Engineering topics

Godbey, K.

Publications and source records attributed to Godbey, K..

3 records

DOE OSTI2024

Isospin composition of fission barriers

We employ a microscopic method to study how isospin affects the fission potential of 240 Pu. Our approach uses constrained Hartree-Fock theory (CHF) which allows us to separately investigate the isoscalar and isovector properties of the nuclear energy density functional (EDF). By analyzing the isoscalar and isovector components of the EDF along the fission path we can assess the isovector contribution to fission barriers. Here, we study this effect for the fully adiabatic path to scission. The isovector component of the fission potential is found to increase in magnitude as the nucleus evolves towards scission, exemplifying the importance of stringent constraints on the isovector sector of the nuclear EDF for reliable predictions of fission properties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Shell effects in fission and quasi-fission

Quantum shell effects are responsible for asymmetric fission. They are also expected to affect the formation of fission fragments in quasi-fission reactions occurring in heavy-ion collisions. Shell effects in fission are studied with the single-particle energy level density near the Fermi level. In particular, shell effects in the pre-fragments and their role in fixing the final mass asymmetry of the fission fragments are discussed. Systematic time-dependent Hartree-Fock simulations of heavy-ion collisions show that quasi-fission fragment properties share strong similarities with fragments formed in fission of the compound nuclei. This is an indication that similar shell effects are responsible for the final asymmtery in both mechanisms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Model orthogonalization and Bayesian forecast mixing via principal component analysis

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar. In addition to contaminating the model space, the existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance. In this paper we describe a method based on the principal component analysis that eliminates model redundancy. We show that by adding model orthogonalization to the proposed Bayesian model combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