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

Abstract

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.

Quantitative Analysis Study of Effects of Nuclide Concentration Uncertainties on Biases and Bias Uncertainties in Criticality Calculation Method

“…Energies 2023, 16,7378 where β n inf represents the k inf bias propagated to the criticality calculation through the nuclide concentration for the nuclide (n) from the measurement data of fuel sample j in Equation (17). β inf is the k inf bias resulting from all the calculated nuclide concentration biases in Equation (18).…”

“…Even with the adoption of a coupled diffusion-depletion calculation process and the comprehensive tracking of the fuel assembly position throughout the cycles, it remains challenging to fully account for the actual depletion process of the fuel assembly in the reactor. Moreover, because the depletion calculation requires the decomposition of the burnup chain and the selection of an effective numerical calculation method to obtain the spent-fuel composition at a specific burnup [15], the approximation introduced by the burnup chain decomposition and the inherent error of the numerical calculation method make it difficult to obtain an accurate nuclear fuel composition for the depletion calculation [16,17]. Hence, when the BUC technique is applied, it is essential to analyze the spent-fuel composition used in the depletion calculation in a reasonable and conservative manner and quantify the nuclide concentration uncertainties in the criticality calculation.…”

Assessment of Spent Nuclear Fuel in Ukrainian Storage System: Inventory and Performance

“…The results showed that the fission products contribute to the decay heat power mainly in the short storage period (<50 years). As highlighted in [4], the accuracy of the results obtained from analytical and numerical calculations depends on the degree of the accuracy of available data and on the use of appropriate cross-section libraries [5][6][7]. At present, most of the calculations of spent fuel characteristics after in-service operation are performed by means of validated numerical codes such as ORIGEN (Oak Ridge Isotope Generation, developed at ORNL, Oak Ridge, TN, USA) or KORIGEN (FZK, Hannover, Germany).…”

Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks

“…Several studies have used such computational models in the past to investigate the uncertainty of SNF, e.g. in Shama et al (2023Shama et al ( , 2022 the biases, uncertainties, and correlations of calculated decay heat from SNF using the Polaris and ORIGEN codes were analyzed, finding that both codes exhibited insignificant biases and similar uncertainties and correlations influenced by fuel assembly (FA) burnup and cooling time. The study also made use of machine learning models and the MOCABA algorithm for predicting biases and verifying results.…”