation distance and flow structures. K E Y W O R D S grid induced separation, production-limited DDES, RANS-LES transition, turbulent shear flows

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“…Additionally, they were able to show that a numerical solution of the joint scalar-discrete number density pdf transport equation is computationally viable and can be achieved in reasonable computing times by using a Lagrangian stochastic particle solver [53]. Recently, Neuber et al [47] applied the PBE-PDF method based on a DPB in the context of LES and obtained predictions of droplet size distributions in a turbulent jet. Specifically, these authors combined a Lagrangian stochastic particle scheme based on the modified Curl micromixing model with a generalized multiple mapping conditioning (MMC) approach in order to localize micromixing in composition space.…”

Section: Introductionmentioning

confidence: 99%

“…For the same experimental campaign, Di Veroli and Rigopoulos [13] argued that, in the course of the measurements, droplet formation may have continued in the sampling tube, accounting, in part, for the mismatch between measured droplet size distributions and RANS-PBE-PDF predictions. Recently, Neuber et al [47] concluded that, in the presence of kinetic and experimental uncertainties, a quantitative model validation may instead be based on a series of experiments covering different operating conditions. In this light, our comparison with reference DNS-QMOM results has the advantage that both kinetic and measurement uncertainties are excluded and that, although definitive conclusions regarding the primary source of a discrepancy may be difficult to draw, potential sources of such discrepancies either on part of the LES-PBE-PDF method or the DNS-QMOM are well-defined.…”

Section: Droplet Condensation In a Turbulent Mixing Layermentioning

confidence: 99%

“…Recently, however, researchers have also invoked LES in order to analyze polydispersed particle formation in turbulent flows. Makowski et al [37], for example, investigated BaSO 4 precipitation in an impingement T-mixer, while Neuber et al [47] predicted the evolution of droplet size distributions in a turbulent jet. Although LES is considered in this article, we emphasize that our adaptive discretization scheme for the PBE as well as the PBE-PDF model for the turbulence-chemistry/particle formation interaction (see below) are not limited in validity to LES, but can similarly be applied in the context of RANS-based flow models.…”

Section: Introductionmentioning

confidence: 99%

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An LES-PBE-PDF approach for modeling particle formation in turbulent reacting flows

Sewerin

Rigopoulos

2017

Physics of Fluids

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Many chemical and environmental processes involve the formation of a polydispersed particulate phase in a turbulent carrier flow. Frequently, the immersed particles are characterized by an intrinsic property such as the particle size and the distribution of this property across a sample population is taken as an indicator for the quality of the particulate product or its environmental impact.In the present article, we propose a comprehensive model and an efficient numerical solution scheme for predicting the evolution of the property distribution associated with a polydispersed particulate phase forming in a turbulent reacting flow. Here, the particulate phase is described in terms of the particle number density whose evolution in both physical and particle property space is governed by the population balance equation (PBE). Based on the concept of large eddy simulation (LES), we augment the existing LES-transported PDF approach for fluid phase scalars by the particle number density and obtain a modelled evolution equation for the filtered probability density function (PDF) associated with the instantaneous fluid composition and particle property distribution. This LES-PBE-PDF approach allows us to predict the LES-filtered fluid composition and particle property distribution at each spatial location and point in time without any restriction on the chemical or particle formation kinetics. In view of a numerical solution, we apply the method of Eulerian stochastic fields, invoking an explicit adaptive grid technique in order to discretize the stochastic field equation for the number density in particle property space. In this way, sharp moving features of the particle property distribution can be accurately resolved at a significantly reduced computational cost.As a test case, we consider the condensation of an aerosol in a developed turbulent mixing layer. Our investigation not only demonstrates the predictive capabilities of the LES-PBE-PDF model, but also indicates the computational efficiency of the numerical solution scheme.

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MMC-LES modelling of droplet nucleation and growth in turbulent jets

Cited by 28 publications

References 51 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

Numerical investigation of turbulent shear flows using production‐limited delayed detached‐eddy simulation

An LES-PBE-PDF approach for modeling particle formation in turbulent reacting flows

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