A perturbation-based acceleration for Monte Carlo – Thermal Hydraulics Picard iterations. Part II: Application to 3D PWR-based problems

Terlizzi, Stefano; Kotlyar, Dan

doi:10.1016/j.anucene.2021.108713

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…This work has demonstrated the potential of the GTF-ND sub-block to replace the GTF-FOP sub-block proposed in Refs. [2,4]. The GTF sub-block was able to predict with good accuracy the spatial shape variation in the fission, absorption, and transport macroscopic cross sections after a density perturbation.…”

Section: Discussionmentioning

confidence: 93%

“…The latter task will allow for assessment of the GTF-ND block's potential to reduce the number of MC and TH iterations, similar to the results shown in Refs. [2][3][4].…”

Section: Discussionmentioning

confidence: 99%

“…[2], the acceleration was obtained by concatenating two sub-blocks. The first one used the Generalized Transfer Function (GTF) theory to update the mono-energetic macroscopic cross sections after a change in temperature and density conditions [3,4]. The second sub-block used first order perturbation (FOP) theory to estimate the corresponding value of the power distribution and the associated effective multiplication factor.…”

Section: Introductionmentioning

confidence: 99%

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Accelerated Coupled Monte Carlo-Thermal Hydraulic Calculations Using a Hybrid GTF-Diffusion-Based Prediction Block: First Results

Painter,

Terlizzi,

Kotlyar

2022

International Conference on Physics of Reactors 2022 (PHYSOR 2022)

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Accurate predictions of spatial power and temperature distributions require the coupling of a neutron transport solver with a thermal-hydraulic (TH) feedback. Nowadays, Monte Carlo (MC) codes are widely coupled to TH solvers, typically via a Picard iteration (PI) method, due to the higher fidelity that such frameworks can produce. To speed up a PI, a prediction step can produce an improved initial guess for a source distribution and feed it to the MC code. Recent work [1, 2] investigated a prediction step that uses generalized transfer functions (GTFs) to predict the macroscopic cross sections' variations following a perturbation in TH properties, such as coolant density. The previous method also relied on first order perturbation (FOP) theory to predict perturbed power profiles, rather than using an expensive MC iterate. The implemented FOP method relied on generating a fission matrix from which the forward and adjoint eigenmodes were extracted and later used to for power calculations. The generation of the fission matrix can introduce a significant computational overhead, therefore undermining the performance of the proposed hybrid technique when applied to high-dimensional problems, e.g., full core calculations. This work attempts to improve the GTF-FOP prediction step by replacing the FOP solver with a nodal diffusion solver, thus eliminating the need to calculate a fission matrix. The GTF-diffusion step was tested for various moderator density perturbations. In each case, the predicted power distribution showed good agreement with the reference case. The latter is attributed to the generally good prediction of most spatially distributed macroscopic cross sections, except the transport cross section, which will become the focus of future work.

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Section: Discussionmentioning

confidence: 93%

“…The latter task will allow for assessment of the GTF-ND block's potential to reduce the number of MC and TH iterations, similar to the results shown in Refs. [2][3][4].…”

Section: Discussionmentioning

confidence: 99%