Spallation reaction study for long-lived fission products in nuclear waste

Spallation reaction for the long-lived fission product 107Pd has been studied for the purpose of nuclear waste transmutation. The isotopic-distribution cross sections on both proton and deuteron were obtained at 118 MeV/nucleon in inverse kinematics at the RIKEN Radioactive Isotope Beam Factory. A large cross-section difference was found between the proton and deuteron results for the light-mass products. The data were compared with the SPACS semi-empirical parameterization and the PHITS calculations including both the intranuclear cascade and evaporation processes. In addition, the potential of spallation reaction for transmutation of 107Pd is discussed.

Corrigendum: Comparison of spallation reaction models based on multiple-criteria decision analysis. https://doi.org/10.3897/nucet.4.31869

Corrigenda: Comparison of spallation reaction models based on multiple-criteria decision analysis. https://doi.org/10.3897/nucet.4.31869

Comparison of spallation reaction models based on multiple-criteria decision analysis

The paper presents the results of a comparative evaluation of the predictive ability of seventeen spallation reaction models (CEM02, CEM03, Phits/jam, Cascade/ASF, Phits/Bertini, Bertini/Dresner, Cascade-4, INCL4/Abla, INCL4/smm, geant4/binary, Isabela/smm, geant4/Bertini, Isabela/Abla, INCL4/Gemini, CASCADeX-1.2, Isabel/Gemini, Phits/jqmd) for the interaction reactions of high-energy protons with natPb nuclei using the most popular methods of multiple-criteria decision analysis (MAVT/MAUT, AHP, TOPSIS, PROMETHEE). Multiple-criteria decision analysis methods are used extensively to support decision-making in various fields of knowledge, including nuclear physics and engineering, when aggregating conflicting criteria with due account for the expert and decision-maker opinions. Four factors of computational and experimental agreement (R, D, F, H), most commonly used in this field of knowledge, have been employed as the criteria, which, having been aggregated as part of applying respective multiple-criteria decision analysis methods, make it possible to estimate the integral measure of the computational model effectiveness and to rank the models, using this as the basis, depending on the degree of their predictive ability. It has been demonstrated that the ranking results obtained using different multiple-criteria decision analysis methods show a good agreement. Using a stochastic approach to the generation of weights, the models were ranked in conditions with the absence of data on the significance of individual agreement factors. Recommendations are presented for using the multiple-criteria decision analysis methods to address tasks involved in the preparation of nuclear data in conditions of a multiple-factor evaluation of discrepancies between calculations and experiment.