  Flamelet Progress Variable (FPV) combustion models allow the evaluation of
all thermo chemical quantities in a reacting flow by computing only the mixture
fraction Z and a progress variable C. When using such a method to predict a
turbulent combustion in conjunction with a turbulence model, a probability
density function (PDF) is required to evaluate statistical averages (e.g.,
Favre average) of chemical quantities. The choice of the PDF is a compromise
between computational costs and accuracy level. The aim of this paper is to
investigate the influence of the PDF choice and its modeling aspects in the
simulation of non premixed turbulent combustion. Three different models are
considered: the standard one, based on the choice of a beta distribution for Z
and a Dirac distribution for C; a model employing a beta distribution for both
Z and C; a third model obtained using a beta distribution for Z and the
statistical most likely distribution (SMLD) for C. The standard model, although
widely used, doesn't take into account the interaction between turbulence and
chemical kinetics as well as the dependence of the progress variable not only
on its mean but also on its variance. The SMLD approach establishes a
systematic framework to incorporate informations from an arbitrary number of
moments, thus providing an improvement over conventionally employed presumed
PDF closure models. The rational behind the choice of the three PDFs is
described in some details and the prediction capability of the corresponding
models is tested versus well known test cases, namely, the Sandia flames, and a
test case for supersonic combustion provided by Cheng.
