Showing papers by "Chalk River Laboratories published in 2022"

In-situ Field Gamma Spectrometry in a Radionuclide Air Sampler

Abstract: The work being presented is on the development of a system to measure the speciation of airborne radionuclide emissions from the environment during a nuclear emergency. On-site air sampling measurements that were conducted during the Fukushima Daiichi accident were limited because field teams had to be sent out to run the sampling systems and retrieve the filters for gamma spectrometry analysis in a separate laboratory. The start of air sampling was delayed, and it was impossible for emergency responders to use the information about the airborne radionuclide composition in a timely way. The goal of the current study is to develop a system that could provide live, near real-time information about the concentrations of different radionuclides in the air without having to rely on human intervention. The development of the prototype in the current work is largely being enabled by Cd-Zn-Te spectrometers, which provide reasonably high-resolution spectrometry given that it is a room temperature sensor, and allow the measurements to be conducted in the field. A custom filter cartridge has been designed to hold a pair of aerosol and iodine filters in place while keeping the gamma spectrometers as close as possible in order to obtain high count rate efficiencies. A single cartridge holds both filters and has an internal flow channel directing the air flow between them. The cartridge design also facilitates replacing the filters as the accumulated radioactivity on the filters becomes too high. An automation system can move a filter cartridge from the fresh cartridge storage bank to the sampling location (filtration and gamma spectrometry) and return the used filter cartridge to the used cartridge storage bank. The radionuclide air sampling system prototype has been designed and constructed. It has been tested with fixed sources located on the respective aerosol and iodine filters. The real-time data capture aspects of the system were also demonstrated with a live 131I capture experiment. The projected performance of the system during a reactor accident was also simulated, emulating the characteristic detector efficiencies and projecting how the airborne concentrations could be reconstructed. The study has designed and constructed a radionuclide air sampler that could be used for measuring airborne radioactivity in emissions from a nuclear accident. Because the gamma spectrometry measurements are done in situ with good resolution and the system is automated, it would allow data to be transmitted back to an emergency operations center immediately rather than having to wait for additional laboratory analysis.

Preliminary Evaluation of the LASSO Method for Prediction of the Relative Power Density Distribution in Mixed Oxide (Pu, DU)O2 Fuel Pellets

Abstract: Predicting the power distribution within nuclear fuel is essential for predicting reactor fuel performance, since power distributions can impact pellet temperature distributions and fission product transport and migration. Analytical expressions for radial power distribution in fuel pellets were sought using lattice physics calculations to generate data and a machine learning technique was applied to find representative expressions. Analytical approximations can be useful in nuclear fuel performance codes, such as ELESTRES/ELOCA for providing very rapid predictions of power distributions with reduced computational effort and memory requirements, relative to using an embedded or coupled neutron transport / burnup reactor physics code. Radial power distributions were calculated a priori using lattice physics codes to model mixed oxide (MOX) 37-element fuel bundles in pressure tube heavy water reactors (PT-HWRs). Such advanced fuels are of interest for future fuel cycles. Several datasets were generated with different amounts of PuO2 and variable neutron energy spectrum. Results of preliminary studies with the Least Absolute Shrinkage and Selection Operator (LASSO) regression machine learning method have obtained analytical fitting functions with a mean maximum relative error (MRE) of 0.056 and a maximum MRE of 0.152 on the test set. However, using LASSO to estimate the coefficients of a physically-motivated modified Bessel plus an exponential function, results in a lower MRE (mean MRE 0.041 and maximum MRE 0.11) on the same test set. Further potential improvements in both the curve fit and the machine learning methods are discussed.