Nucleon-Induced Fission Cross Sections of Heavy Nuclei in the Intermediate Energy Region

Abstract: Fission is the most important nuclear reaction for society at large today due to its use in energy production. However, this has raised the problem of how to treat the long-lived radioactive waste from nuclear reactors. A radical solution would be to change the composition of the waste into stable or short-lived nuclides, which could be done through nuclear transmutation. Such a concept requires accelerator-driven systems to be designed, where those for transmutation are reactor hybrids. This thesis is a contribution to the knowledge base for developing transmutation systems, specifically with respect to the computational modeling of the underlying nuclear reactions, induced by the incident and secondary particles. Intermediate energy fission cross sections are one important type of such data. Moreover, they are essential for understanding the fission process itself and related nuclear interactions. The experimental part of this work was performed at the neutron beam facility of the The Svedberg Laboratory in Uppsala. Fission cross sections of 238U, 209Bi, natPb, 208Pb, 197Au, natW, and 181Ta were measured for neutrons in the range En = 30-160 MeV using thin-film breakdown counters for the fission fragment detection. A model was developed for the determination of the efficiency of such detectors. A compilation of existing data on proton-induced fission cross sections for nuclei from 165Ho to 239Pu was performed. The results, which constitute the main body of information in this field, were added to the worldwide EXFOR database. The dependences of the cross sections on incident energy and target nucleus were studied, which resulted in systematics that make it possible to give estimates for unmeasured nuclides. Nucleon-induced fission cross sections were calculated using an extended version of the cascade exciton model. A comparison with the systematics and the experimental data obtained in the present work revealed significant discrepancies. A modification of the model was made that significantly improved the predictions.