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A multi-modal approach combining in situ X-ray absorption spectroscopy, optical spectroscopy, and ab initio molecular dynamics simulations was used to investigate and understand nickel metal speciation in molten salt systems.
Neutron Resonance Transmission Analysis (NRTA) is a spectroscopic technique which uses the resonant absorption of neutrons in the epithermal range to infer the isotopic composition of an object. This spectroscopic technique has relevance in many traditional fields of science and nuclear security. NRTA in the past made use of large, expensive accelerator facilities to achieve precise neutron beams, significantly limiting its applicability. Here, we describe a series of NRTA experiments where we use a compact, low-cost deuterium-tritium (DT) neutron generator to produce short neutron beams (2.6 m) along with a 6 Li-glass neutron detector. The time-of-flight spectral data from five elements – silver, cadmium, tungsten, indium, and 238 U – clearly show the corresponding absorption lines in the 1-30 eV range. The experiments show the applicability of NRTA in this simplified configuration, and prove the feasibility of this compact and low-cost approach. This could significantly broaden the applicability of NRTA, and make it practical and applicable in many fields, such as material science, nuclear engineering, and arms control.
Recent advances in selective laser melting (SLM) 3D printing technology allow additive manufacture of lower hybrid current drive (LHCD) RF launchers from a new material, Glenn Research Copper 84 (GRCop-84), a Cr 2 Nb (8 at. % Cr, 4 at. % Nb) precipitation hardened alloy, in configurations unachievable with conventional machining. Tensile strength and thermal conductivity are compared to other competing high conductivity copper alloys for fusion use. Swelling and conductivity at 100 DPA when exposed to the Hanford Fast Flux Test Facility neutron spectrum is predicted in the 1–2 % and 65-70% IACS range, respectively, by comparison to copper alloys with similar precipitate and grain size. Finally, self-similar ion irradiation of a GRCop-84 target at 430 °C to 20 DPA resulted in no noticeable void formation in FIB cross sections or TEM lamella.
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Void formation is an important aspect of irradiation response of metals. In situ transmission electron microscopy observation for void evolution during irradiation is an effective technique for studying void evolution. However, the amount of data collected during in situ studies drastically overwhelm the current capability for manual data analyses. Here, we used a data-driven approach where a convolutional neural network combined with greedy matching to detect and track nanovoid evolutions and migrations. This approach was able to discover the surprising phenomena of void size fluctuation and shrinkage during irradiation of Cu with pre-existing nanovoids. Phase–field simulations revealed the fundamental mechanism behind this in situ observed phenomenon of void size fluctuation.
In this work, we present a software framework, SπRITROOT, which is capable of track reconstruction and analysis of heavy-ion collision events recorded with the SRIT time projection chamber. The track-fitting toolkit GENFIT and the vertex reconstruction toolkit RAVE are applied to a box-type detector system. A pattern recognition algorithm which performs helix track finding and handles overlapping pulses is described. The performance of the software is investigated using experimental data obtained at the Radioactive Isotope Beam Facility (RIBF) at RIKEN. This work focuses on data from 132 Sn + 124 Sn collision events with beam energy of 270 AMeV. Particle identification is established using < dE/dx >and magnetic rigidity, with pions, hydrogen isotopes, and helium isotopes.
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Condensate storage tanks (CSTs) in nuclear power plants (NPPs) are classified as critical equipment capable of surviving strong shaking in a design basis earthquake to assure the ability to subsequently provide cooling water. Structural modeling and dynamic analysis of CSTs are complicated due to fluid-structure interaction (FSI) and coupling issues, which could have significant effects on seismic response. The capability of a number of approximate models to capture seismic response is investigated using dynamic analysis results from detailed finite element models. Modal analyses and time history analyses are carried out using both 2D and 3D models to predict the dynamic behavior of CSTs as a function of ground motion intensity. Although 2D simplified models can be used to quickly evaluate the dynamic response of CSTs when there is substantial margin to failure, a detailed 3D model is required when it is necessary to examine a limit state associated with the failure mode of the tank. The findings show good agreement between the natural frequencies of convective modes determined from the 2D and 3D CST models. The difference between the frequencies of impulsive modes were found to be equal to or less than 25%.
