Self-Powered Neutron Detectors (SPNDs) are currently used in reactors’ environment to sense the magnitude of neutron-fluxes, usually for spatial-distribution mapping of the fuel region as to optimize burn-up strategies. During the development of tailored instrumentation for Lead-cooled Fast Reactors, the possibility to perform online spectral deconvolution of fast neutron-fluxes was recognized. Seven geometrically similar SPNDs with different neutron-sensitive materials, have been characterized by the Monte Carlo transport code MCNPX. Thanks to a database of spectral sensitivities vs. neutrons’ energy, a mathematical deconvolution process from 7 electric current values virtually measured by SPNDs was started, retrieving spectral information, in terms of 7 energy windows, of the neutron-flux the detectors are subjected by. This paper describes the procedure that led to those results, prefiguring future development to improve proposed method. © 2017 Elsevier B.V.
A proposal for an alternative use of prompt-Self Powered Neutron Detectors: Online spectral-deconvolution for monitoring high-intensity neutron flux in LFRs
Pietropaolo, A.
2017-01-01
Abstract
Self-Powered Neutron Detectors (SPNDs) are currently used in reactors’ environment to sense the magnitude of neutron-fluxes, usually for spatial-distribution mapping of the fuel region as to optimize burn-up strategies. During the development of tailored instrumentation for Lead-cooled Fast Reactors, the possibility to perform online spectral deconvolution of fast neutron-fluxes was recognized. Seven geometrically similar SPNDs with different neutron-sensitive materials, have been characterized by the Monte Carlo transport code MCNPX. Thanks to a database of spectral sensitivities vs. neutrons’ energy, a mathematical deconvolution process from 7 electric current values virtually measured by SPNDs was started, retrieving spectral information, in terms of 7 energy windows, of the neutron-flux the detectors are subjected by. This paper describes the procedure that led to those results, prefiguring future development to improve proposed method. © 2017 Elsevier B.V.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
