About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
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Understanding and Mitigating High Temperature Corrosion Processes Through Synergistic Integration of Experimental, Computational and Manufacturing Techniques
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| Presentation Title |
AI-Driven Multiscale Computational Framework for Corrosion-Induced Degradation of High Temperature Alloys |
| Author(s) |
Praneeth Bachu, Marie Romedenne, Severine Cambier, 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. Ongoing work 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 will be presented. The applicability of the framework will be demonstrated with two case studies: 1) Advanced data analytics to predict the parabolic oxidation rates of of chromia- and alumina forming Fe-, Co- and Ni-base alloys and 2) Predicting the corrosion induced degradation of Fe- and Ni-based alloys in molten halide salts. |