Optimal and fast field reconstruction with reduced basis and limited observations: Application to reactor core online monitoring

Abstract The fast reconstruction of neutronic field in a nuclear core using reduced modeling and limited observations has attracted considerable attention. In particular, four design parameters are considered for developing efficient and robust field reconstruction in this framework, including the choice of reduced basis, the order of reduced dimension, the number of sensors and their placement. In this work, a systematic study has been brought to show the effect of these design parameters on the performance of field reconstruction by investigating i) five basis selection methods and ii) five different sensor placement methods. From a series of practical engineering applications based on HPR1000 reactor core, it is observed that the POD models and the sensor placement determined by the discrete empirical interpolation method and the oversampled discrete empirical interpolation method provide an optimal reconstruction, where the computational complexity, the accuracy, and the robustness to noise are well-balanced. The result is helpful for the practical implementation of the neutronic field reconstruction methods with reduced basis and limited observations in nuclear reactor cores.

[1]  Nathan E. Murray,et al.  An application of Gappy POD , 2006 .

[2]  N. Nguyen,et al.  A general multipurpose interpolation procedure: the magic points , 2008 .

[3]  Danny C. Sorensen,et al.  Nonlinear Model Reduction via Discrete Empirical Interpolation , 2010, SIAM J. Sci. Comput..

[4]  Eugenia Kalnay,et al.  Atmospheric Modeling, Data Assimilation and Predictability , 2002 .

[5]  Benjamin Stamm,et al.  Comparison of some Reduced Representation Approximations , 2013, 1305.5066.

[6]  A. Cohen,et al.  Model Reduction and Approximation: Theory and Algorithms , 2017 .

[7]  Siep Weiland,et al.  Missing Point Estimation in Models Described by Proper Orthogonal Decomposition , 2004, IEEE Transactions on Automatic Control.

[8]  Yvon Maday,et al.  PBDW State Estimation: Noisy Observations; Configuration-Adaptive Background Spaces; Physical Interpretations , 2015 .

[9]  Yingrui Yu,et al.  Reactor power distribution detection and estimation via a stabilized gappy proper orthogonal decomposition method , 2020 .

[10]  Steven L. Brunton,et al.  Dynamic mode decomposition - data-driven modeling of complex systems , 2016 .

[11]  A. Patera,et al.  A PRIORI CONVERGENCE OF THE GREEDY ALGORITHM FOR THE PARAMETRIZED REDUCED BASIS METHOD , 2012 .

[12]  Sondipon Adhikari,et al.  Linear system identification using proper orthogonal decomposition , 2007 .

[13]  Yongqiang Ma,et al.  Development and validation of reactor nuclear design code CORCA-3D , 2019, Nuclear Engineering and Technology.

[14]  Karen Veroy,et al.  Reduced basis approximation and a posteriori error bounds for 4D-Var data assimilation , 2018, Optimization and Engineering.

[15]  Marc Bocquet,et al.  Data Assimilation: Methods, Algorithms, and Applications , 2016 .

[16]  B. Bouriquet,et al.  Stabilization of (G)EIM in presence of measurement noise: application to nuclear reactor physics , 2016, 1611.02219.

[17]  K. Taira,et al.  Super-resolution reconstruction of turbulent flows with machine learning , 2018, Journal of Fluid Mechanics.

[18]  Peter Benner,et al.  Comparison of model order reduction methods for optimal sensor placement for thermo-elastic models* , 2019 .

[19]  Lawrence Sirovich,et al.  Karhunen–Loève procedure for gappy data , 1995 .

[20]  Jean-Philippe Argaud,et al.  Variational assimilation for xenon dynamical forecasts in neutronic using advanced background error covariance matrix modelling , 2013, 1304.6836.

[21]  Anthony T. Patera,et al.  A Priori Convergence Theory for Reduced-Basis Approximations of Single-Parameter Elliptic Partial Differential Equations , 2002, J. Sci. Comput..

[22]  Gianluigi Rozza,et al.  Generalized Reduced Basis Methods and n-Width Estimates for the Approximation of the Solution Manifold of Parametric PDEs , 2013 .

[23]  Qing Li,et al.  An inverse-distance-based fitting term for 3D-Var data assimilation in nuclear core simulation , 2020 .

[24]  D. Sorensen,et al.  A Survey of Model Reduction Methods for Large-Scale Systems , 2000 .

[25]  Steven L. Brunton,et al.  Shallow neural networks for fluid flow reconstruction with limited sensors , 2020, Proceedings of the Royal Society A.

[26]  Albert Cohen,et al.  Greedy Algorithms for Optimal Measurements Selection in State Estimation Using Reduced Models , 2018, SIAM/ASA J. Uncertain. Quantification.

[27]  Antonio A. Alonso,et al.  Optimal sensor placement for state reconstruction of distributed process systems , 2004 .

[28]  Jean-Philippe Argaud,et al.  ICONE19-43013 OPTIMAL DESIGN OF MEASUREMENT NETWORK FOR NEUTRONIC ACTIVITY FIELD RECONSTRUCTION BY DATA ASSIMILATION , 2011, 1104.2120.

[29]  Gianluigi Rozza,et al.  Model Order Reduction , 2021 .

[30]  Jean-Philippe Argaud,et al.  Background error covariance iterative updating with invariant observation measures for data assimilation , 2019, Stochastic Environmental Research and Risk Assessment.

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

[32]  Xiaodong Sun,et al.  Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments , 2019 .

