Modelling of blended Diesel and biodiesel fuel droplet heating and evaporation
Modelling of blended Diesel and biodiesel fuel droplet heating and evaporation
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DOI：
10.1016/j.fuel.2016.09.060
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发表时间：
2017
期刊：
影响因子：
7.4
通讯作者：
Mansour Al Qubeissi;S. Sazhin;A. Elwardany
中科院分区：
文献类型：
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作者：
Mansour Al Qubeissi;S. Sazhin;A. Elwardany
本文提出了一种双燃料液滴加热和蒸发建模的新方法，具体应用于生物柴油（以广泛使用的大豆甲酯，SME 为代表）和柴油燃料在内燃机代表性条件下的混合物。使用最近引入的多维准离散 (MDQD) 模型，原始成分（含有多达 105 种柴油和生物柴油燃料成分）被替换为较少数量的成分和准成分。考虑了这些组分和准组分在液相中的瞬时扩散以及温度梯度和液滴内的再循环。将结果与当混合生物柴油/柴油燃料液滴由纯生物柴油燃料或纯柴油燃料液滴代表时的情况的预测进行比较。结果表明，代表纯生物柴油燃料的 100% SME 预测的液滴蒸发时间和表面温度与纯柴油燃料的预测接近。此外，结果表明，在相同的发动机条件下，使用 MDQD 模型对 B5（5% SME 和 95% 柴油）和 B50（50% SME 和 50% 柴油）双燃料的实际成分进行 17 个准组分/组分的近似，导致液滴寿命的预测分别高达 9% 和 4%。与使用分立元件模型考虑所有 105 个元件的情况相比，后一个模型的应用导致 CPU 时间减少了 83% 以上。
The paper presents a new approach to the modelling of heating and evaporation of dual-fuel droplets with a specific application to blends of biodiesel (represented by the widely used soybean methyl ester, SME) and Diesel fuels in conditions representative of internal combustion engines. The original compositions, with up to 105 components of Diesel and biodiesel fuels, are replaced with a smaller number of components and quasi-components using the recently introduced multi-dimensional quasi-discrete (MDQD) model. Transient diffusion of these components and quasi-components in the liquid phase and temperature gradient and recirculation inside droplets are taken into account. The results are compared with the predictions of the case when blended biodiesel/Diesel fuel droplets are represented by pure biodiesel fuel or pure Diesel fuel droplets. It is shown that droplet evaporation time and surface temperature predicted for 100% SME, representing pure biodiesel fuel, are close to those predicted for pure Diesel fuel. Also, it is shown that the approximations of the actual compositions of B5 (5% SME and 95% Diesel) and B50 (50% SME and 50% Diesel) dual-fuels by 17 quasi-components/components, using the MDQD model, lead to under-predictions in droplet lifetimes by up to 9% and 4%, respectively, under the same engine conditions. The application of the latter model has resulted in above 83% reduction in CPU time compared to the case when all 105 components are taken into account using the discrete component model.