Use este identificador para citar ou linkar para este item: http://carpedien.ien.gov.br:8080/handle/ien/841
Tipo: article
Título: Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks
Autor(es): SALGADO, César Marques
PEREIRA, Cláudio Márcio do Nascimento Abreu
SCHIRRU, Roberto
BRANDÃO, Luis Eduardo Barreira
Resumo: This work presents a new methodology for flow regimes identification and volume fraction predictions in wateregaseoil multiphase systems. The approach is based on gamma-ray pulse height distributions (PHDs) pattern recognition by means the artificial neural networks (ANNs). The detection system uses appropriate fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors adequately positioned in order measure transmitted and scattered beams, which makes it less dependent on the regime flow. The PHDs are directly used by the ANNs without any parameterization of the measured signal. The system comprises four ANNs. The first identifies the flow regime and the other three ANNs are specialized in volume fraction predictions for each specific regime. The ideal and static theoretical models for annular, stratified and homogeneous regimes have been developed using MCNP-X mathematical code, which was used to provide training, test and validation data for the ANNs. The energy resolution of NaI(Tl) detectors is also considered on the mathematical model. The proposed ANNs could correctly identify all three different regimes with satisfactory prediction of the volume fraction in wateregaseoil multiphase system, demonstrating to be a promising approach for this purpose.
Abstract: This work presents a new methodology for flow regimes identification and volume fraction predictions in wateregaseoil multiphase systems. The approach is based on gamma-ray pulse height distributions (PHDs) pattern recognition by means the artificial neural networks (ANNs). The detection system uses appropriate fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors adequately positioned in order measure transmitted and scattered beams, which makes it less dependent on the regime flow. The PHDs are directly used by the ANNs without any parameterization of the measured signal. The system comprises four ANNs. The first identifies the flow regime and the other three ANNs are specialized in volume fraction predictions for each specific regime. The ideal and static theoretical models for annular, stratified and homogeneous regimes have been developed using MCNP-X mathematical code, which was used to provide training, test and validation data for the ANNs. The energy resolution of NaI(Tl) detectors is also considered on the mathematical model. The proposed ANNs could correctly identify all three different regimes with satisfactory prediction of the volume fraction in wateregaseoil multiphase system, demonstrating to be a promising approach for this purpose.
Palavras-chave: NaI detector
Monte Carlo simulation
Volume fraction
Artificial neural network
Gamma-ray
CNPq: Instrumentação para Medida e Controle de Radiação
Idioma: eng
País: Brasil
Editor: Insitituto de Engenharia Nuclear
Sigla da Instituição: IEN
Tipo de Acesso: openAccess
URI: http://hdl.handle.net/ien/841
Data do documento: 2010
Aparece nas coleções:Aplicação de técnicas nucleares na indústria, saúde e meio ambiente - Artigos de Periódicos

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
1-s2.0-S0149197010000375-main.pdfArtigo principal604,11 kBAdobe PDFVisualizar/Abrir


Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.

Ferramentas do administrador