Development of a Trajectory Period Folding Method for Burnup Calculations

In this paper, we present a trajectory period folding method for numerical modelling of nuclear transformations. The method uses the linear chain method, commonly applied for modelling of isotopic changes in matter. The developed method folds two consecutive periods of time and forms linear chain representations. In the same way as in the linear chain method, the mass flow of straight nuclide-to-nuclide transitions following the formation of nuclide transmutation chains in every step is considered over the total period of interest. Therefore, all quantitative data about the isotopic transformations for the period beyond a particular calculation step are preserved. Moreover, it is possible to investigate the formation history of any isotope from the beginning of irradiation to the arbitrary time step, including cooling periods and multi-recycling for any designed nuclear fuel cycle. We present a case study for the transition from 238U to 239Pu and define the properties of the developed method and its possible applications in reactor physics calculations.

[1]  J. Cetnar,et al.  Flattening of the Power Distribution in the HTGR Core with Structured Control Rods , 2021, Energies.

[2]  D. Cacuci.  High-Order Deterministic Sensitivity Analysis and Uncertainty Quantification: Review and New Developments , 2021, Energies.

[3]  M. Oettingen.  Assessment of the Radiotoxicity of Spent Nuclear Fuel from a Fleet of PWR Reactors , 2021, Energies.

[4]  Jerzy Cetnar,et al.  Linear Chain Method for Numerical Modelling of Burnup Systems , 2021, Energies.

[5]  J. Cetnar,et al.  Radionuclide neutron source trajectories in the closed nuclear fuel cycle , 2019, Nukleonika.

[6]  P. V. Subhash,et al.  Simplified recursive relations for the derivatives of Bateman linear chain solution and their application to sensitivity and multi-point analysis , 2018, Annals of Nuclear Energy.

[7]  Jerzy Cetnar,et al.  Statistical error propagation in HTR burnup model , 2017 .

[8]  T. Takeda.  Minor actinides transmutation performance in a fast reactor , 2016 .

[9]  Jerzy Cetnar,et al.  Monte Carlo modeling of Lead-Cooled Fast Reactor in adiabatic equilibrium state , 2016 .

[10]  P. Blaise,et al.  Nuclear data uncertainties propagation methods in Boltzmann/Bateman coupled problems: Application to reactivity in MTR , 2016 .

[11]  Kai Huang,et al.  Improvements to the Transmutation Trajectory Analysis of depletion evaluation , 2016 .

[12]  P. Stanisz,et al.  Validation of VHTRC calculation benchmark of critical experiment using the MCB code , 2016 .

[13]  Tomasz Mirowski,et al.  The MCB code for numerical modeling of Fourth Generation nuclear reactors , 2015, Comput. Sci..

[14]  J. Cetnar,et al.  Comparative analysis between measured and calculated concentrations of major actinides using destructive assay data from Ohi-2 PWR , 2015 .

[15]  Jerzy Cetnar,et al.  Validation of gadolinium burnout using PWR benchmark specification , 2014 .

[16]  P. Aarnio,et al.  Comparison of depletion algorithms for large systems of nuclides , 2011 .

[17]  M. Salvatores,et al.  Radioactive waste partitioning and transmutation within advanced fuel cycles: Achievements and Challenges , 2011 .

[18]  C. Artioli,et al.  A new paradigm for core design aimed at the sustainability of nuclear energy: The solution of the extended equilibrium state , 2010 .

[19]  Tomohiro Endo,et al.  Effect of error propagation of nuclide number densities on Monte Carlo burn-up calculations , 2006 .

[20]  J. Cetnar.  General solution of Bateman equations for nuclear transmutations , 2006 .