Benchmark selection methodology for reactor calculations and nuclear data uncertainty reduction E. Alhassan a , H. Sj¨ ostrand a , P. Helgesson a , M. ¨ Osterlund a , S. Pomp a , A.J. Koning a,b , D. Rochman c a Division of Applied Nuclear Physics, Department of Physics and Astronomy, Uppsala University, Uppsala, Sweden b Nuclear Research and Consultancy Group (NRG), Petten, The Netherlands c Paul Scherrer Institut, 5232 Villigen, Switzerland Abstract Criticality, reactor physics and shielding benchmarks are expected to play im- portant roles in GEN-IV design, safety analysis and in the validation of an- alytical tools used to design these reactors. For existing reactor technology, benchmarks are used for validating computer codes and for testing nuclear data libraries. Given the large number of benchmarks available, selecting these benchmarks for specific applications can be rather tedious and difficult. Un- til recently, the selection process has been based usually on expert judgement which is dependent on the expertise and the experience of the user and thereby introducing a user bias into the process. This approach is also not suitable for the Total Monte Carlo methodology which lays strong emphasis on automation, reproducibility and quality assurance. In this paper a method for selecting these benchmarks for reactor calculation and for nuclear data uncertainty reduction based on the Total Monte Carlo (TMC) method is presented. For reactor code validation purposes, similarities between a real reactor application and one or several benchmarks are quantified using a similarity index while the Pearson correlation coefficient is used to select benchmarks for nuclear data uncertainty reduction. Also, a correlation based sensitivity method is used to identify the sensitivity of benchmarks to particular nuclear reactions. Based on the bench- mark selection methodology, two approaches are presented for reducing nuclear data uncertainty using integral benchmark experiments as an additional con- straint in the TMC method: a binary accept/reject and a method of assigning file weights using the likelihood function. Finally, the methods are applied to a full lead-cooled fast reactor core and a set of criticality benchmarks. Significant reductions in 239 Pu and 208 Pb nuclear data uncertainties were obtained after implementing the two methods with some benchmarks. Keywords: Benchmarks selection, binary accept/reject, file weights, nuclear Email addresses: erwin.alhassan@physics.uu.se (E. Alhassan), henrik.sjostrand@physics.uu.se (H.Sj¨ostrand) Preprint submitted to Annals of Nuclear Energy May 21, 2015