Analysis of data from and predictions of properties of spent nuclear fuel
Dnr:
sens2020607
Type:
SNIC SENS
Principal Investigator:
Erik Branger
Affiliation:
Uppsala universitet
Start Date:
2021-01-01
End Date:
2025-01-01
Primary Classification:
10301: Subatomic Physics
Webpage:
Allocation
- Cygnus /proj/nobackup at UPPMAX: 128 GiB
- Cygnus /proj at UPPMAX: 128 GiB
- Bianca at UPPMAX: 2 x 1000 core-h/month
Abstract
In the context of handling of spent nuclear fuel (SNF), data from measurements of SNF will be analyzed and the properties of the SNF will be predicted using different methods, including multivariate analysis and machine learning.