کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6630225 1424931 2018 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Numerical simulation of premixed combustion using the modified dynamic thickened flame model coupled with multi-step reaction mechanism
ترجمه فارسی عنوان
شبیه سازی عددی از احتراق پیش مخلوط با استفاده از مدل شعله ی ضعیف شده دینامیکی اصلاح شده همراه با مکانیزم واکنش چند مرحله ای
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuel - Volume 233, 1 December 2018, Pages 346-353
نویسندگان
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