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May 31–June 3, 2026
Denver, CO|Sheraton Denver
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Can AI deliver nuclear on time and on budget? These companies think so.
AI for energy, and energy for AI: that is the new refrain. But can nuclear power plants be deployed at the pace needed for substantial and timely contributions to the energy infrastructure? For Westinghouse, delivering its AP1000 on time and on budget in the United States is a challenge not yet accomplished, while newcomers like Aalo Atomics are turning to AI to speed design, permitting, and construction.
R. H. Chen, M. L. Corradini, G. H. Su, S. Z. Qiu
Nuclear Science and Engineering | Volume 173 | Number 1 | January 2013 | Pages 1-14
Technical Paper | doi.org/10.13182/NSE12-10
Articles are hosted by Taylor and Francis Online.
A molten fuel breakup model that considers solidification effects is proposed in this paper. Both the effect of a solid crust layer and the effect of thermal stresses on the fuel particle fragmentation are taken into account in this model. This solidification model predicts the transient temperature profile and crust layer thickness of the fuel particle by numerically solving the Fourier heat conduction equation under specific initial and boundary conditions. This fuel particle breakup model and transient temperature profile model were incorporated into the TEXAS fuel-coolant interaction (FCI) model; this revised TEXAS FCI model is called TEXAS-VI. This paper compares TEXAS-VI to the FARO L14 experiment (FARO L14), for which fuel-coolant mixing and quench data have been published. The FARO L14 pressure history, liquid water pool temperature, and vapor temperature were found to be in good agreement with the revised model predictions. This mixing behavior will also have an impact on FCI explosion energetics. The solidification effect is under investigation for energetics.