通过增强的多变量优化框架制定的FACE汽油替代品
FACE gasoline surrogates formulated by an enhanced multivariate optimization framework
摘要：
高效低排放内燃机的设计和优化高度依赖于燃料化学。解决复杂燃料（如汽油）的化学问题是一项挑战。一个解决方案是研究一种燃料替代品：一种由少量特征良好的碳氢化合物混合而成的混合物，通过模拟它们的热物理和化学动力学特性来代表真正的燃料。在目前的研究中，现有的汽油替代配方算法进一步增强，将新的化学计量学模型。这些模型使用碳氢化合物燃料的红外光谱来预测辛烷值，并且对广泛的纯碳氢化合物和此类混合物有效。这项工作利用14种碳氢化合物来形成高级燃烧发动机（FACE）汽油燃料的定制替代调色板，包括以前未考虑的候选组分物种：正戊烷，2-甲基戊烷，1-戊烯，环己烷和邻二甲苯。我们评估的性能“充分”和“减少”代用品的10种燃料的先进燃烧发动机（FACE）汽油，包含8-12和4-7个组件，分别。这些替代物与实际燃料的目标特性平均在5%以内匹配。这种紧密的一致性表明，该算法可以设计与广泛的目标性质相匹配的替代物：辛烷值，密度，氢碳比，蒸馏特性和碳-碳键类型的比例。我们还将我们的替代品与文献中的替代品（FACE汽油A、C、F、G、I和J）进行了比较。总的来说，这里展示的方法提供了一个很有前途的方法，更好地设计具有广泛属性的汽油类燃料的替代品。
英文摘要：
Design and optimization of higher efficiency, lower-emission internal combustion engines are highly dependent on fuel chemistry. Resolving chemistry for complex fuels, like gasoline, is challenging. A solution is to study a fuel surrogate: a blend of a small number of well-characterized hydrocarbons to represent real fuels by emulating their thermophysical and chemical kinetics properties. In the current study, an existing gasoline surrogate formulation algorithm is further enhanced by incorporating novel chemometric models. These models use infrared spectra of hydrocarbon fuels to predict octane numbers, and are valid for a wide array of neat hydrocarbons and mixtures of such. This work leverages 14 hydrocarbon species to form tailored surrogate palettes for the Fuels for Advanced Combustion Engine (FACE) gasolines, including candidate component species not previously considered: n-pentane, 2-methylpentane, 1-pentene, cyclohexane, and o-xylene. We evaluate the performance of "full" and "reduced" surrogates for the 10 fuels for advanced combustion engine (FACE) gasolines, containing between 8-12 and 4-7 components, respectively. These surrogates match the target properties of the real fuels, on average, within 5 %. This close agreement demonstrates that the algorithm can design surrogates matching the wide array of target properties: octane numbers, density, hydrogen-to-carbon ratio, distillation characteristics, and proportions of carbon-carbon bond types. We also compare our surrogates to those available in literature (FACE gasolines A, C, F, G, I and J). Overall, the approach demonstrated here offers a promising method to better design surrogates for gasoline-like fuels with a wide array of properties.
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