Residence-time theory combined with DNP transport yields closed-form static reactivity loss and zero-power transfer function for CFRs, generalizing plug-flow and CSTR cases via a mixing parameter and validated on MSRE data plus Serpent-2/CFD results.
GeN-Foam model and benchmark of delayed neutron precursor drift in the Molten Salt Reactor Experiment
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Generative models including VAEs, normalizing flows, GANs, and diffusion models can learn neutron source distributions from Monte Carlo lists for fast, memory-free sampling after training.
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Residence-time theory applied to circulating-fuel reactors: zero-power analysis
Residence-time theory combined with DNP transport yields closed-form static reactivity loss and zero-power transfer function for CFRs, generalizing plug-flow and CSTR cases via a mixing parameter and validated on MSRE data plus Serpent-2/CFD results.
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Machine Learning for neutron source distributions
Generative models including VAEs, normalizing flows, GANs, and diffusion models can learn neutron source distributions from Monte Carlo lists for fast, memory-free sampling after training.