Past weapons production activities have resulted in mass quantities of trans-uranic
waste being buried in drums at several sites in the United States. In an effort to
relocate these waste drums to more permanent storage sites, Fluor Hanford has
begun characterizing their contents to ensure compliance with various shipping and
storage requirements. Non-destructive analysis techniques are regularly employed,
among them passive radiation detection using a Canberra Gamma-Energy-Analyzer
germanium detector vault. Necessary strict legal tolerances require strong quality
assurance. The detectors are frequently calibrated in the traditional method with
check sources, but it would be advantageous to have an estimate of system
minimum detectable activity (MDA). However, any estimate is complicated by the
fact that sources are distributed stochastically in the waste drums.
In this study, a method was developed to predict system detector efficiency for a
variety of detector configurations and drum fill materials and calculate MDA based
on these efficiencies. The various system designs were modeled in Monte Carlo
N-Particle Code, version 4b, to determine photopeak detection efficiency. An
external code written in C programming language was used to randomly assign
between one and 20 sources to volumetric regions of the waste drum. Twenty
simulations were performed for each design and drum fill material combination,
each time redefining the stochastically distributed source. This provided a
normally distributed spectrum of 20 efficiencies for each situation. From this,
mean and lower 95% confidence limit efficiencies were used to calculate MDA.
The patterns among the results were then compared with values predicted by the
MDA formula. Finally, an examination was made of the impact on the MDA of the
system's true design in the case of single or multiple detector failure.
The results indicate that this method of estimating minimum detectable activity,
although costly in computing time, provides results consistent with intuitive and
calculated expectations. Future work would allow easy calibration of the model to
measured efficiency results. Used in coordination with physical experiments, this
method may eventually prove useful in benchmarking system performance and
accurately ensuring reliable waste drum characterizations. / Graduation date: 2003
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/32361 |
| Date | 16 October 2001 |
| Creators | Buchholz, Matthew A. |
| Contributors | Higginbotham, Jack F. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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