NEUP DECP-29754: Using Machine Learning to Understand the Transient Critical Heat Flux and Post-CHF Heat Transfer

To answer crucial questions on transient critical heat flux and post-CHF heat transfer behavior, the PI proposes to conduct experiments using high-resolution distributed temperature sensors in a directly heated fuel rod simulator at prototypical LWR accident conditions and use machine learning methods to improve the predictability capabilities of relevant computational codes. Two educational developments are proposed to promote broader and more inclusive nuclear engineering learning.

Additional Info

Field	Value
Awarded Institution	University of Wisconsin-Madison
PI	Juliana Pacheco Duarte