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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Calculation of the Thermal Neutron Scattering Cross-Section of Solids Using OCLIMAX

The thermal neutron scattering cross-section of a solid depends on the energy (or wavelength) of the incident neutrons. Devising a method to calculate the energy dependence from first principles, without the approximations built in the scattering theory, has been a major undertaking in nuclear engineering. Here, we demonstrate such a calculation method using the program OCLIMAX. In this work, our approach eliminates various approximations and limitations involved in a regular calculation with the LEAPR module of NJOY code, and the results are compared with available experimental and theoretical data. It is also demonstrated how additional insight can be obtained from the calculated full dynamical structure factor. The results reported here show the great potential and excellent platform provided by OCLIMAX for future development in the study of neutron thermalization in solid materials for different applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Learning nuclear cross sections across the chart of nuclides with graph neural networks

We explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section data into a latent space using either variational autoencoders (VAEs) or implicit neural representations (INRs). Then, we train graph neural networks (GNNs) on the resulting embeddings to predict missing values across the nuclear chart by leveraging the topological structure of neighboring isotopes. We demonstrate accurate cross section predictions within a 9 × 9 block of missing nuclei. We also find that the optimal GNN training strategy depends on the type of latent representation used, with VAE embeddings performing best under end-to-end optimization in the original space, while INR embeddings achieve better results when the GNN is trained only in the latent space. Furthermore, using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on the representation encoding of cross sections combined with graph neural networks hold significant potential in augmenting nuclear theory models, e.g., by providing reliable estimates of covariances of cross sections, including cross-material covariances.

Machine learning↗
DOE OSTI2020

Consistency of $^{16}O(n,α)$ cross sections

The evaluated $^{16}O(n,α)$ cross sections in the ENDF/B-VIII.0 nuclear data library remain uncertain because of systematic discrepancies in the measured data. In the energy region below the first excited state, R-matrix analyses rely heavily on the inverse reaction, and in particular, they rely on the measured $^{13}C(α,n)^{16}O$ cross sections reported by Bair et al. in 1973 and Harissopulos et al. in 2005. The Harissopulos cross section values are systematically lower than those previously reported by Bair. Here, drawing on the available experimental information, this paper briefly describes and demonstrates that the two sets of measured cross sections are consistent.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Windowed multipole representation of R -matrix cross sections

Nuclear cross sections are basic inputs to any nuclear computation. Campaigns of experiments are fitted with the parametric R-matrix model of quantum nuclear interactions, and the resulting cross sections are documented—both pointwise and as resonance parameters (with uncertainties)—in standard evaluated nuclear data libraries (ENDF, JEFF, BROND, JENDL, CENDL, TENDL): these constitute our common knowledge of fundamental low-energy nuclear cross sections. In the past decade, a collaborative effort has been deployed to establish a new nuclear cross-section library format—the Windowed Multipole Library—with the goal of considerably reducing the computational cost of cross-section calculations in nuclear transport simulations. This work lays the theoretical foundations underpinning these efforts. From general R-matrix scattering theory, we derive the windowed multipole representation of nuclear cross sections. Though physically and mathematically equivalent to R-matrix cross sections, the windowed multipole representation is particularly well suited for subsequent temperature treatment of angle-integrated cross sections, in particular Doppler broadening, which is the averaging of cross sections over the thermal motion of the target atoms. Doppler broadening is of critical importance in neutron transport applications, as it ensures the stability of many nuclear reactors (negative thermal reactivity). Yet, Doppler broadening of nuclear cross sections has been a considerable bottleneck for nuclear transport computations, often requiring memory-costly pretabulations. We show that the windowed multipole representation can perform accurate Doppler broadening analytically (up to the first reaction threshold), from which we derive cross-section temperature derivatives to any order—all computable on the fly (without precalculations stored in memory). Furthermore, we here establish a way of converting the R-matrix resonance parameters uncertainty (covariance matrices) into windowed multipole parameters uncertainty. We show that generating stochastic nuclear cross sections by sampling from the resulting windowed multipole covariance matrix can reproduce the cross-section uncertainty in the original nuclear data file. The windowed multipole representation is therefore a novel nuclear physics formalism able to generate Doppler broadened stochastic nuclear cross sections on the fly, unlocking breakthrough computational gains for nuclear computations. Through this foundational paper, we hope to make the windowed multipole representation accessible, reproducible, and usable for the nuclear physics community, as well as provide the theoretical basis for future research on expanding its capabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Correlated $\ n-γ$ angular distributions from the $\ Q$ = 4.4398 MeV 12 C ($\ n, n' γ$) reaction for incident neutron energies from 6.5 MeV to 16.5 MeV

