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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.07804 |
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| _version_ | 1866913231704948736 |
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| author | Varghese, A. Arana-Catania, M. Mori, S. Encinas-Oropesa, A. Sumner, J. |
| author_facet | Varghese, A. Arana-Catania, M. Mori, S. Encinas-Oropesa, A. Sumner, J. |
| contents | 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 co-dependencies 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 the salt deposition flux as the most influential corrosion variable for this limited dataset. 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 dataset, while also identifying the presence of two different degradation modes in operation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07804 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Discovery to Understand Hot Corrosion Varghese, A. Arana-Catania, M. Mori, S. Encinas-Oropesa, A. Sumner, J. Materials Science 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 co-dependencies 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 the salt deposition flux as the most influential corrosion variable for this limited dataset. 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 dataset, while also identifying the presence of two different degradation modes in operation. |
| title | Causal Discovery to Understand Hot Corrosion |
| topic | Materials Science |
| url | https://arxiv.org/abs/2402.07804 |