Mechanistic materials modeling for nuclear fuel performance. (July 2017)

• Record Type: Journal Article
• Authors: Tonks, Michael R.; Andersson, David; Phillpot, Simon R.; Zhang, Yongfeng; Williamson, Richard; Stanek, Christopher R.; Uberuaga, Blas P.; Hayes, Steven L.
• Is Part Of: Annals of nuclear energy. Volume 105(2017:Jul.)
• Page Start: 11
• Page End: 24
• Publication Date: 2017-07
• Subjects: Fuel performance modeling -- Multiscale modeling and simulation; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2017.03.005
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Fuel performance codes are limited by empirical materials models correlated to burnup. We propose mechanistic materials models based on the evolving microstructure. Multiscale simulation is used with experimental data to inform model development. The approach's completion will require researchers working together around the world. Abstract: Fuel performance codes are critical tools for the design, certification, and safety analysis of nuclear reactors. However, their ability to predict fuel behavior under abnormal conditions is severely limited by their considerable reliance on empirical materials models correlated to burn-up (a measure of the number of fission events that have occurred, but not a unique measure of the history of the material). Here, we propose a different paradigm for fuel performance codes to employ mechanistic materials models that are based on the current state of the evolving microstructure rather than burn-up. In this approach, a series of state variables are stored at material points and define the current state of the microstructure. The evolution of these state variables is defined by mechanistic models that are functions of fuel conditions and other state variables. The material properties of the fuel and cladding are determined from microstructure/property relationships that are functions of the state variables and the current fuel conditions. Multiscale modeling and simulation is being used in conjunction with experimental data to inform the development of these models. This mechanistic, microstructure-based approach has the potential to provide a more predictive fuel performance capability, but will require a team of researchers to complete the required development and to validate the approach.