Optimization of a flow regime identification system and prediction of volume fractions in three-phase systems using gamma-rays and artificial neural network.

Salgado, W L; Dam, R S F; Salgado, C M

Salgado, W L; Dam, R S F; Salgado, C M.

Afiliación

Salgado WL; Instituto de Engenharia Nuclear, Divisão de Radiofármacos (DIRA/IEN/CNEN), P.O. Box 68550, Rio de Janeiro, RJ, 21941-906, Brazil; Universidade Federal do Rio de Janeiro, Programa de Engenharia Nuclear (PEN/COPPE), P.O. Box 68509, Rio de Janeiro, RJ, 21941-914, Brazil. Electronic address: william.otero@coppe.ufrj.br.
Dam RSF; Instituto de Engenharia Nuclear, Divisão de Radiofármacos (DIRA/IEN/CNEN), P.O. Box 68550, Rio de Janeiro, RJ, 21941-906, Brazil; Universidade Federal do Rio de Janeiro, Programa de Engenharia Nuclear (PEN/COPPE), P.O. Box 68509, Rio de Janeiro, RJ, 21941-914, Brazil. Electronic address: rdam@coppe.ufrj.br.
Salgado CM; Instituto de Engenharia Nuclear, Divisão de Radiofármacos (DIRA/IEN/CNEN), P.O. Box 68550, Rio de Janeiro, RJ, 21941-906, Brazil. Electronic address: otero@ien.gov.br.

Appl Radiat Isot ; 169: 109552, 2021 Mar.

Article en En | MEDLINE | ID: mdl-33434775

Palabras clave

Artificial neural network; Flow regime; Gamma-rays densitometry; MCNP6 code; Three-phase flow; Volume fraction

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Appl Radiat Isot Asunto de la revista: MEDICINA NUCLEAR / SAUDE AMBIENTAL Año: 2021 Tipo del documento: Article

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