We have developed a surrogate blending methodology to identify surrogates with a desired degree of complexity. Along with estimation methods for various physical and chemical properties for fuel blends, we have assembled and developed a rich library of over 60 fuel components. The components cover a carbon number range from 1 to 20, and chemical classes including linear and branched alkanes, olefins, aromatics with one and two rings, alcohols, esters, and ethers. With these, surrogates can be formulated to represent most gasoline, diesel, gaseous fuels, renewable fuels, and several additives. As part of the library, we have assembled self-consistent and detailed reaction mechanisms for all the components, as well as for emissions including NOx and polycyclic aromatic hydrocarbons and a detailed soot-surface mechanism. An extensive validation suite has been used to improve the kinetics database such that good predictions and agreement to data are achieved for the fuel components and fuel-component blends, within experimental uncertainties. This effectively eliminates the need to tune specific rate parameters when employing the kinetics mechanisms in combustion simulations. For engine simulations, the master mechanisms have been reduced using a combination of available reduction methods while strictly controlling the error tolerances for targeted predictions. This approach has resulted in small mechanisms for efficiently incorporating the validated kinetics into computational fluid dynamics (CFD) applications. The surrogate formulation methodology, the comprehensive fuel library, and mechanism reduction strategies suggested in this work allow the use of CFD to explore design concepts and fuel effects in engines with reliable predictions.

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