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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Development of Technical Nuclear Forensics for Spent Research Reactor Fuel

Sternat, Matthew Ryan 1982- 14 March 2013 (has links)
Pre-detonation technical nuclear forensics techniques for research reactor spent fuel were developed in a collaborative project with Savannah River National Lab ratory. An inverse analysis method was employed to reconstruct reactor parameters from a spent fuel sample using results from a radiochemical analysis. In the inverse analysis, a reactor physics code is used as a forward model. Verification and validation of different reactor physics codes was performed for usage in the inverse analysis. The verification and validation process consisted of two parts. The first is a variance analysis of Monte Carlo reactor physics burnup simulation results. The codes used in this work are MONTEBURNS and MCNPX/CINDER. Both utilize Monte Carlo transport calculations for reaction rate and flux results. Neither code has a variance analysis that will propagate through depletion steps, so a method to quantify and understand the variance propagation through these depletion calculations was developed. The second verification and validation process consisted of comparing reactor physics code output isotopic compositions to radiochemical analysis results. A sample from an Oak Ridge Research Reactor spent fuel assembly was acquired through a drilling process. This sample was then dissolved in nitric acid and diluted in three different quantities, creating three separate samples. A radiochemical analysis was completed and the results were compared to simulation outputs at different levels ofdetail. After establishing a forward model, an inverse analysis was developed to re-construct the burnup, initial uranium isotopic compositions, and cooling time of a research reactor spent fuel sample. A convergence acceleration technique was used that consisted of an analytical calculation to predict burnup, initial 235U, and 236U enrichments. The analytic calculation results may also be used stand alone or in a database search algorithm. In this work, a reactor physics code is used as a for- ward model with the analytic results as initial conditions in a numerical optimization algorithm. In the numerical analysis, the burnup and initial uranium isotopic com- positions are reconstructed until the iterative spent fuel characteristics converge with the measured data. Upon convergence of the sample’s burnup and initial uranium isotopic composition, the cooling time can be reconstructed. To reconstruct cooling time, the standard decay equation is inverted and solved for time. Two methods were developed. One method uses the converged burnup and initial uranium isotopic compositions along in a reactor depletion simulation. The second method uses an isotopic signature that does not decay out of its mass bin and has a simple production chain. An example would be 137Cs which decays into the stable 137Ba. Similar results are achieved with both methods, but extended shutdown time or time away from power results in over prediction of the cooling time. The over prediction of cooling time and comparison of different burnup reconstruction isotope results are indicator signatures of extended shutdown or time away from power. Due to dynamic operation in time and function, detailed power history reconstruction for research reactors is very challenging. Frequent variations in power, repeated variable shutdown time length, and experimentation history affect the spectrum an individual assembly is burned with such that full reactor parameter reconstruction is difficult. The results from this technical nuclear forensic analysis may be used with law enforcement, intelligence data, macroscopic and microscopic sample characteristics in a process called attribution to suggest or exclude possible sources of origin for a sample.

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