A new perspective on modelling passive scalar conditional mixing statistics in turbulent spray flames

被引:6
|
作者
Wang, Bosen [1 ]
Kronenburg, Andreas [1 ]
Stein, Oliver T. [1 ]
机构
[1] Univ Stuttgart, Inst Tech Verbrennung, Herdweg 51, D-70174 Stuttgart, Germany
关键词
CP-DNS; Turbulent spray; Stabilized flame; Mixture fraction; Scalar dissipation; PDF; LARGE-EDDY SIMULATION; DIRECT NUMERICAL SIMULATIONS; DROPLET ARRAY COMBUSTION; MOMENT CLOSURE; DISSIPATION RATE; DILUTE; VAPORIZATION; FLOWS; CMC; LES;
D O I
10.1016/j.combustflame.2019.07.016
中图分类号
O414.1 [热力学];
学科分类号
摘要
Carrier-phase direct numerical simulations (CP-DNS) are performed for turbulent spray combustion. The CP-DNS database is used to evaluate scaling laws for modelling characteristic mixing quantities, such as the mixture fraction conditional scalar dissipation, N-f, and its probability density function (PDF or P-f) in inter-droplet space. Three different methods to process the input parameters for the scaling laws are evaluated. If conditionally averaged quantities are used as input, the models can better capture (1) the scalar dissipation's exponential increase for larger mixture fraction values and (2) the overall shape of the mixture fraction distribution featuring a distinct secondary peak at stoichiometry that results from increased droplet evaporation in regions where the flame is located. It is also found that the scaling laws provide acceptable estimates independent of droplet equivalence ratio, droplet pre-evaporation rate, spray combustion regime, droplet Stokes number, droplet size and large turbulence scale. This study provides a more physical perspective to the understanding of the mixing in spray combustion. The scaling laws for turbulent micro-mixing may have better predictive capability than the existing sub-models for single phase combustion used in flamelet and conditional moment closure (CMC) approaches that provide sub grid closures for turbulence-chemistry interactions in LES or RANS. (C) 2019 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
引用
收藏
页码:376 / 387
页数:12
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