Application of multiphysics model order reduction to doppler/neutronic feedback

Abstract

In this paper, a proper orthogonal decomposition based reduced-order model is presented for parametrized multiphysics computations. Our application physics is Doppler feedback in a simplified model of the molten salt fast reactor concept. The reduced model is created using the method of snapshots where the offline training set is obtained by exercising a full-order model created with the OpenFOAM based multiphysics solver, GeN-Foam. The steady state models solve the multi-group diffusion k-eigenvalue equations with moving precursors together with the energy equation. A fixed velocity field is assumed throughout the computations, hence the momentum and continuity equations are not solved. The discrete empirical interpolation method is used for the efficient coupling of the ROM solvers, while the input parameter space is surveyed using the improved distributed latin hypercube sampling algorithm.

Adjoint‐based sensitivity analysis of circulating liquid fuel system for the multiphysics model of molten salt reactor

“…System response R can be defined as the sum of functionals of solutions such as flux, temperature, and concentration of all groups of delayed neutron precursors and decay heat precursors in steady-state as Equation (19), where P X , P Z , P Yi , and P Wi are dependent on the definitions from which type of system response is of interests. For instance, these can be functions related to instrumental or measurement characteristics.…”

“…Especially for MSR, the influence from the coupled physics such as reactivity feedback from temperature or density changes of moving fuel salt is the issue to assess how much sensitive the system response is. Several sensitivity analyses for MSR are performed to evaluate multiphysics model using a reduced order model to reflect the important system behavior and to identify potential bias from nuclear data uncertainty with sensitivity and uncertainty analysis technique …”

Reduced order models for thermal radiative transfer problems based on moment equations and data-driven approximations of the Eddington tensor

“…Neural networks have been used to obtain closures for P N -type systems of moment equations of the BTE [62,63]. Data-driven ROMs have also been created for particle transport problems in nuclear reactor-physics applications [64,65,66,67], including (i) pin-by-pin reactor calculations [68], (ii) reactor kinetics [69,70], (iii) molten salt fast reactor problems [71,72,73], (iv) problems with feedback from delayed neutron precursors [74,75,76], (v) problems with domain decomposition [77], and (vi) for generation of neutron flux and cross sections for light water reactors [78].…”

“…A new approach based on data-driven reduced-order models (ROMs) has been gaining popularity in recent years which make use of data-based methodologies to dimensionality reduction. Data-driven models have been developed for (i) linear particle transport problems [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48] (ii) nonlinear RT problems [49,50,51,52,53,54,55,56,57,58,59,60], and (iii) various problems in nuclear reactor-physics [61,62,63,64,65,66,67,68,69,70,71]. The fundamental idea behind these ROMs is to leverage databases of solutions to their problems of interest (known a-priori) to develop some reduction in the dimensionality for their involved equations.…”

Frequency Limited & Weighted Model Reduction Algorithm With Error Bound: Application to Discrete-Time Doubly Fed Induction Generator Based Wind Turbines for Power System

“…Furthermore, the growing complexity of mathematical models used to predict real-world systems, such as climate systems, the human cardiovascular system, the hydraulic systems, image systems, nuclear reactors, etc. [16]- [19], has led to the usefulness of the model order reduction, which means creating a systematic approach for replacing complicated models with far less complicated ones. The model order reduction emphasis as in various following fields such as…”