| Scope |
The past decade has witnessed a surge in new characterization techniques that provide profound insights into materials behaviors across various scales. This symposium invites presentations that leverage these new characterization methods to investigate structure, properties, and performance of materials, particularly in harsh environments such as those encountered in nuclear energy systems. Key areas of focus include machine learning assisted characterization, real-time in situ measurements, and miniature-scale studies. These areas are crucial for tackling the specific challenges posed by irradiation field, extreme temperatures, and aggressive chemical environments. Characterizing these dynamics is essential for improving the understanding of materials performance under both routine operations and unforeseen accidents. The data generated by these new characterization tools are more than just descriptive metrics, they serve as a transformative bridge for refining and validating both physics-based and machine learning models, thereby enhancing their prediction accuracy. At the forefront of integrating these new techniques into materials research, this symposium welcomes the research presentations that advance our understanding and accelerate innovations in this field.
Specific topics include, but are not limited to:
o Data-driven methods in materials characterization.
o Non-destructive characterization techniques.
o Non-contact thermal and elastic measurement techniques.
o In-situ testing under irradiation, mechanical, and corrosion condition.
o Methods for monitoring corrosive attack in coolant environments.
o Materials behaviors in combined extreme conditions.
o Methods enabling enhanced coupling of experimental and modeling. |