Neutron scattering cross sections and angular distributions represent one of the most glaring sources of uncertainty in calculations of nuclear systems. Even simple nuclei like 12 C show indications of errors in nuclear databases for scattering reactions. Measurements of inelastic neutron scattering have historically measured either the scattered neutrons or the nuclear deexcitation $\ γ$ emission. Only a very small number of experiments attempted correlated measurements of both the neutron and $\ γ$ data simultaneously, even though these $\ n-γ$ correlations could be essential for understanding particle transport in nuclear systems. In this work we describe a measurement of the $\ n, γ$, and correlated $\ n-γ$ angular distributions from the $\ Q$ = 4.4398 MeV 12 C ($\ n, n'γ$) reaction in a single experiment using an EJ-309 liquid scintillator detector array with wide angular coverage, and with a continuous incident neutron energy range from 6.5 to 16.5 MeV. We also provide a thorough covariance description of these results, including normalization of the probability distribution. While the measured n distributions agree well with the relatively large number of available literature measurements, there are comparatively very few measurements of the γ distributions from this reaction. However, our data support the presence of a nonzero α 4 Legendre polynomial component of the γ angular distribution suggested in past measurements, which is currently not incorporated in the ENDF/B-VIII.0 library despite the use of these same literature data for evaluation of the 12 C ($\ n, n'γ$) cross section. The correlated $\ n-γ$ distribution measurements are limited to three measurements at incident neutron energies near 14 MeV. Our results do not generally agree with any of these literature measurements. We observe clear indications of significant changes in the $\ n$ distribution for specific $\ γ$-detection angles and vice versa especially near thresholds for other reaction channels, which shows the potential for significant bias in experiments that, for example, tag on inelastic scattering using a single or small number of $\ γ$ -detection angles and could impact particle transport calculations.

6 ≤ A ≤ 19↗
DOE OSTI2022

Measurement of the U 235 ( n , f ) prompt fission neutron spectrum from 10 keV to 10 MeV induced by neutrons of energy from 1 MeV to 20 MeV

The characterization of fission-driven nuclear systems primarily relies on calculations of neutron-induced chain reactions, and these calculations require evaluated nuclear data as input. Calculation accuracy heavily depends on input nuclear data evaluation accuracy, and thus high precision on the experimental input to the nuclear data evaluation is essential for fundamental quantities like the energy spectrum of neutrons emitted from neutron-induced fission (i.e., the prompt fission neutron spectrum, PFNS). Despite decades of measurement efforts, prior to the measurements described in this work there were only three literature data sets for the 235 U(n,f) PFNS at incident neutron energies above 1.0 MeV considered reliable for inclusion in nuclear data evaluations and no reliable data sets above 3.0 MeV incident neutron energy. In this work we report on new measurements of the 235 U(n,f) PFNS spanning a grid of 1.0–20.0 MeV in incident neutron energy and 0.01–10.0 MeV in outgoing (PFNS) neutron energy. These measurements were carried out at the Weapons Neutron Research facility at the Los Alamos Neutron Science Center and used a multifoil parallel-plate avalanche counter target with both a Li-glass and a liquid scintillator detector array in separate experiments to span the quoted outgoing neutron energy ranges. The PFNS results are shown in terms of the energy spectra themselves as well as the average PFNS energy $(\langle{E}\rangle)$ and ratios of $\langle{E}\rangle$ at forward and backward angles. Here, the results are compared with literature data and selected nuclear data evaluations. Generally, the data agree with the ENDF/B-VIII.0 evaluation below 5.0-MeV incident neutron energy and more closely with the JEFF-3.3 evaluation above 5.0 MeV, though no evaluations considered for comparison in this work agree with the data across all of the incident and outgoing neutron energies shown, especially in regions where the third-chance fission process becomes available. Additionally, we show a ratio of the present PFNS results for 235 U(n, f) with a recent and highly correlated experiment to measure the 239 Pu(n, f) PFNS at the same experimental facility and with nearly identical equipment and analysis procedures. Many observations reported in this work are the first of their kind and represent significant advancements for knowledge of the 235 U(n, f) PFNS.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Joint Measurement of the 235 U Antineutrino Spectrum by PROSPECT and STEREO

