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.
CFD simulations of the Molten Salt Fast Reactor
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MD-PNOP recasts parameter-induced operator differences as source terms to enable single-configuration neural operator training for extrapolation and acceleration of parametric PDE solvers.
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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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MD-PNOP: Equation-Recast Neural Operators for Minimal-Data Extrapolation and PDE Solver Acceleration
MD-PNOP recasts parameter-induced operator differences as source terms to enable single-configuration neural operator training for extrapolation and acceleration of parametric PDE solvers.