Fire field modelling using the SMARTFIRE automated dynamic solution control environment

SMARTFIRE is a fire field model based on an open architecture integrated CFD code and knowledge-based system. It makes use of the expert system to assist the user in setting up the problem specification and new computational techniques such as Group Solvers to reduce the computational effort involved in solving the equations. This paper concentrates on recent research into the use of artificial intelligence techniques to assist in dynamic solution control of fire scenarios being simulated using fire field modelling techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxations using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate enhanced solution reliability due to obtaining acceptable convergence within each time step unlike some of the comparison simulations.

Magneto-hydro-dynamic analysis of an electrolysis cell for aluminum production

An electrolytic cell for Aluminum production contains molten metal subject to high currents and magnetic flux density. The interaction between these two fields creates electromagnetic forces within the liquid metal and can generate oscillations of the fluid similar to the waves at the free surface of oceans and rivers. The study of this phenomenon requires the simulation of the current density field, of the magnetic flux density field and the solution of the equations of motion of the liquid mass. An attempt to analyze the dynamical behavior of this problem is made by coupling different codes, based on different numerical techniques, in a single tool. The simulations are presented and discussed.

Impact of the vertical potshell deformation on the MHD cell stability behavior of a 500 kA aluminium electrolysis cell

In previous publications [1,2], it was rationalized that a large vertical potshell deformation may have a negative impact on the operations of very high amperage cells. The MHD-Valdis non-linear Magneto-Hydro-Dynamic model was therefore extended to take into account the displacement of the potshell. The MHD cell stability behavior of a 500 kA cell with a 17.3 meters long potshell was then studied.

In vitro-in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks

The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro-in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis model. The release of probucol to the aqueous (micellar) phase was monitored during the progress of lipolysis. These release profiles compared with plasma profiles obtained in a previous bioavailability study conducted in mini-pigs at the same conditions. The release rate and extent of release from the oil formulation were found to be significantly lower than from SMEDDS and SNEDDS. The rank order of probucol released (SMEDDS approximately SNEDDS > oil formulation) was similar to the rank order of bioavailability from the in vivo study. The employed neuro-fuzzy model (AFM-IVIVC) achieved significantly high prediction ability for different data formations (correlation greater than 0.91 and prediction error close to zero), without employing complex configurations. These preliminary results suggest that the dynamic lipolysis model combined with the AFM-IVIVC can be a useful tool in the prediction of the in vivo behavior of lipid-based formulations.