Uncertainty Quantification and Propagation of Multiphysics Simulation of the Pressurized Water Reactor Core

Zeng, Kaiyue; Hou, Jason; Ivanov, Kostadin; Jessee, Matthew Anderson

doi:10.1080/00295450.2019.1580533

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…e results of the uncertainty breakdown indicate that the uncertainties of the core responses in the cycle depletion calculations are influenced by the input uncertainty of all three physics domains. is is consistent with the conclusion of previous studies, such as [14], that the uncertainty of power and reactivity is mostly dominated by the nuclear data uncertainty, while that of the temperature is strongly dictated by the uncertainty of fuel modelling and thermalhydraulics variables. It is obvious that the evaluation of the input uncertainties is of paramount importance for determining the uncertainty of the responses.…”

Section: Uncertainty Propagation For Cycle Depletionsupporting

confidence: 92%

“…In total, 9 sets of results have been collected. Most results were obtained using the one-step and two-step approaches mentioned above, such as in [14], except for Cases 11 and 35, where the direct full-core transport calculation was performed with explicit modelling of the geometry. e estimated relative uncertainties of the core eigenvalue are shown in Figure 10, and the average value over all 9 sets of results is 0.49%, which is close to the observed values in the pin cell and lattice calculations.…”

Section: Core Physicsmentioning

confidence: 99%

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Best-Estimate Plus Uncertainty Framework for Multiscale, Multiphysics Light Water Reactor Core Analysis

Hou

Avramova

Ivanov

2020

Science and Technology of Nuclear Installations

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Tremendous work has been done in the Light Water Reactor (LWR) Modelling and Simulation (M&S) uncertainty quantification (UQ) within the framework of the Organization for Economic Cooperation and Development (OECD)/Nuclear Energy Agency (NEA) LWR Uncertainty Analysis in Modelling (UAM) benchmark, which aims to investigate the uncertainty propagation in all M&S stages of the LWRs and to guide uncertainty and sensitivity analysis methodology development. The Best-Estimate Plus Uncertainty (BEPU) methodologies have been developed and implemented within the framework of the LWR UAM benchmark to provide a realistic predictive simulation capability without compromising the safety margins. This paper describes the current status of the methodological development, assessment, and integration of the BEPU methodology to facilitate the multiscale, multiphysics LWR core analysis. The comparative analysis of the results in the stand-alone multiscale neutronics phase (Phase I) is first reported for understanding the general trend of the uncertainty of core parameters due to the nuclear data uncertainty. It was found that the predicted uncertainty of the system eigenvalue is highly dependent on the choice of the covariance libraries used in the UQ process and is less sensitive to the solution method, nuclear data library, and UQ method. High-to-Low (Hi2Lo) model information approaches for multiscale M&S are introduced for the core single physics phase (Phase II). In this phase, the other physics (fuel and moderator), providing feedback to neutronics M&S in a LWR core, and time-dependent phenomena are considered. Phase II is focused on uncertainty propagation in single physics models which are components of the LWR core coupled multiphysics calculations. The paper discusses the link and interactions between Phase II to the multiphysics core and system phase (Phase III), that is, the link between uncertainty propagation in single physics on local scale and multiphysics uncertainty propagation on the core scale. Particularly, the consistency in uncertainty assessment between higher-fidelity models implemented in fuel performance codes and the rather simplified models implemented in thermal-hydraulics codes, to be used for coupling with neutronics in Phase III is presented. Similarly, the uncertainty quantification on thermal-hydraulic models is established on a relatively small scale, while these results will be used in Phase III at the core scale, sometimes with different codes or models. Lastly, the up-to-date UQ method for the coupled multiphysics core calculation in Phase III is presented, focusing on the core equilibrium cycle depletion calculation with associated uncertainties.

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Section: Uncertainty Propagation For Cycle Depletionsupporting

confidence: 92%

Section: Core Physicsmentioning

confidence: 99%

Best-Estimate Plus Uncertainty Framework for Multiscale, Multiphysics Light Water Reactor Core Analysis

Hou

Avramova

Ivanov

2020

Science and Technology of Nuclear Installations

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“…First we will look at the E(t i ) error as a function of the number of snapshots. Using the same parameter range for Z we use different ∆Z to generate 3 training sets, one with ∆Z = 2 (Z = [1,3,5,7,9,11,13,15]), one with ∆Z = 4 (Z = [1,5,9,13]), and one with ∆Z = 6 (Z = [1,7,13]). The testing set for this Both methods perform similarly relative to their performance in rank.…”

