J. Nucl. Eng., Volume 6, Issue 3 (September 2025) – 14 articles

This work investigates a ⁴He-detector active interrogation system that leverages neutron spectroscopy to classify nuclear waste streams. MCNP models tested the concept through the simulation of a D-D neutron generator, an array of ⁴He detectors, and various waste compositions. The fast-neutron Differential Die-Away signature was augmented with a neutron-energy discrimination signature. This signature isolates induced fission neutrons, the energy of which is greater than that of the D-D monoenergetic spectrum. With the incorporation of this spectroscopic technique, the measurement time decreased by 3–9% (depending on the degree of neutron moderation and absorption presented by the sample), demonstrating how neutron spectroscopy can enhance active interrogation methods. The reduced measurement times would have significant financial and logistical benefits for facilities with large footprints of low-level waste production. Full article

Hydrogen generation and the correct simulation of severe accidents have been of utmost importance since the Fukushima Dai-ichi accident. QUENCH experiments are quite useful for validating mathematical models implemented in system codes for early-phase severe accidents, where hydrogen generation, fuel rod temperature, and their deterioration during these conditions are of vital importance. This paper presents a new system code, TLANESY, designed for the simulation of thermal–hydraulic systems with two-phase flow (mainly water) and with application in the analysis of severe accidents during the early phase. The computational implementation consists of fast-running numerical methods and their validation with experimental data from the QUENCH-01 experiment. The results showed an error with respect to the total hydrogen generation of approximately 0.6%. A stand-alone sensitivity analysis was also performed with some parameters related to the cladding, where it was shown that variation in the thermal conductivity by 15% can alter the total hydrogen generation by up to 5%, indicating that impurities in this material can have a significant impact on this Figure of Merit. Full article

The capability to discriminate among nuclear fuel properties is essential for a successful nuclear safeguard and security program. Accurate nuclear material identification is hindered due to challenges such as differing levels of enrichments, weak radiation signals in the case of fresh nuclear fuel, and complex self-shielding effects. This study explores the application of supervised machine learning algorithms to digitized radiation detector data for classifying signatures of special nuclear materials. Three scintillation detectors, an EJ-309 liquid scintillator, a CLYC crystal scintillator, and an EJ-276 plastic scintillator, were used to measure gamma-ray and neutron data from special nuclear material at the National Criticality Experiments Research Center (NCERC) at the National Nuclear Security Site (NNSS), at Nevada, USA. Radiation detector pulse data was extracted from the collected digitized data and applied to three separate supervised learning models: Random Forest, XGBoost, and a feedforward Deep Neural Network, chosen for their wide-spread use and distinct data ingest and processing analytics. Through model refinement, such as adding an additional parameter feature, an accuracy of greater than 95% was achieved. Analysis on model parameter feature importance revealed Countrate, which is the overall gamma-ray and neutron incidents for each detector, was the most influential parameter and essential to include for improved classification. Initial model versions not including the Countrate parameter feature failed to classify. Supervised learning models allow for measured gamma-ray and neutron pulse data to be used to develop effective identification and discrimination between material compositions of different fuel assemblies. The study demonstrated that traditional pulse shape parameters alone were insufficient for discriminating between special nuclear materials; the addition of Countrate markedly improved model accuracy but all models were heavily dependent on this specific feature, thus illustrating the need for alternative, more distinct parameter features. The machine learning development framework captured in this study will be beneficial for future applications in discriminating between different fuel enrichments and additives such as burnable poisons. Full article

The results of a recently developed Serpent 2 model of the Oregon State TRIGA^® Reactor (OSTR) are compared to the results from the OSTR MCNP^® model and measured values for reactor steady state behavior. This benchmark comparison is performed using fresh fuel isotopic data and measured reactivity values at the beginning of the current core life in 2008 to negate burnup uncertainties in calculated values. Reactivity bias, integral control rod reactivity worths, core excess reactivity, shutdown margin, the fuel temperature coefficient of reactivity, and kinetic parameters calculated by Serpent 2 and MCNP^® are compared to the measured values. The results from the Serpent 2 model strongly agree with both MCNP^® results and measured values and are within one standard deviation of each other in all cases, with the exception of the Serpent 2 calculated total control rod reactivity worth, which slightly under-predicts the total rod worth when compared to the measured value despite the MCNP^® and Serpent 2 calculated total rod worth values being within each other’s 1$\sigma$ standard deviations. Full article

