Utilizing Artificial Neural Networks and Combined Capacitance-Based Sensors to Predict Void Fraction in Two-Phase Annular Fluids Regardless of Liquid Phase Type
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource, 12 Seiten
- Language
-
Englisch
- Bibliographic citation
-
In: IEEE access(11), S.143745-143756 - ISSN 2169-3536
In: Institute of Electrical and Electronics Engineers (IEEE), New York, NY
- Event
-
Veröffentlichung
- (where)
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Jena
- (who)
-
Friedrich-Schiller-Universität Jena
- (when)
-
2023
- Creator
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Al-Fayoumi, Mustafa A.
Al-Mimi, Hani Mahmoud
Veisi, Aryan
Al-Aqrabi, Hussain
Daoud, Mohammad Sh.
Eftekhari-Zadeh, Ehsan
- DOI
-
10.1109/ACCESS.2023.3340127
- URN
-
urn:nbn:de:101:1-2407170216197.866605156933
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:03 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Al-Fayoumi, Mustafa A.
- Al-Mimi, Hani Mahmoud
- Veisi, Aryan
- Al-Aqrabi, Hussain
- Daoud, Mohammad Sh.
- Eftekhari-Zadeh, Ehsan
- Friedrich-Schiller-Universität Jena
Time of origin
- 2023
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