In response to continuously declining availability of fossil fuels, nuclear power has become an important alternative source of power generation. Growing application of nuclear power has relieved the shortage of electricity. However, it is also accompanied with new drawbacks, such as radioactive waste, and potential catastrophic consequences in case of major accidents. For nuclear power plants, severe accidents mostly originate from the reactor, making the reliability of the reactor of paramount importance to the overall safety of a nuclear power plant. Massive heat generation through nuclear fission/fusion and heat exchange render the whole reactor operating at extremely high temperatures (i.e., 900–1000 °C), which, in conjunction with large load from subsystems/ components made of special heavy alloys, renders the reactor under the risk of self-welding, a phenomenon that could lead to strong bonding between components and paralyze the operability of the nuclear reactor. This paper investigates the self-welding behavior of Inconel 617, a primary candidate nickel alloy for key components of the next generation gas cooled nuclear plant, inside a controlled atmospheric furnace with preset static load, simulating the high-pressure high-temperature conditions inside nuclear reactors. Therefore, test results show that Inconel 617 experiences self-welding through common oxide zone at the interface, and it has remarkable bonding strength, requiring 4114 N (57.15 MPa) and 4338 N (60.25 MPa) to break apart a mated sample pair, under an apparent contact pressure of 0.341 MPa after 50-h aging, in helium and air atmospheres, respectively. The higher oxygen presence in air results in slightly stronger oxide bond at the interface, compared to helium, and the bond strength increases with dwell time.
Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.
Seismic isolation can mitigate earthquake demands on safety-class equipment and is being considered for application to some advanced nuclear reactors. For tall, slender vessels that could represent an advanced reactor, a steam generator or a heat exchanger, mid-height seismic isolation has been shown to be beneficial and practical. Here, this paper focuses on numerical modeling of a mid-height isolated tall, slender vessel with two primary goals 1) investigate and quantify the benefits of mid-height seismic isolation for a range of support structure stiffnesses, isolation systems, and seismic inputs, and 2) provide analysis and design recommendations for isolation of safety-class equipment. Results of response-history analysis were in good agreement with experimental measurements and demonstrated that mid-height isolation can substantially reduce seismic demands on tall, slender vessels for a range of support structure stiffnesses, isolation systems, and seismic inputs. Importantly, the reductions in horizontal spectral accelerations in the mid-height isolated vessel from the non-isolated condition were not affected by the stiffness of the support structure. Recommendations are made for analysis and design of isolated equipment and testing of isolators used for equipment protection in nuclear facilities, which are also applicable to other industries.
Further understanding of tritium transport mechanisms in the combined molten fluoride salt and graphite environment is necessary for the design and licensing of a Fluoride-Salt-Cooled High-Temperature Reactor (FHR). The three in-core fluoride salt irradiations completed at the Massachusetts Institute of Technology Reactor (MITR) are a useful parallel for studying transport phenomena expected in a FHR environment. During the irradiations, evolution of tritium from the flibe salt was monitored and compared to the calculated total generation rate. A difference of 22 ± 10% between the integrated calculated tritium generation rate and the total release was measured for the third MITR irradiation (FS-3). The fraction of tritium which was not released from the salt could be explained by tritium retention in graphite. Additionally, for post irradiation examination, a thermal desorption furnace was used to heat nuclear graphite samples in order to release and measure retained tritium. The desorption analysis in this work utilized seven subsections of graphite from the second salt irradiation (FS-2); three from a disc of IG-110U and four from ARB matrix graphite. Observed desorption versus temperature as well as total tritium content in the samples after irradiation indicate that the graphites were not volumetrically saturated with tritium, but rather tritium retention was likely limited to the near-surface region. Measurements of the samples resulted in 2.90 ± 0.29 μCi/mm 2 of tritium retained by IG-110U and 1.83 ± 0.31 μCi/mm 2 for ARB during the 300 h FS-2 in-core irradiation. Based on the desorption measurements, the estimated total tritium retention in graphite from the FS-2 samples is consistent with the tritium release measurements from the FS-3 experiment.
