Rate Theory Model of Irradiation-Induced Solute Clustering in b.c.c. Fe-Based Alloys

• Published in: JOM (1989) Vol. 72; no. 11; pp. 4017 - 4027
• Main Authors: Swenson, Matthew J., Wharry, Janelle P.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2020; Springer Nature B.V
• Subjects: Algorithms; Alloys; Atoms & subatomic particles; Charged particles; Chemistry/Food Science; Clustering; Earth Sciences; Efficiency; Engineering; Environment; Evolution; Experiments; Ferrous alloys; Machine learning; Morphology; Nanoclusters; Nanostructured Materials under Extreme Environments; Nuclear reactors; Physics; Point defects; Radiation; Radiation dosage; Rate theory; Sensitivity analysis; Tomography; Vacancies; Variables
• ISSN: 1047-4838; 1543-1851
• DOI: 10.1007/s11837-020-04365-4

Solute nanoclusters are critical to the structural and mechanical integrity of numerous alloys based on the b.c.c. Fe matrix, which have risen to prominence as candidates for advanced nuclear reactor applications. Because irradiation can profoundly alter the morphology and composition of these solute nanoclusters, it is critical to understand and predict solute clustering behavior in the presence of irradiation. In this study, we advance a simple theory to model irradiation-induced nanocluster evolution subject to different irradiating particles. The model is trained and validated with experimental data following an approach similar to training a machine learning algorithm, resulting in an agile model that can be used for rapid screening of new alloys. Using the model, nanocluster evolution is found to depend upon the disordering parameter (i.e., cluster morphology and dose rate) and irradiation temperature, and is most sensitive to the solute migration, vacancy formation, and vacancy migration energies. Results are discussed with respect to the irradiation temperature shift for varying irradiating particle types and dose rates.