黄佐华  (教授)

博士生导师 硕士生导师

教师英文名称:Zuohua Huang

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所在单位:能源与动力工程学院 绿色氢电全国重点实验室

职务:教授

学历:博士研究生毕业

办公地点:西安交通大学兴庆校区北二楼四层

性别:男

联系方式:

学位:博士

学科:动力工程及工程热物理

   

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Numerical simulation of premixed combustion using the modified dynamic thickened flame model coupled with multi-step reaction mechanism (PDF)

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发布时间:2025-04-30

发布时间:2025-04-30

论文名称:Numerical simulation of premixed combustion using the modified dynamic thickened flame model coupled with multi-step reaction mechanism (PDF)

发表刊物:Fuel

摘要:Thickened flame (TF) model is one of the effective methods to resolve the flame front in turbulent premixed combustion modeling. The multi-step reaction mechanism is becoming increasingly important for combustion simulations such as pollutant formation, ignition and extinction. The effect of TF model on flame structures when coupling with multi-step reaction mechanism was investigated. The simulation results show that, no matter in laminar or turbulent condition, the global TF model coupling with multi-step reaction mechanism results in an incomplete combustion, which is mainly due to the enhanced species diffusion. Although Durand and Polifke’s dynamic thickened flame (DTF) sensor performs well for predicting laminar flame structure when coupling with multi-step reaction mechanism, it underestimates the effective thickening factor. In turbulent premixed flame simulation, the underestimated thickening factor leads to a faster local fuel consumption speed because of the over-predicted sub-grid flame wrinkling factor. A modified DTF sensor suitable for multi-step reaction mechanism is proposed. This sensor using the hyperbolic tangent function of progress variable to calculate thickening factor dynamically. It ensures that both the preheated and reaction zones are thickened effectively. The sub-grid wrinkling factor is hence estimated corresponding to the calculated flame thickness. Results of 1D laminar and 3D turbulent flame show that this method performs well for predicting both burned gas temperature and species concentration in burnt gas, which is important for predicting emissions.

合写作者:Guo SL, Wang JH, Wei XT, Yu SB, Zhang M, Huang ZH

卷号:2018, 233: 346-353

页面范围:346-353

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发表时间:2018-12-01

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