Russian experimental database for validation of computer codes used for safety analysis of nuclear facilities

Abstract As a result of past decades of nuclear industry development there have been created hundreds of unique experimental facilities with thousands of experimental studies carried out. The results of such studies form the scientific legacy of the nuclear power industry. The article describes Russian approach for keeping this scientific legacy available for the current and future generations of researches especially for those ones who interested in development, verification and validation of computer codes used for safety analysis of nuclear installations. In 2017 Russian nuclear regulatory authority (Rostechnadzor) and State Atomic Energy Corporation (Rosatom) agreed to combine their efforts on building the modern digital database of experimental facilities and the respective experimental data which describes process and phenomena in nuclear installations. The development of the database is being performed by the Scientific and Engineering Centre for Nuclear and Radiation Safety (SEC NRS) as a technical support organization (TSO) of Rostechnadzor. To design the structure and format of the Russian experimental database, the analysis of the best practices of international and national databases were carried out. To provide criteria for assessment of quality of the experimental data, regulatory requirements to the verification and validation of the computer codes, including the metrological requirements to measurements in the field of atomic energy use, were analyzed. The results of these analysis are presented in the article. It is shown that assessment of the already existing experimental data will help to understand, which topical areas have lack of high fidelity and modeling-oriented experimental data. The development of the requirements to the experiments with respect to the computer code developers needs could reduce the risk to obtain the poor quality experimental data in future.

[1]  E. Royer,et al.  The 1995 look-up table for critical heat flux in tubes , 1996 .

[2]  V. Yagov,et al.  Heat transfer during cooling of high temperature spheres in subcooled water at different pressures , 2017 .

[3]  Youho Lee,et al.  Application of machine learning for prediction of critical heat flux: Support vector machine for data-driven CHF look-up table construction based on sparingly distributed training data points , 2018 .

[4]  G. M. Galassi,et al.  Preservation and Use of Integral System Test Facilities Data: the Experience of the Lobi Data and the Stresa Database , 2012 .

[5]  A. Durmayaz,et al.  The 2006 CHF look-up table , 2007 .

[6]  V. Yagov,et al.  Film boiling of subcooled liquids. Part I: Leidenfrost phenomenon and experimental results for subcooled water , 2016 .

[7]  P. L. Kirillov.  Addendum and comments to the paper 1995 look-up table for calculating critical heat flux in tubes , 1997 .

[8]  Timothy G. Trucano,et al.  Verification and validation benchmarks , 2008 .

[9]  Hyung Lee,et al.  Nuclear Energy -- Knowledge Base for Advanced Modeling and Simulation (NE-KAMS) Code Verification and Validation Data Standards and Requirements: Fluid Dynamics Version 1.0 , 2011 .

[10]  M. Corradini,et al.  Presentation and comparison of experimental critical heat flux data at conditions prototypical of light water small modular reactors , 2017 .

[11]  Mohammad Hassan Moradi,et al.  Empirical correlation study of dryout heat transfer at high pressure using high order neural network and feed forward neural network , 2011 .

[12]  Joshua S. Kaizer,et al.  An overview of measurements, data compilations and prediction methods for the critical heat flux in water-cooled tubes , 2018 .