Assessing the Predictive Capability of the LIFEIV Nuclear Fuel Performance Code Using Sequential Calibration PDF Download

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

Get Book Here

Book Description
This report considers the problem of calibrating a numerical model to data from an experimental campaign (or series of experimental tests). The issue is that when an experimental campaign is proposed, only the input parameters associated with each experiment are known (i.e. outputs are not known because the experiments have yet to be conducted). Faced with such a situation, it would be beneficial from the standpoint of resource management to carefully consider the sequence in which the experiments are conducted. In this way, the resources available for experimental tests may be allocated in a way that best 'informs' the calibration of the numerical model. To address this concern, the authors propose decomposing the input design space of the experimental campaign into its principal components. Subsequently, the utility (to be explained) of each experimental test to the principal components of the input design space is used to formulate the sequence in which the experimental tests will be used for model calibration purposes. The results reported herein build on those presented and discussed in [1,2] wherein Verification & Validation and Uncertainty Quantification (VU) capabilities were applied to the nuclear fuel performance code LIFEIV. In addition to the raw results from the sequential calibration studies derived from the above, a description of the data within the context of the Predictive Maturity Index (PMI) will also be provided. The PMI [3,4] is a metric initiated and developed at Los Alamos National Laboratory to quantitatively describe the ability of a numerical model to make predictions in the absence of experimental data, where it is noted that 'predictions in the absence of experimental data' is not synonymous with extrapolation. This simply reflects the fact that resources do not exist such that each and every execution of the numerical model can be compared against experimental data. If such resources existed, the justification for numerical models would be reduced considerably. The authors note that the PMI is primarily intended to provide a high-level, quantitative description of year-to-year (or version-to-version) improvements in numerical models, where these descriptions can be used as a means of justifying funding requests to support further model development research. It is in this context that the present report should be considered: the availability of data from experimental tests should be viewed as a time-dependent variable, where experiments are added to the calibration suite as resources become available. For the present report, the experimental data is of course already available (permitting demonstration of the proposed methodology). Furthermore, the authors are not proposing this methodology as the answer to the question of how to allocate resources for experimental tests, and readers are directed to [5] and the references contained in Section 1 of [5] for additional information on the subject. However, the strength of this methodology is that it offers a means by which to select the sequence of experiments in a pre-arranged experimental campaign (a situation for which the methods discussed in [5] are less appropriate). The report is organized as follows. Section 2 describes the methodology employed to formulate the sequences of experiments for the calibrations performed for this study. Section 3 then presents the results associated with two sequences; supplementary results are provided in the Appendix. The report then concludes in Section 4 with a brief summary.