RESUMEN
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.
RESUMEN
The Monte Carlo method was used to calculate the efficiency, escape and Compton curves of a planar high-purity germanium detector (HPGe) in the 20-150 keV energy. These curves were used for the determination of photons spectra produced by an X-ray machine in order to allow a precise characterization of photon beams applied to medical diagnosis. The detector was modeled with the MCNP5 computer code and validated by comparison with experimental data. The air kerma calculated after the spectra stripping was compared with ionization chamber measurements.