Some of nuclear power’s primary detractors are the unique environmental challenges and impacts of radioactive wastes generated during fuel cycle operations. Key benefits of spent fuel reprocessing (SFR) are reductions in primary high active waste (HAW) masses, volumes, and lengths of radiotoxicity at the expense of secondary waste generation and high capital and operational costs. By employing advanced waste management and resource recovery concepts in SFR beyond the existing standard PUREX process, such as minor actinide and fission product partitioning, these challenges could be mitigated, alongside further reductions in HAW volumes, masses, and duration of radiotoxicity. This work assesses various current and proposed SFR and fuel cycle options as base cases, with further options for fission product partitioning of the high heat radionuclides (HHRs), rare earths, and platinum group metals investigated. A focus on primary waste outputs and the additional energy that could be generated by the reprocessing of high-burnup PWR fuel from Gen III(+) reactors using a simple fuel cycle model is used; the effects of 5- and 10-year spent fuel cooling times before reprocessing are explored. We demonstrate that longer cooling times are preferable in all cases except where short-lived isotope recovery may be desired, and that the partitioning of high-heat fission products (Cs and Sr) could allow for the reclassification of traditional raffinates to intermediate level waste. Highly active waste volume reductions approaching 50% vs. PUREX raffinate could be achieved in single-target partitioning of the inactive and low-activity rare earth elements, and the need for geological disposal could potentially be mitigated completely if HHRs are separated and utilised. Full article

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article

Pebble Bed Reactors (PBRs) represent a new generation of nuclear reactors. However, modeling TRi-structural ISOtropic (TRISO) fuel particles employed in PBRs presents a unique challenge in comparison to most conventional reactor designs. Rapid generation of different possible fuel particle configurations for Monte-Carlo simulations provides improved insights into the effects of particle distribution irregularities on the neutron economy. Defective pebbles could cause changes in the neutron flux in a nuclear reactor due to increased or decreased moderating effects. Different configurations of particle fuel also impact isotope production within the nuclear reactor. This study simulates several TRISO configurations representing limited capabilities of randomization algorithms, manufacturing defects configurations and/or special pebble design. All predictions are compared to an equivalent homogenized model used as baseline. The results show that the TRISO configuration has a non-negligible impact on the parameters under consideration. To explain these results, the ratio of the thermal flux of each model to the thermal flux of the homogeneous model is calculated. A clear pattern is observed in the data: as irregularities in the moderator medium emerge due to the distribution of TRISO particles, the neutron spectrum softens, leading to higher values of k_∞ and better fuel utilization. This dependence of the spectrum on the TRISO configuration is used to explain the pattern observed in the depletion calculation. The results open the possibility of optimizing the TRISO configuration in manufactured pebbles for fuel utilization and safeguards. Future work should focus on full core simulations to determine the extent of these findings. Full article

