Nuclear Data Sensitivity Tool (NDaST)
Screenshot of NDaST Image: NEA

Nuclear Data Sensitivity Tool (NDaST)

The Nuclear Data Sensitivity Tool (NDaST) is a Java based piece of software, designed to perform calculations on nuclear data sensitivity files for benchmark cases. These calculations are either:

  • an estimation of the impact of nuclear data perturbations to the computed case results, and/or;
  • calculation of the uncertainty in the computed results due to evaluated nuclear covariance data.

This allows simple and fast analysis for nuclear data evaluators to test the impact of revisions across a wide set of benchmarks. Users may also efficiently compare the difference in uncertainties obtained from various sources.

Automated database search and loading from:

Supported formats:

  • sensitivity data: Tsunami 1D/3D (.sdf), ABBN
  • covariance data: ENDF, BOXER, COVERX.

User manual:

Launch NDaST

Launch NDaST
May 2022 version. Requires Java 6 or later.


External Publications:

Dyrda, J., Hill, I., Bossant, M., Gulliford, J. and Soppera, N., 2015. The New OECD-NEA Nuclear Data and Sensitivity Tool (NDaST). Transactions113(1), pp.1299-1302.

Hill, I. and Dyrda, J., 2016. Nuclear Data Testing Of Pu {sup 239} CIELO Evaluation With NDaST. Transactions of the American Nuclear Society115

Dyrda, J., Soppera, N., Hill, I., Bossant, M. and Gulliford, J., 2017. New features and improved uncertainty analysis in the NEA nuclear data sensitivity tool (NDaST). In EPJ Web of Conferences (Vol. 146, p. 06026). EDP Sciences.

Dyrda, J., Hill, I., Fiorito, L., Cabellos, O., & Soppera, N. (2018). A comparison of uncertainty propagation techniques using NDaST: full, half or zero Monte Carlo?. EPJ Nuclear Sciences & Technologies4, 14.


Related topics
  • Nuclear science
  • Nuclear data
  • Download files
    NDaST results

    Nuclear Data Covariance Propagation on ICSBEP Benchmarks (k-eff)

    Extract of results from: O.Cabellos, J.Dyrda and N.Soppera, "Checking, Processing and Verification of Nuclear Data Covariances" CW2017, Aix-en-Provence, Oct 2017