IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-12385-1_49.html
   My bibliography  Save this book chapter

Uncertainty Quantification in a Regulatory Environment

In: Handbook of Uncertainty Quantification

Author

Listed:
  • Vincent A. Mousseau

    (Sandia National Laboratories)

  • Brian J. Williams

    (Los Alamos National Laboratory, Statistical Sciences Group)

Abstract

This chapter describes the use of the Predictive Capability Maturity Model (PCMM) (Oberkampf et al., Predictive capability maturity model for computational modeling and simulation. Technical report, SAND2007-5948, Sandia National Laboratories, 2007) applied to a nuclear reactor simulation. The application and PCMM will be discussed relative to review by the Nuclear Regulatory Commission. In a regulatory environment, one takes on the role of a lawyer presenting evidence to a judge with a prosecuting attorney allowed to cross-examine. In this type of “hostile” environment, a structured process that logically presents the evidence is helpful. In addition, many simulations are now multi-scale, multi-physics, and multi-code. For this level of complexity, it is easy to get lost in the details. The PCMM method has been adapted for this multi-physics multi-code software. Since the key is to provide the regulator with confidence that the software is capable of predicting the quantity of interest (QoI) with a well-quantified uncertainty, the PCMM approach is a natural solution.

Suggested Citation

  • Vincent A. Mousseau & Brian J. Williams, 2017. "Uncertainty Quantification in a Regulatory Environment," Springer Books, in: Roger Ghanem & David Higdon & Houman Owhadi (ed.), Handbook of Uncertainty Quantification, chapter 48, pages 1613-1648, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-12385-1_49
    DOI: 10.1007/978-3-319-12385-1_49
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-319-12385-1_49. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.