14 MeV neutron generator dose modeling

Modeling and understanding the doses around the neutron generator provides insightful data in regard to radiation safety and protection precautions. Published data can be used to predict doses, but realistic data for the Nuclear Engineering Teaching Laboratory’s Thermo MP 320 Neutron Generator helps health physicists more accurately predict dose rates and protect experimenters against exposure. The goal was to create a model inclusive of the entire setup and room where the neutron generator is housed. Monte Carlo N-Particle (MCNP) Code reigns as the preferred method for modeling radiation transport and was utilized to model the transport of neutrons within the current configuration of the 14 MeV neutron generator facility. This model took into account all shielding materials and their respective dimensions and locations within the concrete room. By utilizing tallies and tally modifiers, the model predicts dose rates that can be used with experimental factors such as irradiation time and flux to predict a dose in millirem. Validation experiments were performed in the current setup using Landauer Luxel®+ with Neutrak dosimeters placed in strategic locations to record the neutron dose vi received as well as a Ludlum Model 42-41 PRESCILA neutron probe to predict dose rates. The dosimeters and PRESCILA measurement locations matched the positions of the point detector tallies in MCNP. After laboratory analysis, a comparison was performed between the model output and the dosimeter and PRESCILA values to successfully validate the accuracy of the model.

Name thesis:degree_name

Master of Science in Engineering

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

The University of Texas at Austin

Year dc:date.issued

2013

Author dc:creator

• McConnell, Kristen Alycia

Advisor dc:contributor.advisor

• Biegalski, Steven R.

• MCNP
• Neutron generator
• Dose modeling

Handle dc:identifier.uri

http://hdl.handle.net/2152/23577

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/23577