Burnable poisoning and fuel enrichment zoning are two techniques often combined in order to optimize the fuel assembly behavior during the burnup cycle. In the present work, these two techniques will be applied to the 2D optimization of the fuel assembly conceptual design for the supercritical water-cooled reactor developed in the framework of the Joint European Canadian Chinese development of Small Modular Reactor Technology project, funded within the Euratom Research and Training programme 2019–2020. The initial configuration of the fuel assembly does not include any burnable absorbers and uses a homogeneous fuel enrichment of 7.5% in ²³⁵U. The infinite multiplication factor, $k_{\infty }$, starts from approximately 1.32 and drops, almost linearly, to 1.0 after a burnup of 40.0 MWd·kg⁻¹. The uniform enrichment is, however, responsible for a pin-power peaking factor that with fresh fuel starts from 1.32 and reduces to 1.08 at the end of the burnup cycle. A simplified analytical model is developed to assess the effect of different lumped burnable absorbers on the time dependence of the assembly $k_{\infty }$. It is shown that using an adequate number of B₄C rods, positioned in the outer wall of the fuel assembly, together with a suitable distribution of six different ²³⁵U enrichments, it allows for obtaining an assembly $k_{\infty }$ factor that starts from 1.11 at the beginning of the cycle and remains quite constant over a large fraction of the burnup cycle. Moreover, the pin-power peaking factor is reduced to 1.03 at the beginning of the cycle and remains almost unchanged until the end of the burnup cycle. Full article

Powder injection molding (PIM) has been used to produce nearly net-shaped samples of tungsten-based alloys. These alloys have been previously shown to have favorable characteristics when compared with standard ITER-grade tungsten. Six different alloys were produced with this method: W-1TiC, W-2Y₂O₃, W-3Re-1TiC, W-3Re-2Y₂O₃, W-1HfC and W-1La₂O₃-1TiC. These were tested alongside ITER-grade tungsten in the PSI-2 linear plasma device under ITER-relevant plasma and heat loads to assess their suitability for use in a fusion reactor. All materials showed good behavior when exposed to the lower pulse number tests (≤1000 ELM-like pulses), although standard tungsten performed slightly better, with no observable difference in surface roughness. High-power shots, namely one laser pulse of 1.6 GWm⁻², revealed that samples containing yttria are more prone to melting and droplet ejection. After high pulse number tests (10,000 and 100,000 pulses), with and without plasma, the reference tungsten showed the most cracking and highest surface roughness of all materials, while the PIM samples seemed to have a higher resistance to cracking. This can be attributed to the higher ductility of these alloys, particularly those containing rhenium. This means that tungsten-based alloys, whether produced via PIM or other methods, could potentially be used in certain areas of a fusion reactor. Full article

This work presents the mathematical frameworks of the “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Volterra-Type” (1st-FASAM-NIDE-V) and the “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Volterra-Type” (2nd-FASAM-NIDE-V). It is shown that the 1st-FASAM-NIDE-V methodology enables the efficient computation of exactly-determined first-order sensitivities of the decoder response with respect to the optimized NIDE-V parameters, requiring a single “large-scale” computation for solving the 1st-Level Adjoint Sensitivity System (1st-LASS), regardless of the number of weights/parameters underlying the NIE-net. The 2nd-FASAM-NIDE-V methodology enables the computation, with unparalleled efficiency, of the second-order sensitivities of decoder responses with respect to the optimized/trained weights involved in the NIDE-V’s decoder, hidden layers, and encoder, requiring only as many “large-scale” computations as there are non-zero first-order sensitivities with respect to the feature functions. These characteristics of the 1st-FASAM-NIDE-V and 2nd-FASAM-NIDE-V are illustrated by considering a nonlinear heat conduction model that admits analytical solutions, enabling the exact verification of the expressions obtained for the first- and second-order sensitivities of NIDE-V decoder responses with respect to the model’s functions of parameters (weights) that characterize the heat conduction model. Full article

Recent advances in high-fidelity modeling, numerical computing, and data science have spurred interest in model-data integration for nuclear reactor applications. While machine learning often prioritizes data-driven predictions, this study focuses on data assimilation (DA) to synergize physical models with measured data, aiming to enhance predictive accuracy and reduce uncertainties. We implemented deterministic DA methods—Kalman filter (KF) and three-dimensional variational (3D-VAR)—in a one-dimensional single-phase natural circulation loop and extended 3D-VAR to RELAP5, a system code for two-phase loop analysis. Unlike surrogate-based or model-reduction strategies, our approach leverages full-model propagation without relying on computationally intensive sampling. The results demonstrate that KF and 3D-VAR exhibit robustness against varied noise types, intensities, and distributions, achieving significant uncertainty reduction in state variables and parameter estimation. The framework’s adaptability is further validated under oceanic conditions, suggesting its potential to augment baseline models beyond conventional extrapolation boundaries. These findings highlight DA’s capacity to improve model calibration, safety margin quantification, and reactor field reconstruction. By integrating high-fidelity simulations with real-world data corrections, the study establishes a scalable pathway to enhance the reliability of nuclear system predictions, emphasizing DA’s role in bridging theoretical models and operational demands without compromising computational efficiency. Full article

