Uncertainty Quantification on Spent Nuclear Fuel with LMC

The recently developed method Lasso Monte Carlo (LMC) for uncertainty quantification is applied to the characterisation of spent nuclear fuel. The propagation of nuclear data uncertainties to the output of calculations is an often required procedure in nuclear computations. Commonly used methods such as Monte Carlo, linear error propagation, or surrogate modelling suffer from being computationally intensive, biased, or ill-suited for high-dimensional settings such as in the case of nuclear data. The LMC method combines multilevel Monte Carlo and machine learning to compute unbiased estimates of the uncertainty, at a lower computational cost than Monte Carlo, even in high-dimensional cases. Here LMC is applied to the calculations of decay heat, nuclide concentrations, and criticality of spent nuclear fuel placed in disposal canisters. The uncertainty quantification in this case is crucial to reduce the risks and costs of disposal of spent nuclear fuel. The results show that LMC is unbiased and has a higher accuracy than simple Monte Carlo.Comment: Conference paper from the 12th International Conference on Nuclear Criticality Safety (ICNC), Sendai, Japan, October 2023. Submitted to the Arxiv with the permission of the conference organiser

Lasso Monte Carlo, a Novel Method for High Dimensional Uncertainty Quantification

Uncertainty quantification (UQ) is an active area of research, and an essential technique used in all fields of science and engineering. The most common methods for UQ are Monte Carlo and surrogate-modelling. The former method is dimensionality independent but has slow convergence, while the latter method has been shown to yield large computational speedups with respect to Monte Carlo. However, surrogate models suffer from the so-called curse of dimensionality, and become costly to train for high-dimensional problems, where UQ might become computationally prohibitive. In this paper we present a new technique, Lasso Monte Carlo (LMC), which combines surrogate models and the multilevel Monte Carlo technique, in order to perform UQ in high-dimensional settings, at a reduced computational cost. We provide mathematical guarantees for the unbiasedness of the method, and show that LMC can converge faster than simple Monte Carlo. The theory is numerically tested with benchmarks on toy problems, as well as on a real example of UQ from the field of nuclear engineering. In all presented examples LMC converges faster than simple Monte Carlo, and computational costs are reduced by more than a factor of 5 in some cases

Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks

The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks

EASY-II: a system for modelling of n, d, p, {\gamma} and {\alpha} activation and transmutation processes

EASY-II is designed as a functional replacement for the previous European Activation System, EASY-2010. It has extended nuclear data and new software, FISPACT-II, written in object-style Fortran to provide new capabilities for predictions of activation, transmutation, depletion and burnup. The new FISPACT-II code has allowed us to implement many more features in terms of energy range, up to GeV; incident particles: alpha, gamma, proton, deuteron and neutron; and neutron physics: self-shielding effects, temperature dependence, pathways analysis, sensitivity and error estimation using covariance data. These capabilities cover most application needs: nuclear fission and fusion, accelerator physics, isotope production, waste management and many more. In parallel, the maturity of modern general-purpose libraries such as TENDL-2012 encompassing thousands of target nuclides, the evolution of the ENDF format and the capabilities of the latest generation of processing codes PREPRO-2012, NJOY2012 and CALENDF-2010 have allowed the FISPACT-II code to be fed with more robust, complete and appropriate data: cross-sections with covariance, probability tables in the resonance ranges, kerma, dpa, gas and radionuclide production and 24 decay types. All such data for the five most important incident particles are placed in evaluated data files up to an incident energy of 200 MeV. The resulting code and data system, EASY-II, includes many new features and enhancements. It has been extensively tested, and also benefits from the feedback from wide-ranging validation and verification activities performed with its predecessor.Comment: 6 pages, 7 figure

L’Etat et la profession universitaire en France et en Allemagne

Cet article repose sur une analyse des relations entre la profession universitaire et l'administration de tutelle en France et en Allemagne et de l'autonomie respective des disciplines et des gestionnaires de l'enseignement supérieur. En France, les relations sont fortes et fréquentes entre la tutelle parisienne et certains représentants de la profession universitaire qui influencent les décisions prises à travers de multiples mécanismes (consultations, expertises, groupes de réflexion,...) Dans les trois ministères de Land étudiés en Allemagne, en revanche, les relations avec la profession académique sont rares. Les ajustements entre les disciplines obéissent à des mécanismes plus locaux (sans régulation fédérale) qui contribuent à modifier leur poids relatif au sein de chaque Land. Cette comparaison nous permettra de conclure sur la nature des relations entre la profession universitaire et l'Etat dans les deux pays

Nuclear data for fusion technology – the European approach

The European approach for the development of nuclear data for fusion technology applications is presented. Related R&D activities are conducted by the Consortium on Nuclear Data Development and Analysis for Fusion to satisfy the nuclear data needs of the major projects including ITER, the Early Neutron Source (ENS) and DEMO. Recent achievements are presented in the area of nuclear data evaluations, benchmarking and validation, nuclear model improvements, and uncertainty assessments