Reduced Basis Approaches in Time-Dependent Non-Coercive Settings for Modelling the Movement of Nuclear Reactor Control Rods

In this work, two approaches, based on the certified Reduced Basis method, have been developed for simulating the movement of nuclear reactor control rods, in time-dependent non-coercive settings featuring a 3D geometrical framework. In particular, in a first approach, a piece-wise affine transformation based on subdomains division has been implemented for modelling the movement of one control rod. In the second approach, a “staircase” strategy has been adopted for simulating the movement of all the three rods featured by the nuclear reactor chosen as case study. The neutron kinetics has been modelled according to the so-called multi-group neutron diffusion, which, in the present case, is a set of ten coupled parametrized parabolic equations (two energy groups for the neutron flux, and eight for the precursors). Both the reduced order models, developed according to the two approaches, provided a very good accuracy compared with high-fidelity results, assumed as “truth” solutions. At the same time, the computational speed-up in the Online phase, with respect to the fine “truth” finite element discretization, achievable by both the proposed approaches is at least of three orders of magnitude, allowing a real-time simulation of the rod movement and control. AMS subject classifications: 65M12, 65Y20, 49M25

[1]  Gianluigi Rozza,et al.  A Reduced Order Model for Multi-Group Time-Dependent Parametrized Reactor Spatial Kinetics , 2014 .

[2]  Christophe Geuzaine,et al.  Gmsh: A 3‐D finite element mesh generator with built‐in pre‐ and post‐processing facilities , 2009 .

[3]  J. Hesthaven,et al.  Reduced Basis Approximation and A Posteriori Error Estimation for Parametrized Partial Differential Equations , 2007 .

[4]  P. Holmes,et al.  Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 1996 .

[5]  A. Chatterjee An introduction to the proper orthogonal decomposition , 2000 .

[6]  J. Lamarsh Introduction to Nuclear Engineering , 1975 .

[7]  Jacques-Louis Lions,et al.  Mathematical Analysis and Numerical Methods for Science and Technology: Volume 5 Evolution Problems I , 1992 .

[8]  G. Rozza,et al.  Comparison and combination of reduced-order modelling techniques in 3D parametrized heat transfer problems , 2011 .

[9]  A. Patera,et al.  A Successive Constraint Linear Optimization Method for Lower Bounds of Parametric Coercivity and Inf-Sup Stability Constants , 2007 .

[10]  E. Gadioli,et al.  Introductory Nuclear Physics , 1997 .

[11]  Gianluigi Rozza,et al.  Reduced basis approximation and a posteriori error estimation for the time-dependent viscous Burgers’ equation , 2009 .

[12]  A. Patera,et al.  A posteriori error bounds for reduced-basis approximations of parametrized parabolic partial differential equations , 2005 .

[13]  R. Chaplin NUCLEAR REACTOR THEORY , 2022 .

[14]  Anthony T. Patera,et al.  A natural-norm Successive Constraint Method for inf-sup lower bounds , 2010 .

[15]  Allan F. Henry,et al.  Nuclear Reactor Analysis , 1977, IEEE Transactions on Nuclear Science.

[16]  Clarence E. Lee Nuclear Reactor Kinetics and Control , 1982 .

[17]  Maria Pusa,et al.  BURNUP CALCULATION CAPABILITY IN THE PSG2 / SERPENT MONTE CARLO REACTOR PHYSICS CODE , 2009 .

[18]  B. Haasdonk,et al.  REDUCED BASIS METHOD FOR FINITE VOLUME APPROXIMATIONS OF PARAMETRIZED LINEAR EVOLUTION EQUATIONS , 2008 .

[19]  Benjamin S. Kirk,et al.  Library for Parallel Adaptive Mesh Refinement / Coarsening Simulations , 2006 .

[20]  A. Patera,et al.  Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations , 2007 .

[21]  Franziska Wulf,et al.  Introduction To Nuclear Engineering , 2016 .

[22]  N. Nguyen,et al.  An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations , 2004 .

[23]  Timothy Abram,et al.  A Technology Roadmap for Generation-IV Nuclear Energy Systems, USDOE/GIF-002-00 , 2002 .

[24]  Gianluigi Rozza,et al.  Fundamentals of reduced basis method for problems governed by parametrized PDEs and applications , 2014 .

[25]  Gianluigi Rozza,et al.  Certified reduced basis approximation for parametrized partial differential equations and applications , 2011 .

[26]  A. Quarteroni,et al.  Numerical Approximation of Partial Differential Equations , 2008 .

[27]  Gianluigi Rozza,et al.  Reduced Basis Approximation and a Posteriori Error Estimation for Parametrized Parabolic PDEs: Application to Real‐Time Bayesian Parameter Estimation , 2010 .

[28]  J. Lewins Nuclear reactor kinetics and control , 1978 .

[29]  Yvon Maday,et al.  Reduced basis method for the rapid and reliable solution of partial differential equations , 2006 .

[30]  G. Rozza,et al.  ON THE APPROXIMATION OF STABILITY FACTORS FOR GENERAL PARAMETRIZED PARTIAL DIFFERENTIAL EQUATIONS WITH A TWO-LEVEL AFFINE DECOMPOSITION , 2012 .

[31]  R. Jacqmin,et al.  THE JEFF-3.0 NUCLEAR DATA LIBRARY , 2002 .

[32]  Gianluigi Rozza,et al.  Comparison of a Modal Method and a Proper Orthogonal Decomposition approach for multi-group time-dependent reactor spatial kinetics , 2014 .

[33]  Gianluigi Rozza,et al.  Computational Reduction for Parametrized PDEs: Strategies and Applications , 2012 .

[34]  John W. Peterson,et al.  A high-performance parallel implementation of the certified reduced basis method , 2011 .

[35]  G. Rozza,et al.  A multi-physics reduced order model for the analysis of Lead Fast Reactor single channel , 2016 .