Development and Verification of Two-step Spectrum Unfolding Code
To address the challenge of unknown preset spectra,this paper introduces a two-step spectrum unfolding method that combines the generalized regression neural network(GRNN)and the iterative algorithm.We have independently developed the spectrum unfolding codes for GRNN and iteration and conducted separate and comprehensive validations of the codes.Initially,we utilized activation method data from the Chinese Experimental Fast Reactor(CEFR)to validate the codes.The results indicated that at neutron energies greater than 0.1 MeV,the GRNN results deviated by a maximum of 10.36%from the theoretical spectra.The iterative method's results deviated by a maximum of 9.15%compared to those obtained using the least squares method.The calculated single nuclear reaction rates showed a maximum relative deviation of 11.71%from the experimental values,indicating good agreement.Furthermore,the GRNN method demonstrated higher accuracy compared to the iterative method without accurate pre-set spectra.Finally,comprehensive validation was performed using Russian boron carbide irradiation data,revealing a maximum deviation of 11.42%in the fast neutron region between the two-step method and the iterative method with pre-set spectra.Therefore,employing a"two-step spectrum unfolding method"to address the challenge of unknown pre-set spectra is feasible,with errors remaining within acceptable limits.The innovative spectrum unfolding method introduced in this paper offers fresh perspectives for the spectrum unfolding of new reactors and offers significant reference value for experiments with unknown pre-set spectra.
万方数据
维普