[33]  Wolfgang Dahmen,et al.  Data Assimilation in Reduced Modeling , 2015, SIAM/ASA J. Uncertain. Quantification.

[34]  Karen Willcox,et al.  A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems , 2015, SIAM Rev..

[35]  Gianluigi Rozza,et al.  Model Order Reduction: a survey , 2016 .

[36]  Scott T. M. Dawson,et al.  Model Reduction for Flow Analysis and Control , 2017 .

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

[38]  G. Karniadakis,et al.  Efficient sensor placement for ocean measurements using low-dimensional concepts , 2009 .

[39]  Anthony Nouy,et al.  Low-rank methods for high-dimensional approximation and model order reduction , 2015, 1511.01554.

[40]  Helin Gong,et al.  Data assimilation with reduced basis and noisy measurement : Applications to nuclear reactor cores. (Couplage de réduction de modèles et de mesures bruitées : Applications à l'assimilation de données pour les cœurs de centrales nucléaires) , 2018 .

[41]  Steven L. Brunton,et al.  Robust flow reconstruction from limited measurements via sparse representation , 2018, Physical Review Fluids.

[42]  Mario Ohlberger,et al.  Reduced Basis Methods: Success, Limitations and Future Challenges , 2015, 1511.02021.

[43]  Maria-Vittoria Salvetti,et al.  A non-linear observer for unsteady three-dimensional flows , 2008, J. Comput. Phys..

[44]  Wolfgang Dahmen,et al.  Convergence Rates for Greedy Algorithms in Reduced Basis Methods , 2010, SIAM J. Math. Anal..

[45]  J. K. Hammond,et al.  PBDW: A non-intrusive Reduced Basis Data Assimilation method and its application to an urban dispersion modeling framework , 2019, Applied Mathematical Modelling.

[46]  Jean-Philippe Argaud,et al.  Sensor placement in nuclear reactors based on the generalized empirical interpolation method , 2018, J. Comput. Phys..

[47]  Christopher C. Pain,et al.  A POD reduced‐order model for eigenvalue problems with application to reactor physics , 2013 .

[48]  K. Willcox,et al.  Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition , 2004 .

[49]  Balaji Jayaraman,et al.  Interplay of Sensor Quantity, Placement and System Dimension in POD-Based Sparse Reconstruction of Fluid Flows , 2019 .

[50]  Karen Willcox,et al.  An Accelerated Greedy Missing Point Estimation Procedure , 2016, SIAM J. Sci. Comput..

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

[52]  L. Sirovich Turbulence and the dynamics of coherent structures. II. Symmetries and transformations , 1987 .

[53]  Xiaodong Sun,et al.  Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach , 2019 .

[54]  P. Schmid,et al.  Dynamic mode decomposition of numerical and experimental data , 2008, Journal of Fluid Mechanics.

[55]  Stefano Discetti,et al.  On PIV random error minimization with optimal POD-based low-order reconstruction , 2015, Experiments in Fluids.

[56]  K. Willcox Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition , 2004 .

[57]  Steven L. Brunton,et al.  Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns , 2017, IEEE Control Systems.

[58]  Anthony T. Patera,et al.  The Generalized Empirical Interpolation Method: Stability theory on Hilbert spaces with an application to the Stokes equation , 2015 .

[59]  N. Nguyen,et al.  EFFICIENT REDUCED-BASIS TREATMENT OF NONAFFINE AND NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS , 2007 .

[60]  Jean-Philippe Argaud,et al.  Data Assimilation in Nuclear Power Plant Core , 2010 .

[61]  J. Hesthaven,et al.  Certified Reduced Basis Methods for Parametrized Partial Differential Equations , 2015 .

[62]  Xingjie Peng,et al.  A data-driven strategy for xenon dynamical forecasting using dynamic mode decomposition , 2020 .

[63]  Olga Mula,et al.  State estimation with nonlinear reduced models. Application to the reconstruction of blood flows with Doppler ultrasound images , 2019, 1904.13367.

[64]  Anthony T. Patera,et al.  A parameterized‐background data‐weak approach to variational data assimilation: formulation, analysis, and application to acoustics , 2015 .

[65]  Jian Yu,et al.  Flowfield Reconstruction Method Using Artificial Neural Network , 2019, AIAA Journal.

[66]  H. Weyl Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung) , 1912 .

[67]  Ronald DeVore,et al.  Greedy Algorithms for Reduced Bases in Banach Spaces , 2012, Constructive Approximation.

[68]  Wolfgang Dahmen,et al.  Optimal Reduced Model Algorithms for Data-Based State Estimation , 2019, SIAM J. Numer. Anal..

[69]  Laurent David,et al.  APPLICATION OF KALMAN FILTERING AND PARTIAL LEAST SQUARE REGRESSION TO LOW ORDER MODELING OF UNSTEADY FLOWS , 2013 .

[70]  A. Quarteroni,et al.  Reduced Basis Methods for Partial Differential Equations: An Introduction , 2015 .

[71]  Zlatko Drmac,et al.  A New Selection Operator for the Discrete Empirical Interpolation Method - Improved A Priori Error Bound and Extensions , 2015, SIAM J. Sci. Comput..

[72]  Vassilios Theofilis,et al.  Modal Analysis of Fluid Flows: An Overview , 2017, 1702.01453.

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

[74]  Y. Maday,et al.  A generalized empirical interpolation method : application of reduced basis techniques to data assimilation , 2013, 1512.00683.

[75]  Jean-Philippe Argaud,et al.  Nuclear core activity reconstruction using heterogeneous instruments with data assimilation , 2015 .