psi methodologies for nuclear data uncertainty propagation with casmo-5m and mcnpx: results for oecd/nea uam benchmark phase i

Clicks: 217
ID: 163513
2013
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #13 of 65 articles by views in drug metabolism reviews

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Capabilities for uncertainty quantification (UQ) with respect to nuclear data have been developed at PSI in the recent years and applied to the UAM benchmark. The guiding principle for the PSI UQ development has been to implement nonintrusive “black box” UQ techniques in state-of-the-art, production-quality codes used already for routine analyses. Two complimentary UQ techniques have been developed thus far: (i) direct perturbation (DP) and (ii) stochastic sampling (SS). The DP technique is, first and foremost, a robust and versatile sensitivity coefficient calculation, applicable to all types of input and output. Using standard uncertainty propagation, the sensitivity coefficients are folded with variance/covariance matrices (VCMs) leading to a local first-order UQ method. The complementary SS technique samples uncertain inputs according to their joint probability distributions and provides a global, all-order UQ method. This paper describes both DP and SS implemented in the lattice physics code CASMO-5MX (a special PSI-modified version of CASMO-5M) and a preliminary SS technique implemented in MCNPX, routinely used in criticality safety and fluence analyses. Results are presented for the UAM benchmark exercises I-1 (cell) and I-2 (assembly).
Reference Key
wieselquist2013sciencepsi Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;W. Wieselquist;T. Zhu;A. Vasiliev;H. Ferroukhi
Journal drug metabolism reviews
Year 2013
DOI
10.1155/2013/549793
URL
http://dx.doi.org/10.1155/2013/549793
Keywords
electrical engineering electronics nuclear engineering

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.