Flow regime and volume fraction identification using nuclear techniques, artificial neural networks and computational fluid dynamics-Reference-Cited by-同舟云学术

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Flow regime and volume fraction identification using nuclear techniques, artificial neural networks and computational fluid dynamics

Published:2020-05 Issue: Volume:159 Page:109103
ISSN:0969-8043
Container-title:Applied Radiation and Isotopes
language:en
Short-container-title:Applied Radiation and Isotopes

Author:

Affonso Renato R.W.,Dam Roos S.F.,Salgado William L.,Silva Ademir X. da,Salgado César M.

Funder

Universidade Federal do Rio de Janeiro

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Radiation

Reference25 articles.

1. Improved void fraction determination by means of multibeam gamma-ray attenuation measurements;Åbro;Flow Meas. Instrum.,1999

2. Comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings;Baillie;Mech. Syst. Signal Process.,1996

3. Investigation of the exploitation of a fast-sampling single gamma densitometer and pattern recognition to resolve the superficial phase velocities and liquid phase water cut of vertically upward multiphase flows;Blaney;Flow Meas. Instrum.,2008

4. Accuracy aspects in multiphase floe metering using X-ray transmission;Bom;IEEE Trans. Nucl. Sci.,2001

5. Back-propagation Theory, Architectures and Applications;Chauvin,1995

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