期刊
JOURNAL OF THERMAL SCIENCE
卷 30, 期 1, 页码 343-361出版社
SPRINGER
DOI: 10.1007/s11630-020-1300-8
关键词
micro-combustion; OpenFOAM; conjugate heat transfer; thermally orthotropic combustor; flame streets
资金
- National Natural Science Foundation of China [51806158]
- Fundamental Research Funds for the Central Universities [WUT: 2018IVA055, WUT: 2019IVB029]
A versatile numerical tool based on OpenFOAM framework was developed for modeling low-Mach number reacting flows, showing good performance in reproducing experimental flames.
A versatile numerical tool based on the open-source framework OpenFOAM has been developed in this paper for modeling time-accurate, low-Mach number reacting flows, with a particular interest in small-scale flames. This tool consists of a gas-phase Navier-Stokes solver and a solid-wall heat conduction solver which can be implemented alone, or used together in a coupled means to reveal the small-scale combustion's characteristics of significantly enhanced flame-wall thermal coupling. Validation works has proved that the tool is capable of reproducing experimental flames at various scales (from conventional to small scales), including well-recognized micro-flame features in literature such as three modes of premixed flame dynamics (weak flames, flames with repetitive extinction and ignition, and stable flames). Then, an experimentally-already-found but rarely-simulated unique phenomenon of diffusion flame street is successfully reproduced with well-captured flame structures. Moreover, the conjugate heat transfer model with the specific formulation of solid-wall heat conduction enables an attempt to simulate a novel, thermally-orthotropic combustor with its axial thermal conductivities superior to the transverse ones. Finally, computational performance of the developed OpenFOAM solver is compared to that of the previously-used compressible flow solver Eilmer. The OpenFOAM solver is found to show better wave-damping abilities for overcoming acoustic wave effects at the initial stage of simulations, and is much more efficient in terms of the computational cost.
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