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首页> 外文期刊>Combustion Science and Technology >SENSITIVITY OF RADIATION MODELING TO PROPERTY ESTIMATION TECHNIQUES IN THE FREEBOARD OF LIGNITE-FIRED BUBBLING FLUIDIZED BED COMBUSTORS (BFBCs)
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SENSITIVITY OF RADIATION MODELING TO PROPERTY ESTIMATION TECHNIQUES IN THE FREEBOARD OF LIGNITE-FIRED BUBBLING FLUIDIZED BED COMBUSTORS (BFBCs)

机译:褐煤起泡流化床燃烧器(BFBC)的干板中辐射建模对属性估计技术的敏感性

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摘要

Predictive accuracy and computationally efficiency of method of lines (MOL) solution of the discrete ordinate method (DOM) coupled with different radiative property estimation techniques (GG, SLW, SNBCK) are assessed by applying them to the prediction of incident radiative fluxes along the freeboard walls of a 0.3 MWt atmospheric bubbling fluidized bed combustor (ABFBC) and comparing their predictions with measurements generated previously from two runs one without and the other with recycle. Freeboard is treated as a three-dimensional rectangular enclosure containing a grayon-gray, absorbing-emitting-isotropically scattering medium. Radiative properties of particles are evaluated by using geometric optics approximation. A comparative study is also provided between the source term distributions predicted by MOL solution of DOM with gray gas (GG), spectral line-based weighted sum of gray gases (SLW), and statistical narrow-band correlated-k (SNBCK) along the centerline of the freeboard for both runs. Comparisons reveal that the MOL solution of DOM with SLW model and geometric optics approximation provides accurate and computationally efficient solutions for wall fluxes and source term distributions in the freeboard of fluidized bed combustors containing particle laden combustion gases.
机译:通过将离散纵坐标法(DOM)的线法(MOL)解与不同的辐射特性估算技术(GG,SLW,SNBCK)相结合,将其应用于沿干舷的入射辐射通量的预测,可以评估其预测精度和计算效率0.3 MWt大气鼓泡流化床燃烧器(ABFBC)的壁,并将其预测结果与先前两次运行的结果进行比较,两次运行一次不进行循环,另一次进行循环。干舷被视为包含灰色/非灰色,吸收发射-各向同性散射介质的三维矩形外壳。通过使用几何光学近似来评估粒子的辐射特性。还提供了一个比较研究,该研究由DOM的灰分气体(GG)的MOL解预测的源项分布,灰分气体的基于谱线的加权总和(SLW)和沿其的统计窄带相关k(SNBCK)之间进行比较。两次运行的干舷中心线。比较表明,具有SLW模型和几何光学近似的DOM的MOL解决方案为含有颗粒燃烧气体的流化床燃烧室干舷中的壁通量和源项分布提供了精确且计算高效的解决方案。

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