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Fuel performance codes, such as Transuranus, predict fuel behavior and are used to ensure the safe operation of nuclear reactors. These codes are moderately time-consuming and affordable in many applications but may be limited in others, primarily when many fuel rods must be evaluated simultaneously. This work presents how the temporal neural network techniques, Temporal Convolutional Networks, and a Fourier Neural Operator can be combined to form a deep heterogeneous joint architecture as a surrogate model for fuel performance modeling in time-critical situations. We train the model using realistic power histories and corresponding outputs generated using the fuel performance code Transuranus. The ultimate result is a surrogate model for use in time-critical situations that take milliseconds to evaluate for thousands of fuel rods and have a mean test error of unseen data around a few percent.

In this study, we applied the Total Monte Carlo (TMC) methodology in de-excitation simulations of primary fission fragments (FF) using the TALYS code. The goal was to develop and optimise a methodology to benchmark initial fission model assumptions on fission mass yield distributions, excitation energy sharing and angular momentum population. The study was performed on the thermal neutron induced fission of Pu-239(n(th),f). The work aimed at evaluating fission model deficiencies and parameter sensitivities. We systematically varied TALYS input data by generating 5000 random files through the GEF code, randomizing 94 model parameters that affect fission yields and energy distributions within 3% of their default values. This variation revealed significant changes in the fission observables, such as prompt neutron and gamma-ray multiplicities and energy spectra. The results indicate some systematic defects in the assumed excitation-energies and angular momenta. Another outcome from the study is the identification of a need for new correlation measurements on prompt neutrons and gamma-rays from the Pu-239(n(th),f) reaction, as well as an updated evaluation.

This research paper introduces a novel approach to predict the decay heat of spent nuclear fuel assemblies (SNFs) using data from non-destructive gamma and neutron measurements, addressing the challenge of ensuring safety in geological repositories. Because calorimetric measurements are time-consuming, it is envisioned that gamma and neutron measurements can be used for decay heat prediction before encapsulation. This paper analyses gamma and neutron data to extract key features, specifically the activities of Cs-137, Eu-154, and the total neutron count rate. A Gaussian process model is then employed to estimate SNF decay heat. The methodology involves training a prediction model on a calibrated simulated dataset designed to mimic real experimental conditions closely. The model is then successfully used to predict the decay heat for unseen experimental data. The results highlight the potential of using gamma and neutron measurements for reliable decay heat prediction. It is shown that the magnitude of the relative deviation obtained is 2–4 %. Furthermore, the study explores the impact of removing certain input features or adjusting their uncertainty levels on the decay heat prediction model precision, in particular for the Eu-154 activity and neutron count rate. This comprehensive methodology paves the way for applying these techniques to a larger experimental scale offering a significant advancement in the safety assessment of SNFs prior to encapsulation and long-term storage.

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices. Confining plasma and managing disruptions in tokamak devices is a challenge. Here the authors demonstrate a method predicting and possibly preventing disruptions and macroscopic instabilities in tokamak plasma using data from JET.

Background: Angular momenta of fission fragments considerably higher than that of the fissioning nucleus have been observed in many experiments, raising the question of how these high angular momenta are generated. Wilson et al. have proposed a model for the angular momentum as a function of the mass of fission products based on the assumption that the angular momentum is generated from the collective motion of nucleons in the ruptured neck of the fissioning system. This assumption has caused a lot of debate in the community.

Purpose: To estimate the angular momenta of fission fragments based on the observed isomeric yield ratios in 25 -MeV proton -induced fission of 238 U.

Method: A surrogate model of the fission code general description of fission observables (GEF) has been developed to generate properties of primary fission fragments. Based on the excitation energy and angular momentum of fission fragments from GEF, an energy -versus -angular -momentum matrix is reconstructed using a set of parameters. With such matrices as input, the reaction code TALYS is used to calculate the deexcitation of the fission fragments, including the population of the isomers, from which the isomeric yield ratios are obtained. By varying one of the parameters, the root -mean -square angular momentum ( J rms ), which determines the angular momentum distribution of the matrix, J rms -dependent isomeric yield ratios are obtained. Considering all primary fission fragments contributing to the isomeric yield ratio for a given fission product, the average angular momentum of those fragments is estimated.

