The Role of Uncertainty Quantification for Reactor Physics M. Salvatores, 1, 2, ∗ G. Aliberti, 3 and G. Palmiotti 2 1 Consultant 2 Idaho National Laboratory, Idaho Falls, ID 83415, USA 3 Argonne National Laboratory, Argonne, IL 60439, USA (Dated: August 1, 2014; Received XX May 2014; revised received XX August 2014; accepted XX September 2014) The quantification of uncertainties is a crucial step in design. The comparison of a-priori uncer- tainties with the target accuracies, allows to define needs and priorities for uncertainty reduction. In view of their impact, the uncertainty analysis requires a reliability assessment of the uncertainty data used. The choice of the appropriate approach and the consistency of different approaches are discussed. I. INTRODUCTION The role of uncertainty quantification has been stressed and has been the object of several assessments in the past (see e.g. References [1] and [2] among many oth- ers), in particular in relation to design requirements for safety assessments, design margins definition and opti- mization, both for the reactor core and for the associated fuel cycles. The use of integral experiments has been ad- vocated since many years, and recently re-assessed [3] in order to reduce uncertainties and to define new reduced “a-posteriori” uncertainties. While uncertainty quantifi- cation in the case of existing power plants benefits from a large data base of operating reactor experimental re- sults, innovative reactor systems (reactor and associated fuel cycles) should rely on limited power reactor experi- ment data bases and on a number of past integral experi- ments that should be shown to be representative enough. Moreover, in some cases, in particular related to inno- vative fuel cycle performance and feasibility assessment, nuclear data uncertainties are the only available informa- tion. Uncertainty quantification in that case becomes a tool for detecting potential show stoppers associated to specific fuel cycle strategies, besides the challenges related to fuel properties, fuel processing chemistry and material performance issues. II. THE DESIGNER DILEMMA The quantification of uncertainties is a crucial step in different phases of a nuclear system design. In a pre- liminary (conceptual) design phase, the comparison of * Corresponding author: salvatoresmassimo@orange.fr calculation scheme (nuclear data and modelling) a-priori uncertainties with the target accuracies for the most im- portant design parameters, allows to define needs and priorities for calculation scheme improvement and uncer- tainty reduction. The designer analysis establishes the quantified penalties due to uncertainties beyond the tar- get accuracy range and their impact on the design (e.g. extra margins on fuel performances, choice of alternative or back-up solutions etc.). Successively, and in parallel with preliminary design, the choice of the most adapted approach to uncertainty reduction could be done accord- ing to timeframe, project schedule etc., but also accord- ing to safety requirements (e.g. demonstration of vali- dated uncertainties). In view of their impact, the un- certainty analysis requires a reliability assessment of the uncertainty data that have been used. The choice of the appropriate approach can be a dilemma for the designer. In practically all case, the uncertainty quantification for design will imply the use of experiments (past experi- ments or ad-hoc experiments still to be performed): • Performance of a full series of design oriented exper- iments (critical mass, reaction rate distributions, reactivity coefficients, control rod worth, etc.) in a representative reactor mock-up. This is the most ambitious (in terms of resources deployment), but not necessarily the most effective or even feasi- ble approach (facility availability, cost, difficulty to achieve representativity etc.). If available, the uncertainty reduction by integral parameter R is a function of the a-priori covariance data D [4] ΔR ′2 0 =ΔR 2 0 · ( 1 − r 2 RE ) , (1) r RE = ( S + R DS E ) ( S + R DS R )( S + E DS E ) , (2) where the S E and the S R are the sensitivity vectors