Causal discovery to understand hot corrosion
Abstract: Gas turbine superalloys experience hot corrosion, driven by factors including corrosive deposit flux, temperature, gas composition, and component material. The full mechanism still needs clarification and research often focuses on laboratory work. As such, there is interest in causal discovery to confirm the significance of factors and identify potential missing causal relationships or codependencies between these factors. The causal discovery algorithm fast causal inference (FCI) has been trialled on a small set of laboratory data, with the outputs evaluated for their significance to corrosion propagation, and compared to existing mechanistic understanding. FCI identified salt deposition flux as the most influential corrosion variable for this limited data set. However, HCl was the second most influential for pitting regions, compared to temperature for more uniformly corroding regions. Thus, FCI generated causal links aligned with literature from a randomised corrosion data set, while also identifying the presence of two different degradation modes in operation.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Causal discovery to understand hot corrosion ; day:12 ; month:02 ; year:2024 ; extent:13
Materials and corrosion ; (12.02.2024) (gesamt 13)
- Creator
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Varghese, Akhil
Arana‐Catania, Miguel
Mori, Stefano
Encinas‐Oropesa, Adriana
Sumner, Joy
- DOI
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10.1002/maco.202314240
- URN
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urn:nbn:de:101:1-2024021314195237412546
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:40 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Varghese, Akhil
- Arana‐Catania, Miguel
- Mori, Stefano
- Encinas‐Oropesa, Adriana
- Sumner, Joy
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