Blind Benchmark Exercise for Spent Nuclear Fuel Decay Heat

Jansson, Peter; Bengtsson, Martin; Bäckström, Ulrika; Čalič, Dušan; Caruso, S.; Dagan, Ron; Fiorito, Luca; Giot, L.; Govers, Kevin; Hernández-Solís, Augusto; Hannstein, Volker; Ilas, Germina; Kromar, Marjan; Leppänen, Jaakko; Mosconi, M.; Ortego, Pedro; Plukienė, R.; Plukis, A.; Ranta-Aho, A.; Rochman, D.; Ros, Linus; Sato, Shunsuke; Schillebeeckx, P.; Shama, Ahmed; Simeonov, Teodosi; Stankovskiy, Alexey; Trellue, Holly R.; Vaccaro, Stefano; Vallet, Vanessa; Verwerft, Marc; Žerovnik, Gašper; Sjöland, Anders

doi:10.1080/00295639.2022.2053489

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…Additional work is necessary to understand the origins of these differences. The present values for the decay heat uncertainties and biases are nevertheless in agreement with the conclusions of reference [62] (blind calculations for five assemblies).…”

Section: Uncertainties and Biasessupporting

confidence: 92%

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On the estimation of nuclide inventory and decay heat: a review from the EURAD European project

Rochman

Dagan²,

Fiorito

et al. 2023

EPJ Nuclear Sci. Technol.

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In this work, a study dedicated to the characterization of the neutronics aspect of the Spent Nuclear Fuel (SNF), as part of the European project EURAD (Work Package 8), is presented. Both measured nuclide concentrations from Post Irradiation Examination samples and decay heat from calorimetric measurements are compared to simulations performed by different partners of the project. Based on these detailed studies and data from the published literature, recommendations are proposed with respect to best practices for SNF modelling, as well as biases and uncertainties for a number of important nuclides and the SNF decay heat for a cooling period from 1 to 1000 years. Finally, specific needs are presented for the improvement of current code prediction capabilities.

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Section: Uncertainties and Biasessupporting

confidence: 92%

“…Such values can be misleading; a few remarks can add a different perspective: 3. Apart from the 5 SNF decay heat measurements from reference [62], all other calculations were not blindly performed, i.e. measurement values were available at the time of the simulations.…”

Section: Decay Heat and Bias Estimation Based On Measured Valuesmentioning

confidence: 99%

On the estimation of nuclide inventory and decay heat: a review from the EURAD European project

Rochman

Dagan²,

Fiorito

et al. 2023

EPJ Nuclear Sci. Technol.

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“…Also, studies of validation data within the European Horizon 2020 project have resulted in recommendations that code predictions are estimated not to be better than 5% from measurements (Rochman et al, 2023). The former recommendation is in line with the outcomes of the Vattenfall/SKB-organized blind benchmark on decay heat predictions for five PWRs (Jansson et al, 2022). In this benchmark, Several organizations noted significant differences between decay heat calculations and measurements, more prominent than expected from previous studies on SNF decay heat validation.…”

Section: Introductionmentioning

confidence: 52%

Analyses of the bias and uncertainty of SNF decay heat calculations using Polaris and ORIGEN

Shama

Caruso²,

Rochman

2023

Front. Energy Res.

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The bias and uncertainty of calculated decay heat from spent nuclear fuel (SNF) are essential for code validation. Also, predicting these quantities is crucial for deriving decay heat safety margins, influencing the design and safety of facilities at the back end of the nuclear fuel cycle. This paper aims to analyze the calculated spent nuclear fuel decay heat biases, uncertainties, and correlations. The calculations are based on the Polaris and ORIGEN codes of the SCALE code system. Stochastically propagated uncertainties of inputs and nuclear data into calculated decay heats are compared. Uncertainty propagation using the former code is straightforward. In contrast, the counterpart of ORIGEN necessitated the pre-generation of perturbed nuclear cross-section libraries using TRITON, followed by coincident perturbations in the ORIGEN calculations. The decay heat uncertainties and correlations have shown that the observed validation biases are insignificant for both Polaris and ORIGEN. Also, similarities are noted between the calculated decay heat uncertainties and correlations of both codes. The fuel assembly burnup and cooling time significantly influence uncertainties and correlations, equivalently expressed in both Polaris and ORIGEN models. The analyzed decay heat data are highly correlated, particularly the fuel assemblies having either similar burnup or similar cooling time. The correlations were used in predicting the validation bias using machine learning models (ML). The predictive performance was analyzed for machine learning models weighting highly correlated benchmarks. The application of random forest models has resulted in promising variance reductions and predicted biases significantly similar to the validation ones. The machine learning results were verified using the MOCABA algorithm (a general Monte Carlo-Bayes procedure). The bias predictive performance of the Bayesian approach is examined on the same validation data. The study highlights the potential of neighborhood-based models, using correlations, in predicting the bias of spent nuclear fuel decay heat calculations and identifying influential and highly similar benchmarks.

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“…The current research efforts mainly focus on enhancing the precision of decay heat calculations and identifying sources of uncertainty. A recent blind benchmark exercise organized and coordinated by the Swedish Nuclear Fuel and Waste Management Company (SKB) (Jansson et al, 2022) drew additional attention to the topic. In this benchmark decay heat measurements of five fuel assemblies were compared with calculations provided by numerous international participants using different codes and nuclear data libraries.…”

Section: Introductionmentioning

confidence: 99%

Validation of the burnup code MOTIVE with respect to fuel assembly decay heat data

et al. 2023

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The burn-up code MOTIVE is a 3D code for fuel assembly inventory determination developed at GRS in recent years. It modularly couples an external Monte Carlo neutron transport code to the in-house inventory code VENTINA. In the present publication, we report on the validation of MOTIVE with respect to full-assembly decay heat measurements of light water reactor fuel. For this purpose, measurements on pressurized water reactor and boiling water reactor fuel assemblies from different facilities have been analyzed with MOTIVE. The calculated decay heat values are compared to the measured data in terms of absolute and relative deviations. These results are discussed and compared to other published validation analyses. Moreover, the observed deviations between measurements and calculations are analyzed further by taking into account the results of the validation of nuclide inventory determination with MOTIVE. The influence of possible biases of calculated nuclide densities important to decay heat at the given decay times are investigated and discussed.

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Blind Benchmark Exercise for Spent Nuclear Fuel Decay Heat

Cited by 9 publications

References 23 publications

On the estimation of nuclide inventory and decay heat: a review from the EURAD European project

On the estimation of nuclide inventory and decay heat: a review from the EURAD European project

Analyses of the bias and uncertainty of SNF decay heat calculations using Polaris and ORIGEN

Validation of the burnup code MOTIVE with respect to fuel assembly decay heat data

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