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The concept of optimal estimators, recently introduced by Moreau et al. [Phys. Fluids 18, 1 (2006)] is used as an a priori tool to discuss the accuracy of subfilter models. Placed in the framework of large-eddy simulation of combustion problems, this work focuses on the subfilter models used to evaluate the subfilter variance of a conserved scalar, the mixture fraction. The a priori tests are performed using 5123 direct numerical simulation data of forced homogeneous isotropic turbulence. First, the performance of the most commonly used models for the subfilter variance is studied. Using optimal estimators, the Smagorinsky-type model [Pierce and Moin, Phys. Fluids 10, 3041 (1998)] is shown to have the best set of parameters. However, the conventional dynamic formulation of the model leads to large errors in the variance prediction. It was found that assumptions used in the model formulation are not verified. A new dynamic procedure based on a Taylor series expansion is then proposed to improve the predictive accuracy. The a priori tests show that the new model substantially improves predictive accuracy.

The increasing demand for cleaner combustion and reduced greenhouse gas emissions motivates research on the combustion of hydrocarbon fuels and their surrogates. Accurate detailed chemical kinetic models are an important prerequisite for high fidelity reacting flow simulations capable of improving combustor design and operation. The development of such models for many

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The generation of nanostructured particles in high-temperature flames is important both for the control of emissions from combustion devices and for the synthesis of high-value chemicals for a variety of applications. The physiochemical processes that lead to the production of fine particles in turbulent flames are highly sensitive to the flow physics and, in particular, the history of thermochemical compositions and turbulent features they encounter. Consequently, it is possible to change the characteristic size, structure, composition, and yield of the fine particles by altering the flow configuration. This review describes the complex multiscale interactions among turbulent fluid flow, gas-phase chemical reactions, and solid-phase particle evolution. The focus is on modeling the generation of soot particles, an unwanted pollutant from automobile and aircraft engines, as well as metal oxides, a class of high-value chemicals sought for specialized applications, including emissions control. Issues arising due to the numerical methods used to approximate the particle number density function, the modeling of turbulence-chemistry interactions, and model validation are also discussed. 159

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