Improvement of Nodal Accuracy by Using Albedo-Corrected Parameterized Equivalence Constants

Kim, Woosong; Heo, Woong; Kim, Yong Hee

doi:10.1080/00295639.2017.1354592

Cited by 14 publications

(2 citation statements)

References 5 publications

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“…Even though nodal algorithms are the older class of methods for parameter prediction of the reactor physics (for example, a 1995 doctoral dissertation by Fitzpatrick [182] was concerned with nodal reactor analysis tools for hexagonal geometry, i.e., with the core being a grid of hexagonal cells [183] instead of square cells: originally, hexagonal geometry used to be favoured at Soviet reactors), new versions of nodal methods keep emerging [184][185][186][187][188][189][190] In a paper published in 2018, Gamarino et al explain [184]:…”

Section: Validation By Simulations Of the Reactor Physicsmentioning

confidence: 99%

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Nissan

2019

Designs

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An important aspect of managing a nuclear reactor is how to design refuellings, and from the 1980s to the present different artificial intelligence (AI) techniques have been applied to this problem. A section of the reactor core resembles a symmetrical grid; long fuel assemblies are inserted there, some of them new, some of them partly spent. Rods of “burnable poisons” dangle above, ready to be inserted into the core, in order to stop the reactor. Traditionally, manual design was made by shuffling positions in the grid heuristically, but AI enabled to automatically generate families of candidate configurations, under safety constraints, as well as in order to optimize combustion, with longer cycles of operation between shutdown periods, thus delaying the end-of-cycle point (except in France, where shutdown is on an annual basis, and Canada, where individual fuel assemblies are replaced, with no need for shutdown for rearranging the entire batch). Rule-based expert systems, the first being FUELCON,1 were succeeded by projects combining neural and rule-based processing (a symbolic-to-neural compilation of rules we did not implement), and later on, genetic algorithms in FUELGEN.2 In the literature, one also comes across the application of fuzzy techniques, tabu search, cellular automata and simulated annealing, as well as particle swarms. Safety regulations require simulating the results using a parameter prediction tool; this is done using either nodal algorithms, or neural processing.

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Section: Validation By Simulations Of the Reactor Physicsmentioning

confidence: 99%

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Nissan

2019

Designs

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“…Recently, a variant of this approach has been proposed. 13 When using this kind of method, additional lattice calculations are required to include the auxiliary albedo parameter(s) in the cross-section libraries. Clarno and Adams employed a spatial superposition of colorsets in order to reduce the number of extra calculations, 14 whereas Rahnema and McKinley developed a high-order cross-section homogenization method that does not demand to perform parametric lattice calculations.…”

Section: Introductionmentioning

confidence: 99%

Rehomogenization of Nodal Cross Sections via Modal Synthesis of Neutron Spectrum Changes

Gamarino

Dall’Osso

Lathouwers

et al. 2018

Nuclear Science and Engineering

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-Nodal diffusion is currently the preferred neutronics model for industrial reactor core calculations, which use few-group cross-section libraries generated via standard assembly homogenization. The infinite-medium flux-weighted cross sections fail to capture the spectral effects triggered in the core environment by nonreflective boundary conditions at the fuel-assembly edges. This poses a serious limitation to the numerical simulation of current-and next-generation reactor cores, characterized by strong interassembly heterogeneity.Recently, a spectral rehomogenization method has been developed at AREVA NP. This approach consists of an on-the-fly modal synthesis of the spectrum variation between the environmental and infinite-medium conditions. It uses information coming from both the nodal simulation and the lattice transport calculation performed to compute the standard cross sections. The accuracy of the spectral corrections depends on the choice of the basis and weighting functions for the expansion and on the definition of a realistic energy distribution of the neutron leakage. In this paper, we focus on the first aspect. Two tracks are researched: a combination of analytical functions (with a physically justified mode) and a mathematical approach building upon the Proper Orthogonal Decomposition. The method is applied to relevant pressurized-water-reactor benchmark problems. We show that the accuracy of the cross sections is significantly improved at reasonably low computational cost and memory requirement. Several aspects of the methodology are discussed, such as the interplay with space-dependent corrections. We demonstrate that this approach can model not only the spectral interactions between dissimilar neighbor assemblies but also the spectral effects due to different physical conditions (namely, multiplicative properties) in the environment and in the infinite medium.

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A neutron-leakage spectrum model for on-the-fly rehomogenization of nodal cross sections

Gamarino

Dall’Osso

Lathouwers

et al. 2018

Annals of Nuclear Energy

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Improvement of Nodal Accuracy by Using Albedo-Corrected Parameterized Equivalence Constants

Cited by 14 publications

References 5 publications

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Rehomogenization of Nodal Cross Sections via Modal Synthesis of Neutron Spectrum Changes

A neutron-leakage spectrum model for on-the-fly rehomogenization of nodal cross sections

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