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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.
Thomas M. Evans, Kevin T. Clarno, Jim E. Morel
Nuclear Science and Engineering | Volume 165 | Number 3 | July 2010 | Pages 292-304
Technical Paper | doi.org/10.13182/NSE09-28
Articles are hosted by Taylor and Francis Online.
We have developed a modification of the two-grid upscatter acceleration scheme of Adams and Morel. The modified scheme uses a low-angular-order discrete ordinates equation to accelerate Gauss-Seidel multigroup iteration. This modification ensures that the scheme does not suffer from consistency problems that can affect diffusion-accelerated methods in multidimensional, multimaterial problems. The new transport two-grid scheme is very simple to implement for different spatial discretizations because it uses the same transport operator. The scheme has also been demonstrated to be very effective on three-dimensional, multimaterial problems. On simple one-dimensional graphite and heavy-water slabs modeled in three dimensions with reflecting boundary conditions, we see reductions in the number of Gauss-Seidel iterations by factors of 75 to 1000. We have also demonstrated the effectiveness of the new method on neutron well-logging problems. For forward problems, the new acceleration scheme reduces the number of Gauss-Seidel iterations by more than an order of magnitude with a corresponding reduction in the run time. For adjoint problems, the speedup is not as dramatic, but the new method still reduces the run time by greater than a factor of 6.