Results: Data of 31 isomeric yield ratios in 25 -MeV proton -induced fission of 238 U were analyzed. From the analysis, the average J rms , equivalent to average angular momentum J av , with uncertainties are obtained for 24 fission products, while in seven cases no conclusive result for the angular momentum could be obtained. Furthermore, considering the neutron emissions of the primary fission fragments, the average angular momentum as a function of the average mass number of the primary fission fragment was estimated.

Conclusion: A mass dependency of the average angular momentum is observed in the proton -induced fission of 238 U. Moreover, the average angular momenta for mass numbers larger than 131 could be fairly well described by the parametrization proposed by Wilson et al. However, the average angular momenta of 130 Sn and 131 Te cannot be described by Wilson's model, which suggests a different lower limit for the validation of the parametrization in the model. In general, higher average angular momenta for A 132 are observed in the present work compared to those from Wilson et al. This is likely due to the higher excitation energy of the fissioning nuclei in this work. Furthermore, the first systematic observation of the average angular momenta of fission products in the symmetric mass region is presented. In this region, a decreasing trend with mass number is observed, which cannot be explained by the proposal in Wilson's paper. Thus, a different mechanism is needed to explain this observation.

Fuel performance codes are used to forecast fuel behavior and ensure safe operation. These analyses must typically include prediction uncertainties, and fuel performance models need calibration. Consequently, code calibration must derive the best estimates and corresponding uncertainties of model parameters for subsequent propagation.

Bayesian calibration is popular for generating the probability distribution of model parameters. However, model inadequacy disrupts these techniques, typically resulting in underestimated uncertainties. Earlier research showcased the incorporation of model inadequacy by model parameter inflation. The method demands cheap code predictions and derivatives, which required further research to develop differentiated Gaussian process surrogates.

This work combines those techniques into a complete methodology. We demonstrate it by calibrating Transuranus against fission gas release and cladding oxidation data. The result is model parameter uncertainties that primarily explain the discrepancies between the predictions and corresponding measurements, except when the output behaves highly non-linearly.

The use of the F-19 isotope in the nuclear fuel cycle is already well established for fuel enrichment, but future plans for Gen-IV reactors, such as Molten Salt Reactors, could utilize a fluorine-based salt as a basis for the fuel. It is therefore imperative that an understanding of the characteristics of F-19 is instituted, and one component of key interest is the quantification of reactor parameter uncertainties that arise from the uncertainties in the nuclear data. The results from such analyses can shed light on where experimentalists need to further improve nuclear data for F-19, as well as yielding critical information for developing and optimizing reactor designs thanks to greater knowledge of the uncertainties that result from nuclear data.

In this work, we analysed a molten salt reactor based on the designs made by Transatomic Power. We conducted uncertainty quantification on three reactor operating modes: thermal, semi-epithermal, and epithermal. In the epithermal mode, the neutron spectrum is faster than in the thermal mode because fewer moderator rods are used. We generated nuclear data that was sampled from the covariance matrices in the JEFF-3.3 nuclear data library using SANDY[1] and NJOY. By utilising the Total Monte Carlo approach, we propagated the uncertainties from the samples to uncertainties in the neutron multiplication by simulating the reactor in OpenMC, a Monte Carlo-based neutron transport code. By perturbing individual reaction channels while keeping others constant, it was possible to quantify the amount of contribution each single reaction channel has to the overall uncertainty.

For the thermal reactor, the F-19 data sampling resulted in an uncertainty in reactivity of 62 pcm. The main contributors to the reactivity uncertainty for the thermal reactor are elastic scattering, neutron capture and alpha production. The epithermal reactor, with a reactivity uncertainty of 213 pcm, is mostly affected by elastic scattering, inelastic scattering, and alpha production. The alpha production channel had an unexpectedly large contribution, and it should be investigated further. The results should be considered preliminary. Quantitatively, we observe that scattering plays a bigger role for the uncertainty in the epithermal system, a phenomenon which could be explained by the fact that with less moderation in the form of moderator rods, the role of F-19 in slowing down neutrons is greater, and hence its contribution to the uncertainty is greater.