RESUMO
This work presents methodology based on nuclear technique and artificial neural network for volume fraction predictions in annular, stratified and homogeneous oil-water-gas regimes. Using principles of gamma-ray absorption and scattering together with an appropriate geometry, comprised of three detectors and a dual-energy gamma-ray source, it was possible to obtain data, which could be adequately correlated to the volume fractions of each phase by means of neural network. The MCNP-X code was used in order to provide the training data for the network.
RESUMO
The report presented in the 7th ONU's Conference (USA, 2001) about climatic changes that took place at the end of 2001 informs that, in less than 50 years, more than 45% of the world population will be suffering from lack of water. This fact occurs by the absence of management on water resources, mainly, in agriculture. As the excess as the lack of humidity in soil can change the harvest quality, causing physiologic anomalies in food and promoting soil diseases incidence caused by bacteria and fungus. In order to establish a larger control in the food quality, a study has been performed, through the neutron radiographic technique, that proposes the optimization of agricultural harvests in relation to the minimum quantity of water necessary for the plant to develop and, also, of the soil compactness. Thus, neutron radiographic images of the system root-soil can be produced so that each root will be evaluated for its ability to penetrate in the soil layers, having the advantage of not interfering in this system what it is not possible through the usual techniques yet. The initial tests using bean roots showed that the soil thickness, which involved the roots, resulted in low contrast images, what impeded their visualization with enough clearness so that their grow could not be observed. For this reason, it was opted to the gadolinium as a contrast agent so that we have been studying its transport through the roots.