NUPEC BWR Full-size Fine-mesh Bundle Test (BFBT) Benchmark

Neykov, D.; Aydoğan, Fatih; Hochreiter, L. E.; Utsuno, H.; Kasahara, Fumio; Sartori, Enrico; Martin, M.R.

doi:10.1787/oecd_papers-v6-art28-en

Cited by 84 publications

(58 citation statements)

References 13 publications

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“…The OECD/NRC BFBT benchmark consists of two phases: Phase I (void distribution) and Phase II (critical power) (Neykov et al, 2006).…”

Section: Summary Of Resultsmentioning

confidence: 99%

“…The OECD/NRC Full Size Fine Mesh Bundle Test (BFBT) benchmark provides high resolution and full-scale experimental data under actual operating conditions to be used for validation of the current models for void distribution and critical power in Boiling Water Reactors (BWRs) (Neykov et al, 2006). This international benchmark has encouraged advancement in the field of two-phase flow theory with very important relevance to the nuclear reactors safety margins evaluation.…”

Section: Objectivesmentioning

confidence: 99%

“…Exercise I-3 is a transient macroscopic grade benchmark, where a sub-channel approach can be applied. Exercise I-4 is a benchmark on uncertainty analysis of the void distribution prediction (Neykov et al, 2006).…”

Section: Phase I -Void Distribution Benchmarkmentioning

confidence: 99%

See 2 more Smart Citations

Multi-physics and multi-scale benchmarking and uncertainty quantification within OECD/NEA framework

Avramova

Ivanov

Kozłowski

et al. 2015

Annals of Nuclear Energy

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“…The OECD/NRC BFBT benchmark consists of two phases: Phase I (void distribution) and Phase II (critical power) (Neykov et al, 2006).…”

Section: Summary Of Resultsmentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%

See 1 more Smart Citation

Multi-physics and multi-scale benchmarking and uncertainty quantification within OECD/NEA framework

Avramova

Ivanov

Kozłowski

et al. 2015

Annals of Nuclear Energy

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“…The BFBT benchmark is sub-divided into two phases, each consisting of several exercises related to the different parameters. In this work, the following cases have been selected from the benchmark database (a complete description is given in Neykov et al, 2005):…”

Section: Bfbt Benchmark: Test Description Measurement Techniques Andmentioning

confidence: 99%

“…To this purpose, the Nuclear Power Engineering Corporation (NUPEC) of Japan developed one of the most valuable databases identified for thermal-hydraulics modelling which includes sub-channel void fraction measurements in a representative Boiling Water Reactor (BWR) fuel assembly. Part of this database is available for international benchmark activities, http (Neykov et al, 2005). The BFBT results were adopted in a previous work (Jaeger et al, 2013) for the validation of four different codes -NEP-TUNE CFD 1.0.8 (Lavieville et al, 2006), SUBCHANFLOW 2.1 (Imke et al, 2010), COBRA-TF (Thurgood et al, 1981), and TRACE 5P2 (US NRC, 2010) -which are used and validated to cover the full range of thermal-hydraulic design and transient analysis.…”

Section: Introductionmentioning

confidence: 99%

Validation of the thermal-hydraulic system code ATHLET based on selected pressure drop and void fraction BFBT tests

Marcello

Escalante

Espinoza

2015

Nuclear Engineering and Design

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Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

Radaideh

Kozłowski

2019

Int J Energy Res

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Summary A novel and modern framework for energy modeling is developed in this paper with a focus on nuclear energy modeling and simulation. The framework combines multiphysics simulations and real data, with validation by uncertainty quantification tasks and facilitation by machine and deep learning methods. The hybrid framework is built on the basis of a wide range of physical models, real data, mathematical and statistical methods, and artificial intelligence techniques. The framework is demonstrated in different applications, including quantifying uncertainties in computer simulations, multiphysics coupling, analysis of variance using machine learning surrogate models, deep learning of time series phenomena, and propagating parametric uncertainties of nuclear data. The applications demonstrated are oriented to nuclear engineering simulations, even though majority of the methods are applicable to other energy sources (eg, renewable). Efficient utilization of this framework is expected to yield a much better understanding of the physical phenomena analyzed as well as an improvement in the performance of the energy design/model under construction.

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NUPEC BWR Full-size Fine-mesh Bundle Test (BFBT) Benchmark

Cited by 84 publications

References 13 publications

Multi-physics and multi-scale benchmarking and uncertainty quantification within OECD/NEA framework

Multi-physics and multi-scale benchmarking and uncertainty quantification within OECD/NEA framework

Validation of the thermal-hydraulic system code ATHLET based on selected pressure drop and void fraction BFBT tests

Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

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