In current and future fusion devices, detecting hydrogen isotopes, particularly tritium and deuterium, implanted or redeposited on the surface of Plasma-Facing Components (PFCs) will be increasingly important to ensure safe machine operations. The Laser-Induced Breakdown Spectroscopy (LIBS) technique has proven capable of performing this task directly in situ, without handling or removing PFCs, thus limiting analysis times and increasing the machine’s duty cycle. To increase sensitivity and the ability to discriminate between isotopes, LIBS analysis can be performed under different background gases at atmospheric pressure, such as air, He, and Ar. In this work, we present the results obtained on tungsten coatings enriched with deuterium and/or hydrogen as a deuterium–tritium nuclear fuel simulant, measured with the LIBS technique in air, He, and Ar at atmospheric pressure, and discuss the pros and cons of their use. The results obtained demonstrate that both He and Ar can improve the LIBS signal resolution of the hydrogen isotopes compared to air. However, using Ar has the additional advantage that the same procedure can also be used to detect He implanted in PFCs as a product of fusion reactions without any interference. Finally, the LIBS signal in an Ar atmosphere increases in terms of the signal-to-noise ratio (SNR), enabling the use of less energetic laser pulses to improve performance in depth profiling analyses. Full article

While traditional risk importance measures in probabilistic risk assessment are effective for ranking safety-significant components, they often overlook critical aspects such as the timing of accident progression and consequences. Dynamic probabilistic risk assessment offers a framework to quantify such risk information, but standardized approaches for estimating risk importance measures remain underdeveloped. This study addresses this gap by: (1) reviewing traditional risk importance measures and their regulatory applications, highlighting their limitations, and introducing newly proposed risk-triplet-based risk importance measures, consisting of timing-based worth, frequency-based worth, and consequence-based worth; (2) conducting a case study of Level 2 dynamic probabilistic risk assessment using the Japan Atomic Energy Agency’s RAPID tool coupled with the severe accident code of MELCOR 2.2 to simulate a station blackout scenario in a boiling water reactor, generating probabilistically sampled sequences with quantified timing, frequency, and consequence of source term release; (3) demonstrating that the new risk importance measures provide differentiated insights into risk significance, enabling multidimensional prioritization of systems and mitigation strategies; for example, the timing-based worth quantifies the delay effect of mitigation systems, and the consequence-based worth evaluates consequence-mitigating potential. This study underscores the potential of dynamic probabilistic risk assessment and risk-triplet-based risk importance measures to support risk-informed and performance-based regulatory decision-making, particularly in contexts where the timing and severity of accident consequences are critical. Full article

The study of scalar field theories like the chameleon field model is of increasing interest due to the Universe’s accelerated expansion, which is believed to be caused in part by dark energy. These fields can elude experimental bounds set on them in high-density environments since they interact with matter in a density-dependent way. This paper analyzes the effect of chameleon fields on the quantum gravitational states of ultra-cold neutrons (UCNs) in qBounce experiments with mirrors. We discuss the deformation of the neutron wave function due to chameleon interactions and quantum systems in potential wells from gravitational forces and chameleon fields. Unlike other works that aim to put bounds on the chameleon field parameters, this work focuses on the quantum mechanics of the chameleonic neutron. The results deepen our understanding of the interplay between quantum states and modified gravity, as well as fundamental physics experiments carried out in the laboratory. Full article