Section: Parametric Dmd Performance Studymentioning

confidence: 99%

“…Due to their larger computational cost, typically requiring several processing nodes or supercomputers, these simulations are most often limited to providing a single, best-estimate answer to a given problem [1]. However, multi-query applications in engineering, such as uncertainty quantification, optimal control, or design optimization, require several, repeated evaluations of a computational model in which the uncertain or design parameters are assigned different values [2,3,4,5,6,7]. Therefore, multi-query applications generally lie beyond the realm of high-fidelity simulation tools and rely on simplified, less-accurate computational models [8].…”

Section: Introductionmentioning

confidence: 99%

Parametric Dynamic Mode Decomposition for Reduced Order Modeling

Huhn¹,

Tano²,

Ragusa³

et al. 2022

Preprint

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Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. The DMD low-rank or reduced operator is typically obtained by singular value decomposition of the temporal data sets. For parameter-dependent models, as found in many multi-query applications such as uncertainty quantification or design optimization, the only parametric DMD technique developed was a stacked approach, with data sets at multiples parameter values were aggregated together, increasing the computational work needed to devise low-rank dynamical reduced-order models. In this paper, we present two novel approach to carry out parametric DMD: one based on the interpolation of the reduced-order DMD eigenpair and the other based on the interpolation of the reduced DMD (Koopman) operator. Numerical results are presented for diffusion-dominated nonlinear dynamical problems, including a multiphysics radiative transfer example. All three parametric DMD approaches are compared.

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“…Uncertainty propagation methodology (shown in Figure 9) has been developed at NC State University based on a stochastic sampling method by taking into account the uncertainties of the three physics M&S (neutronics, thermal-hydraulics, and fuel physics) in the simulation of PWRs that can be incorporated in the conventional LWR simulation approach. More specifically, the Three Mile Island Unit 1 (TMI-1) related exercises from the LWR-UAM benchmark were modeled using the coupled TRACE/PARCS code system in 3D core representation [15]. The input uncertainties of the neutronics simulation include fewgroup cross sections and kinetics parameters generated using the SAMPLER/POLARIS sequence of SCALE 6.2.1.…”

Section: Uncertainty Quantification In Multi-physics Mandsmentioning

confidence: 99%

Innovations in Multi-Physics Methods Development, Validation, and Uncertainty Quantification

Avramova

Abarca

Hou

et al. 2021

JNE

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This paper provides a review of current and upcoming innovations in development, validation, and uncertainty quantification of nuclear reactor multi-physics simulation methods. Multi-physics modelling and simulations (M&S) provide more accurate and realistic predictions of the nuclear reactors behavior including local safety parameters. Multi-physics M&S tools can be subdivided in two groups: traditional multi-physics M&S on assembly/channel spatial scale (currently used in industry and regulation), and novel high-fidelity multi-physics M&S on pin (sub-pin)/sub-channel spatial scale. The current trends in reactor design and safety analysis are towards further development, verification, and validation of multi-physics multi-scale M&S combined with uncertainty quantification and propagation. Approaches currently applied for validation of the traditional multi-physics M&S are summarized and illustrated using established Nuclear Energy Agency/Organization for Economic Cooperation and Development (NEA/OECD) multi-physics benchmarks. Novel high-fidelity multi-physics M&S allow for insights crucial to resolve industry challenge and high impact problems previously impossible with the traditional tools. Challenges in validation of novel multi-physics M&S are discussed along with the needs for developing validation benchmarks based on experimental data. Due to their complexity, the novel multi-physics codes are still computationally expensive for routine applications. This fact motivates the use of high-fidelity novel models and codes to inform the low-fidelity traditional models and codes, leading to improved traditional multi-physics M&S. The uncertainty quantification and propagation across different scales (multi-scale) and multi-physics phenomena are demonstrated using the OECD/NEA Light Water Reactor Uncertainty Analysis in Modelling benchmark framework. Finally, the increasing role of data science and analytics techniques in development and validation of multi-physics M&S is summarized.

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Uncertainty Quantification and Propagation of Multiphysics Simulation of the Pressurized Water Reactor Core

Cited by 11 publications

References 10 publications

Best-Estimate Plus Uncertainty Framework for Multiscale, Multiphysics Light Water Reactor Core Analysis

Best-Estimate Plus Uncertainty Framework for Multiscale, Multiphysics Light Water Reactor Core Analysis

Parametric Dynamic Mode Decomposition for Reduced Order Modeling

Innovations in Multi-Physics Methods Development, Validation, and Uncertainty Quantification

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