Large-eddy simulation of combustion problems involves highly nonlinear terms that, when filtered,
result in a contribution from subgrid fluctuations of scalars, Z, to the dynamics of the filtered value.
This subgrid contribution requires modeling. Reconstruction models try to recover as much
information as possible from the resolved field Z
¯, based on a deconvolution procedure to obtain an
intermediate field ZM . The approximate reconstruction using moments ~ARM! method combines
approximate reconstruction, a purely mathematical procedure, with additional physics-based
information required to match specific scalar moments, in the simplest case, the Reynolds-averaged
value of the subgrid variance. Here, results from the analysis of the ARM model in the case of a
spatially evolving turbulent plane jet are presented. A priori and a posteriori evaluations using data
from direct numerical simulation are carried out. The nonlinearities considered are representative of
reacting flows: power functions, the dependence of the density on the mixture fraction ~relevant for
conserved scalar approaches! and the Arrhenius nonlinearity ~very localized in Z space!.
Comparisons are made against the more popular beta probability density function ~PDF! approach
in the a priori analysis, trying to define ranges of validity for each approach. The results show that
the ARM model is able to capture the subgrid part of the variance accurately over a wide range of
filter sizes and performs well for the different nonlinearities, giving uniformly better predictions
than the beta PDF for the polynomial case. In the case of the density and Arrhenius nonlinearities,
the relative performance of the ARM and traditional PDF approaches depends on the size of the
subgrid variance with respect to a characteristic scale of each function. Furthermore, the sources of
error associated with the ARM method are considered and analytical bounds on that error are
obtained
