Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Pillai, Rishi; Romedenne, Marie; Peng, Jian; Pint, Bruce; Haynes, James A.; Muralidharan, Govindarajan; Shin, Dongwon

doi:10.1007/s11085-021-10076-1

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Journal Article · Fri Aug 06 00:00:00 EDT 2021 · Oxidation of Metals

DOI:https://doi.org/10.1007/s11085-021-10076-1· OSTI ID:1813193

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science & Technology Division

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1813193

Journal Information:: Oxidation of Metals, Journal Name: Oxidation of Metals Vol. 97; ISSN 0030-770X

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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