| Scope |
In the last decade, a wealth of new characterization methods, in-situ experimental techniques, and machine learning have emerged to drastically increase the speed and fidelity with which microstructure, and its dynamic evolution, can be characterized. This symposium solicits presentations that apply any of these advanced techniques to the study of materials structure, properties, and performance in radiation and other extreme environments, e.g., nuclear energy and space applications. These technique advancements have occurred in areas including microstructural characterization, thermophysical property measurement, in situ measurements, and small-scale mechanical property testing. There is a specific interest in techniques that directly impact materials research for environments exhibiting high radiation fields, extreme temperatures, and corrosive or chemically reactive environments. In addition to extremes present during routine operations, off-normal events, or transients, such as the aggressive thermal oxidation and decomposition of plasma facing components during air ingress accidents, call for rapid material innovations at this defining moment of rehabilitation for nuclear energy systems. The unique data provided by these advanced characterization tools also provide a new bridge to enhance the framing, refining, and validation of predictive models.
Specific topics include, but are not limited to:
• Novel destructive and non-destructive techniques for radiation damage characterization.
• Machine learning for microstructural characterization.
• Non-contact thermal and elastic measurement techniques.
• Non-standard mechanical property testing.
• Advanced diffraction techniques (X-ray, electron, or neutron) coupled to extreme environments.
• In-situ observation of microstructural evolution under irradiation.
• Methods for monitoring corrosive attack in coolant environments.
• Studies of synergistic effects of these superimposed extreme environments on materials behaviors.
• Work enabling enhanced coupling of experimental with predictive modeling and simulation. |