Study of salinity independent volume fraction in multiphase flow using artificial neural networks

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 investigates the response in material volume fraction (MVF) prediction system for water-gas-oil multiphase flows considering variations up to 16% in salinity of water. The approach is based on pulse height distributions (PHD) pattern recognition by means of artificial neural network (ANN) [1]. Theoretical models for annular and stratified flow regimes have been developed using MCNP-X code to provide data for the network.

Referências

Salgado, C.M., Brandão, L.E.B., Pereira, C.M.N.A., Salgado, W. L. Salinity independent volume fraction prediction in annular and stratified (water-gas-oil) multiphase flows using artificial neural networks. Progress in Nuclear Energy, 76 (2014) 17-23.

Johansen G. A. and Jackson, P. Salinity independent measurement of gas volume fraction in oil/gas/water pipe flows. Applied Radiation and Isotopes, 53, (2000) 595-601

Capa Progress Report 2013-2014

Downloads

Publicado

2015-12-03

Como Citar

Salgado, C. M., & Brandão, L. E. B. (2015). Study of salinity independent volume fraction in multiphase flow using artificial neural networks. Instituto De Engenharia Nuclear: Progress Report, (2), 10. Recuperado de https://revistas.ien.gov.br/index.php/ienprogressreport/article/view/195

Edição

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

Seção

Application of Nuclear Techniques in Health and Environment