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The cross section averaged over 235U thermal-neutron induced fission spectrum is a fundamental quantity that can be used in evaluation and validation of nuclear data. Many experiments focused on the determination of Spectrum Averaged Cross Sections (SACS) in 235U(nth,f). Prompt Fission Neutron Spectrum (PFNS) in light water reactors using enriched uranium fuel. In these reactors, there is already some amount of water moderator between the uranium fuel and the irradiated sample. Due to the decrease of hydrogen cross-section with neutron energy, the high energy tail of the reactor spectrum in cores with water moderator may be harder than the pure PFNS. This paper aims to compare the shape of the actual reactor spectrum in various core positions of a research light-water reactor differing each from other by the effective water thickness. The spectrum shape is determined both by calculations and experimentally using various high energy threshold reactions. The impact of the photo-nuclear reactions ({\gamma},n) competing with (n,2n) in production of the same residual nucleus was shown to be less than a percent for most of studied dosimeters. An important exception was found for 197Au(n,2n) 196Au dosimeter irradiated in the outcore channel where a notable photo-neutron contribution to the production of 196Au is caused by the neutron production from the high energy {\gamma}-rays from thermal-neutron capture in 54Fe. The corresponding ENDF/B-VIII.0 data turned out to underestimate such {\gamma}-yield by 40% in comparison with ENDF/B-VI.8. This has improved but however not resolved the disagreement between our measurement and calculations. The remaining deficiency was attributed to the underestimation of the evaluated cross section IAEA/PD-2019 for the 197Au({\gamma},n) cross section near the reaction threshold. The later was confirmed by comparison with existing measured data.

Dosimetry cross sections are fundamental quantities necessary for neutron dosimetry using the neutron activation method. It is worth noting that the uncertainty in cross sections is the major source of uncertainty in calculational predictions using nuclear data in simulations thus, cross section validation is a key issue in any aims for refinement of any predictions. A small compact neutron generator is a promising tool for performing integral experiments and even for differential experiments. This paper deals with the measurement of the differential dosimetry cross sections using a small compact D-T neutron generator with 14.05 MeV neutron emission (10E8 n/s into 4pi). Achieving measurable activation at such a low flux field is allowed by using a larger amount of activation material placed in close measurement geometry during decay gamma measurement. The experimentally determined cross sections are in good agreement with the cross sections in the IRDFF-II dosimetry library. The comparison with other nuclear data libraries was performed as well. Its worth noting, the mean standard deviation in IRDFF-II library is about 4 %, while in case of other data libraries they are from 5.5 % - 7.5 %. This result can be understood as a validation of IRDFF-II using 14.05 MeV neutrons and also a confirmation of the applicability of small compact generators in the measurement of activation cross sections.

The reactor baffle is an important component of a nuclear reactor that fixes the location of the fuel assembly in the reactor core. The main types of baffles are called light or heavy. The light baffle has mostly the form of steel plates with outer space filled by water, and the heavy baffle is mostly a forged steel element. Both concepts have advantages as well as disadvantages. In the case of the light baffle, one does not need to solve the issue of void swelling, but the neutron economy is not ideal, while the heavy baffle has a good neutron economy, but void swelling is an issue. This paper deals with the effect of the heavy VVER-1000 baffle on criticality. Criticality was measured using a well-defined core composed of 6 fuel assemblies moved to a simulator of the VVER-1000 internals, which is located at the LR-0 reactor. The experiments confirm the fact that the water filling the cooling channels in the baffle has a strong neutron absorbing effect. The keff calculated using the ENDF/B-VIII.0 library significantly underpredicts the experiment, whereas calculations using a new evaluation of 56Fe by the IAEA (INDEN collaboration) give a better agreement. Generally, the presented results are suitable for validation of iron cross sections.

Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. This information can be a limiting factor when using the multigroup discrete ordinates (SN) method when the number of energy groups is large. Machine Learning (ML) can be used to replace the need for the cross section matrices by reproducing the function that maps the scalar flux to the scattering and fission sources. Through the use of autoencoders and Deep Jointly-Informed Neural Networks (DJINN), the data storage requirements are reduced by 94% of the original data for a 618 group problem. This is accomplished while preserving the scalar flux, maintaining generality, and decreasing wall clock times.

Pebble bed reactors (PBRs) can improve the safety and economics of the nuclear energy production. PBRs rely on TRIstructural-ISOtropic (TRISO) fuel pebbles for enhanced fission product retention. Accurate characterization of individual fuel pebbles would enable the validation of computational models, efficient use of TRISO fuel, and improve fuel accountability. We have developed and tested a new neutron multiplicity counter (NMC) based on 192 boron coated straw (BCS) detectors optimized for ${}^{235}$U assay in TRISO fuel. The new design yielded a singles and doubles neutron detection efficiency of 4.71% and 0.174%, respectively, and a die-away time of 16.7 $\mathrm{\mu}$s. The NMC has a low intrinsic gamma-ray detection efficiency of $8.71\times10^{-8}$ at an exposure rate of 80.3 mR/h. In simulation, a high-efficiency version of the NMC encompassing 396 straws was able to estimate the ${}^{235}$U in a pebble with a relative uncertainty and error both below 2% in 100 s.

Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste . We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation

High-temperature gas reactors rely on TRIstructural-ISOtropic (TRISO) fuel for enhanced fission product retention. Accurate fuel characterization would improve monitoring of efficient fuel usage and accountability. We developed a new neutron multiplicity counter (NMC) based on boron coated straw (BCS) detectors and used it in coincidence mode for 235U assay in TRISO fuel. In this work, we demonstrate that a high-efficiency version of the NMC encompassing 396 straws is able to estimate the 235U in used TRISO-fueled pebbles or compacts with a relative uncertainty below 2.5% in 100 s. We performed neutronics and fuel depletion calculation of the HTR-10 pebble bed reactor to estimate the neutron and gamma-ray source strengths of used TRISO-fueled pebbles with burnup between 9 and 90 GWd/t. Then, we measured a gamma-ray intrinsic efficiency of 10^-12 at an exposure rate of 340.87 R/h. The low gamma-ray sensitivity and high neutron detection efficiency enable the inspection of used fuel. Comment: 25 pages, 19 figures

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.