Study of volume fraction in biphasic flow using nuclear technique

Autores

  • César Marques Salgado Instituto de Engenharia Nuclear
  • Luis Eduardo Barreira Brandão Instituto de Engenharia Nuclear

Palavras-chave:

salinity, volume fraction, MCNP-X code, artificial neural network, gamma-rays

Resumo

This report presents a methodology for volume
fractions prediction in water-gas stratified
flow regime using the nuclear technique and
artificial intelligence. The approach is based
on gamma-ray pulse height distributions pattern
recognition by means of the artificial neural
network. The detection system uses appropriate
narrow beam geometry, comprised of a (137Cs)
energy gamma-ray source and a NaI(Tl) detector
in order measure transmitted beam whose count
rates are influenced by the phases composition.
The static theoretical models for stratified
regime have been developed using MCNP-X code
in order to provide data for the network.

Referências

Salgado, C.M., Brandão, L.E.B., Nunes, R.C., Nascimento, A.C.H., Salgado, W.L., Study of solid-liquid flow regimes in mining industry using gamma radiation. INAC 2013, Brazil, Nov. 24-29, ISBN: 978-85-99141-05-2.

Capa Progress Report 2013-2014

Downloads

Publicado

2015-12-03

Como Citar

Salgado, C. M., & Brandão, L. E. B. (2015). Study of volume fraction in biphasic flow using nuclear technique. Instituto De Engenharia Nuclear: Progress Report, (2), 11. Recuperado de https://revistas.ien.gov.br/index.php/ienprogressreport/article/view/197

Edição

n. 2 (2015): IEN - Progress Report 2013-2014 n.2

Seção

Application of Nuclear Techniques in Health and Environment