SUPR
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

  • Castor /proj/nobackup at UPPMAX: 128 GiB
  • Castor /proj at UPPMAX: 128 GiB
  • Cygnus /proj at UPPMAX: 128 GiB
  • Cygnus /proj/nobackup 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.