Causal discovery to understand hot corrosion
| dc.contributor.author | Varghese, Akhil | |
| dc.contributor.author | Arana-Catania, Miguel | |
| dc.contributor.author | Mori, Stefano | |
| dc.contributor.author | Encinas-Oropesa, Adriana | |
| dc.contributor.author | Sumner, Joy | |
| dc.date.accessioned | 2024-02-14T11:38:11Z | |
| dc.date.available | 2024-02-14T11:38:11Z | |
| dc.date.freetoread | 2024-02-14 | |
| dc.date.issued | 2024-12 | |
| dc.date.pubOnline | 2024-02-12 | |
| dc.description.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. | en_UK |
| dc.format.extent | 1639-1651 | |
| dc.identifier.citation | Varghese A, Arana-Catania M, Mori S, et al., (2024) Causal discovery to understand hot corrosion, Materials and Corrosion. Volume 75, Issue 12, December 2024, pp. 1639-1651 | en_UK |
| dc.identifier.eissn | 1521-4176 | |
| dc.identifier.issn | 0947-5117 | |
| dc.identifier.issueNo | 12 | |
| dc.identifier.uri | https://doi.org/10.1002/maco.202314240 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/20794 | |
| dc.identifier.volumeNo | 75 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Wiley | en_UK |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | causal discovery method | en_UK |
| dc.subject | causal inference | en_UK |
| dc.subject | FCI algorithm | en_UK |
| dc.subject | gas turbine superalloys | en_UK |
| dc.subject | hot corrosion | en_UK |
| dc.subject | Kernel-based conditional independence test | en_UK |
| dc.title | Causal discovery to understand hot corrosion | en_UK |
| dc.type | Article | en_UK |
| dcterms.dateAccepted | 2024-01-12 |