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Covariance and sensitivity data generation at ORNL.

L C Leal1, H Derrien, N M Larson

  • 1Oak Ridge National Laboratory, P. O. Box 2008, Oak Ridge, TN 37831, USA. leallc@ornl.gov

Radiation Protection Dosimetry
|December 31, 2005
PubMed
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This study details generating nuclear data covariance data using the SAMMY code, crucial for assessing design parameter uncertainties in nuclear applications. The retroactive covariance generation approach is applied to gadolinium isotopes.

Area of Science:

  • Nuclear physics
  • Computational physics

Background:

  • Covariance data are essential for estimating uncertainties in nuclear design parameters.
  • Nuclear data uncertainty information is stored in libraries like ENDF/B, derived from experimental analysis.
  • These libraries contain variance and covariance data crucial for error estimation.

Purpose of the Study:

  • To address the generation of covariance data within the resonance region using the SAMMY computer code.
  • To describe the application of the 'retroactive' resonance parameter covariance generation approach.
  • To apply this method specifically to gadolinium isotopes.

Main Methods:

  • Utilizing the SAMMY code for evaluating experimental data in resolved and unresolved resonance energy regions.
  • Employing generalized least-squares formalism (Bayesian theory) and R-matrix theory for cross-section data fitting.

Related Experiment Videos

  • Implementing two covariance data generation approaches within SAMMY, including the retroactive method.

    • Main Results:

    • SAMMY successfully generates resonance parameters and their covariances.
    • The retroactive covariance generation approach is demonstrated for gadolinium isotopes.
    • This method provides a viable alternative when direct covariance data is unavailable.

    Conclusions:

    • The SAMMY code is effective for generating resonance parameter covariance data.
    • The retroactive approach offers a valuable method for obtaining covariance data for isotopes like gadolinium.
    • Accurate covariance data is vital for reliable uncertainty quantification in nuclear applications.