High temperature oxidation of corrosion resistant alloys from machine learning

Taylor, Christopher D.; Tossey, Brett M.

doi:10.1038/s41529-021-00184-3

Title: High temperature oxidation of corrosion resistant alloys from machine learning

Journal Article · 15 July 2021 · npj Materials Degradation

DOI: https://doi.org/10.1038/s41529-021-00184-3 · OSTI ID:1808215

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Abstract Parabolic rate constants, k _p , were collected from published reports and calculated from corrosion product data (sample mass gain or corrosion product thickness) and tabulated for 75 alloys exposed to temperatures between ~800 and 2000 K (~500–1700 ^o C; 900–3000 ^o F). Data were collected for environments including lab air, ambient and supercritical carbon dioxide, supercritical water, and steam. Materials studied include low- and high-Cr ferritic and austenitic steels, nickel superalloys, and aluminide materials. A combination of Arrhenius analysis, simple linear regression, supervised and unsupervised machine learning methods were used to investigate the relations between composition and oxidation kinetics. The supervised machine learning techniques produced the lowest mean standard errors. The most significant elements controlling oxidation kinetics were Ni, Cr, Al, and Fe, with Mo and Co composition also found to be significant features. The activation energies produced from the machine learning analysis were in the correct distributions for the diffusion constants for the oxide scales expected to dominate in each class.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: FE0031631

OSTI ID:: 1808215

Journal Information:: npj Materials Degradation, Journal Name: npj Materials Degradation Journal Issue: 1 Vol. 5; ISSN 2397-2106

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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