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IAEA project aims to develop polymer irradiation model
The International Atomic Energy Agency has launched a new coordinated research project (CRP) aimed at creating a database of polymer-radiation interactions in the next five years with the long-term goal of using the database to enable machine learning–based predictive models.
Radiation-induced modifications are widely applicable across a range of fields including healthcare, agriculture, and environmental applications, and exposure to radiation is a major factor when considering materials used at nuclear power plants.
Braden Goddard, William Charlton, Paolo Peerani
Nuclear Technology | Volume 186 | Number 3 | June 2014 | Pages 403-414
Technical Paper | Fuel Cycle and Management | doi.org/10.13182/NT13-18
Articles are hosted by Taylor and Francis Online.
As new reprocessing techniques and fuel forms are developed, the ability of inspection agencies and facility operators to measure powders containing several actinides becomes increasingly necessary. Neutrons emitted from induced and spontaneous fission of different nuclides are very similar, making it difficult to measure these powders with nondestructive assay techniques. To measure the powders, a neutron multiplicity technique based on first-principle methods was developed to exploit isotope-specific nuclear properties, such as energy-dependent fission cross sections and neutron-induced fission multiplicity. This technique was tested through measurements using an epithermal neutron multiplicity counter with two different interrogation (α,n) sources and varying plutonium materials. To complement these measurements, extensive Monte Carlo N-Particle eXtended (MCNPX) simulations were performed for each measured sample, as well as samples that were not available to measure. The primary application of this first-principle technique is the measurement of materials containing uranium, neptunium, plutonium, and americium. This technique still has several challenges that need to be overcome, the largest of these being the ability to produce results with acceptably small uncertainties.