Since the 1990s, researchers around the world have been creating antineutrino detectors for monitoring power reactors. These detectors have been deployed at light water reactors and are able to determine power levels and burn up throughout a fuel cycle. This technology could allow the IAEA to monitor LWRs remotely and unobtrusively to determine if they are operating using normal parameters. Very soon, the next generation of detector will be deployed at a CANDU reactor for a trial operation.
While physical observation of these detectors is necessaryl in determining their usefulness, reactor physics simulations have proven to be very accurate in their prediction of detector performance. Since there are many designs still in development, reactor physics simulations are the only way to determine the efficacy of the detector technology. In addition to this, reactor simulations are the best way to evaluate the detector technology to ascertain its usefulness during diversion scenarios.
In this research, antineutrino source terms were calculated for a High Temperature Gas Cooled Reactor core. These source terms were a function of power level and initial enrichment. SCALE6.1, developed by Oak Ridge National Laboratory, was used to calculate the isotopic inventory in the core as a function of depletion. These fertile and fissile isotopics, along with the fission cross-section and number of antineutrinos emitted per fission, were used to predict the antineutrino source rate for the core. It was found that changing the power yields a linear response from the antineutrino source term. By increasing the power by five percent, the source term also increased by five percent. Substantial changes in the initial enrichment also lead to a detectable change in the antineutrino source term. / Graduation date: 2012
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/29045 |
| Date | 10 April 2012 |
| Creators | Shaughnessy, Andra L. |
| Contributors | Palmer, Todd S. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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