Abstract — Approximately three hundred experts from more than 30 countries traveled to Lucca, Italy, to attend BEPU2018, which was sponsored by the American Nuclear Society, the Nuclear Energy Agency, and the International Atomic Energy Agency and was also cosponsored by a local organizing committee led by Nuclear and Industrial Engineering. Over 250 draft papers were reviewed, and finally, a grand total of over 170 full papers were accepted and presented in technical sessions. In addition, 21 invited keynote lectures, 13 plenary speeches, and 6 panel discussions addressed the state-of-the-art challenges in various areas of BEPU. The BEPU technical program committee and the special issue guest editors then coordinated efforts to select a limited number of papers and invited keynote and plenary lectures for consideration for archival publication in leading scientific journals. The authors were then invited to update their papers before submitting them for additional peer review for these journal special issues.
The papers in this special issue may be collected into four groups:

1. General considerations about the BEPU approach

2. Development of BEPU methods and techniques

3. Applications of BEPU methods

4. Development of multiphysics, multiscale approaches

We hope you enjoy this special issue of Nuclear Technology and look forward to seeing you at the next BEPU conference in Sicily, Italy, in May 2020.

https://www.tandfonline.com/eprint/EDCTUQBAVWVPKK4ANCM8/full?target=10.1080/00295450.2019.1676080 

 
Abstract — Predictive Modeling Methodology constitutes an innovative approach to perform uncertainty analysis, which reduces the subjectivity and the user-defined way to manage experimental data and derive uncertainty of input parameters that instead characterize the Propagation of Input Uncertainties and/or Propagation of Output Accuracies methods.

The CASUALIDAD (Code with the capability of Adjoint Sensitivity and Uncertainty AnaLysis by Internal Data ADjustment and assimilation) method can be developed as a fully deterministic method based on advanced mathematical tools for performing internally to the thermal-hydraulic system code the sensitivity and the uncertainty analysis.  The method is based upon powerful mathematical tools to perform sensitivity analysis and upon the Data Adjustment/Assimilation methodology by which experimental observations are combined with code predictions and their respective errors through the application of the Bayesian Theorem and of the Principle of the Maximum Likelihood, to provide an improved estimate of the system state and of the associated uncertainty considering all input parameters that affect any prediction.

The methodology has been structured in two main steps. The former has the aim to generate the database of improved estimations starting from the available set of experimental data and related qualified calculations; the latter is dealing with the use of the selected (from the obtained database) set of improved estimations for the uncertainty evaluation of the predicted Nuclear Power Plant (NPP) transient scenario.

The proposed methodology clearly interrelates in a consistent and robust framework, the code validation issue with the evaluation of the uncertainty of code responses passing through the quantification of input uncertainty parameters of code models and thus constituting a step forward respect to the subjectivity of the current methods based on Propagation of Input Uncertainties and/or Propagation of Output Accuracies.
https://www.tandfonline.com/eprint/89WX73M2IY35TTSPRHS2/full?target=10.1080%2F00295450.2019.1632092&

Abstract —  Taking into account uncertainties is a key issue in nuclear power plant safety analysis using best estimate plus uncertainty methodologies. It involves two main types of treatment depending on the variables of interest: input parameters or system response quantity. The OECD/NEA PREMIUM project devoted to the first type of variables has shown that inverse methods for input uncertainty quantification can exhibit strong user-effect. One of the main reasons was the lack of a clear guidance to perform a reliable analysis. This work is precisely devoted to the development of a first good practice guidance document for quantification of thermal-hydraulic code model input uncertainty. The developments have been done in the framework of the OECD/NEA SAPIUM project (January 2017–September 2019). This paper provides a summary of the main project outcome. Recommendations and open issues for future developments are also given.

https://www.sciencedirect.com/science/article/pii/S0029549319301839?dgcid=coauthor

Abstract —  PREMIUM (Post BEMUSE Reflood Models Input Uncertainty Methods) was an activity launched with the aim of pushing forward the methods of quantification of physical model uncertainties in thermal-hydraulic codes. The benchmark PREMIUM was addressed to all who apply uncertainty evaluation methods based on input uncertainties quantification and propagation. The benchmark was based on a selected case of uncertainty analysis application to the simulation of quench front propagation in an experimental test facility. Applied to an experiment, enabled evaluation and confirmation of the quantified probability distribution functions on the basis of experimental data. The scope of the benchmark comprised a review of the existing methods, selection of potentially important uncertain input parameters, quantification of the ranges and distributions of the identified parameters using experimental results of tests performed on the FEBA test facility, verification of the performed quantification on the basis of tests performed at the FEBA test facility and validation on the basis of blind calculations of the Reflood 2-D PERICLES experiment. The benchmark has shown dependency of the results on the applied methodology and a strong user effect. The conclusion was that a systematic approach for the quantification of model uncertainties is necessary.

https://www.sciencedirect.com/science/article/pii/S0029549319302080?dgcid=coauthor

Design-Basis Accident Analysis Methods for Light-Water Nuclear Power Plants
Modern Nuclear Energy Analysis Methods - Vol.3, 2019
World Scientific