Flow behavior inside the mold cavity of liquid molding processes such as resin transfer molding (RTM) is important information that is necessary to determine filling time and void formation. Most of the studies found in the literature use isothermal models with Newtonian fluids and constant viscosities. However, for some specific applications, the mold filling time dependence on temperature and the viscosity dependence on time and temperature must be considered to precisely predict the flow advance inside the mold. In this study, a viscosity model, that accounts for temperature and time dependence is coupled with a standard computational fluid dynamics (CFD) model to simulate the resin advance inside an RTM mold cavity. The model is simpler than similar methods that describe viscosity as a function of temperature and resin conversion. Nevertheless, the results show that the proposed model is capable of calculating flow advance, air and resin temperatures, and viscosity changes with time and temperature as expected in actual RTM and correlated processing of thick parts or with low injection pressure or high fiber content.

The mam objective of this study is to present an alternative methodology based on the Probability Neural Network (PNN) fonnalism to estimate and further predict the penneability values in any location of a 3D grid of a reservoir model. An experimental data set containing measurements of log porosity, true vertical depth, transitive time and lithologies at well locations, was used for training the PNN. Afterwards, the trained PNN is applied to the wells where does not exist core infonnation, but logs. The penneability values estimated by PNN in the wells are used to simulate the penneability at all spatial locations of the reservoir. This methodology was applied to experimental data from a reservoir located at West Africa with encouraging results.

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