Engineering topics
Publications and source records attributed to McDonnell, J. D..
This presentation focus on AMPX development for 2024 it also touches on ENDF/B-VIII.1. The slides touches on Thermal Neutron Scattering Library, AMPX Development Highlights, and Transport libraries for SCALE 7.0.0.
63,65 Cu angular distributions resolved for ENDF/B-VIII.1, consistent with both differential data and integral benchmark performance. 139 La RRR and URR evaluations will be merged with the LANL high energy evaluation, and to be submitted to the future ENDF-B release (post-VIII.1). 14 N RRR evaluation is planned to produce n+ 14 N, p+ 14 C, and a+ 11 B contributions to be submitted to the future ENDF-B release (post-VIII.1). 181 Ta covariances repaired and reported as intended in an errata to ENDF/B-VIII.1.
This presentation is a review of AMPX strategy for GNDS. It includes a summary of point data comparison, Summary of covariance data comparison, and a preview of SAMMY GNDS I/O and other development.
This presentation deals with AMPX development in FY2023 focused on preparing for the release and testing of ENDF/B-VIII.1. It includes updates for Thermal Scattering Law in ENDF/B-VIII.1. The presentation finishes with touching on resolution of a SCALE Issue with thermal kernels and photonuclear physics support.
This presentation starts off with covering general ENDF issues encountered. With a look into issues particular to thermal scattering library. This presentation includes AMPX Development Highlights. The talk concludes with the status of the AMPX Photonuclear Sublibrary Support.
This presentation covers the status of GNDS Support in AMPX. This presentation covers an overview of GNDS support in AMPX. Then the presentation touches on the AMPX process in GNDS and a checklist. This presentation concludes with a continuing effort section and conclusions.
This presentation provides an enlightening discussion of AMPX and ENDF/B-VIII.1. The talk begins with general ENDF issues encountered. Then narrows down into Issues particular to thermal scattering library. Such as the need for clear communication of the intended use for the file and the question of “Which graphite should I use in model?”. The presentation then looks at TSL files with many Bragg peaks are driving a change in SCALE CE library format and transport codes. This lecture concludes with a look at usage of thermal kernels with sharp peaks with the issues encountered using ENDF/B-VIII.0 data and resolved.
This talk centers on AMPX developments in fiscal year of 2022. This presentation provides links to the open-source subset of SCALE, including AMPX. The discussion additionally provides an overview of GNDS support in AMPX. The presentation includes selected features on several SCALE versions and features.
This presentation covers recent AMPX developments. Presentation highlights include the news of AMPX being openly available, link to source and configure options are included. The interconnections between AMPX and Generalized Nuclear Database Structure (GNDS) are additionally highlighted as well.
An HDF5-based file format was introduced for the sensitivity data calculated by TSUNAMI. The format was defined to collect the sensitivity coefficients into hyperslabs, which optimizes file reading time and therefore improves the time-to-solution for applications. In future work, this format will be extended to store sensitivity data for depletion calculations.
Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going eff orts seek to better root nuclear DFT in the theory of nuclear forces, energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in fi nite nuclei. In this study, we review recent eff orts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statistical analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature.