Nuclear Engineering ETDs

Publication Date

Fall 11-15-2023

Abstract

A predictive density law has been implemented and developed into a Python tool to reduce bias and the high degree of conservatism in criticality safety calculations for aqueous plutonium chloride systems. Based upon available data and parameters, an empirical method was used to create this law. This method was used in the development of a Python tool to predict the density and composition of such a solution, which may then populate material data in an MCNP6 input file.

This tool and the density law equation have been verified against experimental data points for accuracy. Density is predicted with a maximum error of 1.88% when compared to experimental data. Alongside nuclear data validation, this density prediction may lead to an approximately 12% decrease in keff of such a solution system near peak reactivity when considering minimal amounts of HC1 (0.5 M); this may correspond to up a 24% decrease for a larger acid content (2 M). Thus, this work will allow for a more accurate modeling method to be utilized in criticality safety applications, as well as provide a better characterization of these systems.

Keywords

solutions, density law, plutonium chloride, MCNP6, MR&R

Sponsors

Los Alamos National Laboratory and DOE NCSP

Document Type

Thesis

Language

English

Degree Name

Nuclear Engineering

Level of Degree

Masters

First Committee Member (Chair)

Dr. Christopher Perfetti

Second Committee Member

Dr. Forrest Brown

Third Committee Member

Norann Calhoun

Fourth Committee Member

Dr. Kelly Aldrich

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