Reliably predicting nuclear properties across the entire chart of isotopes is important for applications ranging from nuclear astrophysics to superheavy science to nuclear technology. To this day, however, all the theoretical models that can scale at the level of the chart of isotopes remain semiphenomenological. Because they are fitted locally, their predictive power can vary significantly; different versions of the same theory provide different predictions. Bayesian model mixing takes advantage of such imperfect models to build a local mixture of a set of models to make improved predictions. Earlier attempts to use Bayesian model mixing for mass table calculations relied on models treated at single-reference energy density functional level, which fail to capture some of the correlations caused by configuration mixing or the restoration of broken symmetries. In this study we have applied Bayesian model mixing techniques within a multireference energy density functional (MR-EDF) framework. We considered predictions of two-particle separation energies from particle number projection or angular momentum projection with four different energy density functionals—a total of eight different MR-EDF models. We used a hierarchical Bayesian stacking framework with a Dirichlet prior distribution over weights together with an inverse log-ratio transform to enable positive correlations between different models. We found that Bayesian model mixing provides significantly improved predictions compared to the participating models. Published by the American Physical Society 2025
Neutron activation analysis (NAA) is a powerful technique for identifying and quantifying trace elements in materials. However, challenges such as high dead times, spectral interferences, and high Compton continuum often arise. This study employs a Compton suppression system (CSS) to enhance NAA sensitivity by reducing the Compton continuum, thereby improving the peak-to-Compton ratio. National Institute of Standards and Technology certified reference materials 1632d, 1633c, and Canadian National Research Council TORT-1 were irradiated in a thermal and epithermal neutron flux under various irradiation, decay and counting times and analyzed using high-resolution gamma-ray spectroscopy with and without Compton suppression. The reduction factor was calculated to evaluate the effectiveness of the CSS, demonstrating significant background reduction and improved detection limits for trace elements. Additional experiments with a 137Cs point source demonstrated the impact of detector-source geometry on system performance, showing a decrease in the reduction factor as the source was moved further from the NaI detector. An optimum distance between the source and HPGe detector was observed, yielding the highest peak-to-Compton ratio. The results highlight the CSS's ability to minimize spectral interference and enhance elemental identification.
Short highlights in the following areas are included: Nuclear Security; Science, Technology and Engineering; Mission Operations; and, Community Relations.
A broad range of applications in nuclear safeguards and security can benefit from compact, fast neutron imagers with large angular acceptance. However, accurate 3D multi-vertex reconstruction in a monolithic detector remains a significant barrier to realization. Current approaches, such as the centroiding approach and the use of shadow masks, have not yet resulted in a successful demonstration in a light-starved environment. This project explored the use of cutting-edge technology—a light field camera—which inherently preserves both the spatial and directional information of incident light. Such technology can, in principle, resolve multi-vertex events and offer accurate position reconstruction in 3D with relatively simple readout electronics. Since the project's inception in May 2023, we have built an experimental setup to calibrate and characterize a commercial light-field camera (Lytro Illum). We assessed its 3D event reconstruction capability in a relatively low-light intensity environment by analyzing the cross-correlation of a series of 2D images of a characterized tunable light source taken at various distances. A sub-cm resolution, lower than typical neutron interaction separations in a compact scintillator volume, was observed with the LED light source, suggesting the promising capability of its nominal optics design to provide the adequate resolution required for a compact monolithic neutron directional detector. However, using a light-field-based readout for a neutron camera requires incorporating an ultra-low-noise light sensor, which is beyond the scope of this project. We also initiated the development of a 3D light-field-based reconstruction algorithm tailored to sparse scenes, as expected from particle interactions in a scintillator medium. Additionally, we demonstrated the capability of the algorithm to replicate the ground truth. Finally, we began the development of a neutron directional detector simulation to determine the performance criteria for light-field-based reconstruction that allows for good neutron directional reconstruction. This Feasibility Study has led to a successful follow-up project under the DNN R&D innovation portfolio starting May 2024.
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