| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
2025 Technical Division Student Poster Contest
|
| Presentation Title |
SPG-95: AI-Driven Multiscale Computational Framework for Corrosion-Induced Degradation of High Temperature Alloys |
| Author(s) |
Praneeth Bachu, Radhakrishnan Balasubramaniam, Tracie Lowe, Celine Hin, Rishi Pillai |
| On-Site Speaker (Planned) |
Praneeth Bachu |
| Abstract Scope |
Environmental degradation is a critical life-limiting mechanism and high-fidelity, efficient computational methods to predict material degradation and lifetimes are essential to accelerate materials selection and design for power generation and transportation technologies. There is a lack of a unified physics-based framework with the ability to predict material performance as a function time, temperature, alloy composition and environment. The aim of this work is to develop a high fidelity physics-based framework that integrates data analytics, coupled thermodynamic-kinetic modeling methods and machine learning to quantitatively predict the impact of environmental degradation on the performance and lifetime of high temperature alloys. The applicability of the framework will be demonstrated by simulating the oxidation of thin foils (~100 μm) of chromia-forming Ni-base alloys in water vapor containing environments at 600-800°C. The modeling results will be validated with experimental results of molten salt exposure, metal loss, oxidation-induced compositional changes and phase transformations in the alloys. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Modeling and Simulation, Computational Materials Science & Engineering, High-Temperature Materials |