Surrogates of transportation fuels are useful design tools for developing engines with cleaner and more e cient combustion. It is important that information about the sooting tendency of the target fuel be incorporated in the formulation of these surrogates. Toward that end, in this work we report experimentally measured sooting tendencies of several diesel and jet fuels, surrogates of these fuels, and pure compounds which are components of these surrogates. ese fuels, surrogates, and compounds were separately doped in trace quantities (. by mass) into the fuel of a methane-air laminar co ow ame. e sooting tendencies, expressed as Yield Sooting Indices (YSIs), were quanti ed from spatially resolved two-dimensional soot volume fraction distributions of these ames as measured with color ratio pyrometry. Previous means of matching the sooting behavior of the surrogate to the fuel, such as by matching the fuel's hydrogen content or its double bond equivalent, were found to be inadequate because these approaches don't fully account for the e ects on the fuel's sooting tendency of branching in aliphatics and alkyl substitutions in aromatics. A strong correlation was observed between the YSI and the relative abundance of di erent types of carbon atoms (as quanti ed by H and C NMR spectroscopy) in these fuels and compounds, indicating that such NMR characterization can capture essential structure-speci c sooting information of relevance to surrogate formulation. Based on these YSI measurements, we demonstrate two approaches to predict the sooting tendency of a fuel surrogate. ese approaches, based on () the choice of pure compounds that are blended to create a surrogate, or () the distribution of carbon atom types in a surrogate, can be used to tune the surrogate's formulation to match the sooting tendency of its target fuel.

Soot from internal combustion engines negatively affects health and climate. Soot emissions might be reduced through the expanded usage of appropriate biomass-derived fuels. Databases of sooting indices, based on measuring some aspect of sooting behavior in a standardized combustion environment, are useful in providing information on the comparative sooting tendencies of different fuels or pure compounds. However, newer biofuels have varied chemical structures including both aromatic and oxygenated functional groups, making an accurate measurement or prediction of their sooting tendency difficult. In this work, we propose a unified sooting tendency database for pure compounds, including both regular and oxygenated hydrocarbons, which is based on combining two disparate databases of yield-based sooting tendency measurements in the literature. Unification of the different databases was made possible by leveraging the greater dynamic range of the color ratio pyrometry soot diagnostic. This unified database contains a substantial number of pure compounds (≥ 400 total) from multiple categories of hydrocarbons important in modern fuels and establishes the sooting tendencies of aromatic and oxygenated hydrocarbons on the same numeric scale for the first time. Using this unified sooting tendency database, we have developed a predictive model for sooting behavior applicable to a broad range of hydrocarbons and oxygenated hydrocarbons. The model decomposes each compound into single-carbon fragments and assigns a sooting tendency contribution to each fragment based on regression against the unified database. The model's predictive accuracy (as demonstrated by leave-one-out cross-validation) is comparable to a previously developed, more detailed predictive model. The fitted model provides insight into the effects of chemical structure on soot formation, and cases where its predictions fail reveal the presence of more complicated kinetic sooting mechanisms. This work will therefore enable the rational design of low-sooting fuel blends from a wide range of feedstocks and chemical functionalities.

Biodiesel fuels form an important constituent of the renewable energy resources landscape. These fuels are produced through transesterification of fatty acids with methanol or ethanol to yield long-chain methyl or ethyl esters of 16-18 carbon atoms, frequently with one or more C− −C double bonds. The C− −C double bonds are expected to play a major role in the combustion chemistry of these biodiesel fuels, especially in the area of soot emission. To better understand the effect of these double bonds on the sooting properties of esters, we have measured the sooting tendencies of twenty C 4 to C 7 unsaturated esters and report their Yield Sooting Indices (YSI). The C− −C double bond was found to have a significant effect on the sooting tendency. In most cases, the unsaturated ester was more sooting than its saturated counterpart, although the increase in sooting tendency was strongly influenced by other structural features of the compound. Esters with the C− −C double bond nearest the carbonyl group had lower increases in the sooting tendency from the parent saturated ester than compounds where the C− −C double bond was further along the carbon backbone. Unsaturated methyl esters had larger increases in YSI (compared to their parent saturated ester) than unsaturated ethyl or propyl esters. The strong dependence of the sooting tendencies of these esters on their chemical structure indicates the complexity of the chemical processes involved, and is an important area for further studies of these compounds.burden of disease and injury attributable to various risk factors concluded that 5 ambient particulate matter pollution and household air pollution from solid 6 fuels were two of the most important risk factors in reducing global human 7 life expectancy [4]. Soot forms an important portion of this particulate matter 8 and has also been implicated as the second most important individual climate-9 warming agent after carbon dioxide [5]. In this context, increasingly stringent 10 soot emission regulations coupled with ever-increasing usage of hydrocarbon fuels 11 for our various energy needs makes the study of soot in combustion particularly 12 important. 13Soot emission from combustion processes is strongly dependent on the struc-14 ture of the fuel being burned [6][7][8][9]. A common way to quantify the differences in 15 these emissions in a laboratory setting is through the use of sooting tendencies, 16 measured for individual fuel or mixtures of fuels. For diffusion flames, sooting 17 tendency has traditionally been characterized by a flame smoke height, and often 18 converted into apparatus-independent measures such as threshold soot index 19 (TSI) [10], or oxygen-extended sooting index (OESI) [11]. 20Our group has developed a new method of characterizing sooting tendency 21 that does not involve the smoke height, but directly measures the maximum soot 22 volume fraction f v,max in a flame [12][13][14]. In this method, a small amount of the 23 test compound is added to a lightly sooting base flame, and the result...

The Abel transform is a mathematical operation that transforms a cylindrically symmetric three-dimensional (3D) object into its two-dimensional (2D) projection. The inverse Abel transform reconstructs the 3D object from the 2D projection. Abel transforms have wide application across numerous fields of science, especially chemical physics, astronomy, and the study of laser-plasma plumes. Consequently, many numerical methods for the Abel transform have been developed, which makes it challenging to select the ideal method for a specific application. In this work eight transform methods have been incorporated into a single, open-source Python software package (PyAbel) to provide a direct comparison of the capabilities, advantages, and relative computational efficiency of each transform method. Most of the tested methods provide similar, high-quality results. However, the computational efficiency varies across several orders of magnitude. By optimizing the algorithms, we find that some transform methods are sufficiently fast to transform 1-megapixel images at more than 100 frames per second on a desktop personal computer. In addition, we demonstrate the transform of gigapixel images. a) danhickstein@gmail.com, Questions and comments regarding PyAbel may be posted to: http://github.com/PyAbel/PyAbel

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