The PROSPECT and STEREO collaborations present a combined measurement of the pure 235 U antineutrino spectrum, without site specific corrections or detector-dependent effects. The spectral measurements of the two highest precision experiments at research reactors are found to be compatible with χ 2 /ndf = 24.1/21, allowing a joint unfolding of the prompt energy measurements into antineutrino energy. This $\bar{ν}_e$ energy spectrum is provided to the community, and an excess of events relative to the Huber model is found in the 5-6 MeV region. When a Gaussian bump is fitted to the excess, the data-model χ 2 value is improved, corresponding to a 2.4σ significance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Results From Invoking Artificial Neural Networks to Measure Insider Threat Detection & Mitigation

Advances on differentiating between malicious intent and natural “organizational evolution” to explain observed anomalies in operational workplace patterns suggest benefit from evaluating collective behaviors observed in the facilities to improve insider threat detection and mitigation (ITDM). Advances in artificial neural networks (ANN) provide more robust pathways for capturing, analyzing, and collating disparate data signals into quantitative descriptions of operational workplace patterns. In response, a joint study by Sandia National Laboratories and the University of Texas at Austin explored the effectiveness of commercial artificial neural network (ANN) software to improve ITDM. Overall, this research demonstrates the benefit of learning patterns of organizational behaviors, detecting off-normal (or anomalous) deviations from these patterns, and alerting when certain types, frequencies, or quantities of deviations emerge for improving ITDM. Evaluating nearly 33,000 access control data points and over 1,600 intrusion sensor data points collected over a nearly twelve-month period, this study's results demonstrated the ANN could recognize operational patterns at the Nuclear Engineering Teaching Laboratory (NETL) and detect off-normal behaviors—suggesting that ANNs can be used to support a data-analytic approach to ITDM. Several representative experiments were conducted to further evaluate these conclusions, with the resultant insights supporting collective behavior-based analytical approaches to quantitatively describe insider threat detection and mitigation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Covariate Dependent Sparse Functional Data Analysis

This study proposes a method to incorporate covariate information into sparse functional data analysis. The method aims at cases where each subject has a limited number of longitudinal measurements and is associated with static covariates. This research is motivated by several use cases in practice. One representative example is void swelling, a nuclear-specific material degradation mechanism. Void swelling is affected by many covariates, including alloy composition and irradiation type. How to accurately model the complicated joint effects of such covariates on the swelling process is the key to mitigating the effect of swelling and ensuring safe operation. Unlike most of the existing methods, the proposed method can handle high-dimensional covariates with the informative covariate identification procedure and sparse and irregularly spaced measurements, that is, does not require complete or dense observations. The main innovation of the proposed method is that we model the variation coming from covariates and the variation left conditioned on covariates, such that the functional principal component analysis and Gaussian process can be conducted in a unified manner. Further, we also propose a systematic approach to identify important covariates in the hypothesis testing context. The methodology is demonstrated on applications in nuclear engineering and healthcare and simulation studies.

42 ENGINEERING↗
DOE OSTI2024

Near-complete extraction of maximum stored energy from large-core fibers using coherent pulse stacking amplification of femtosecond pulses

High field science relies on ultrashort pulse lasers with multi-joule pulse energies for studying light–matter interactions under extreme conditions and for driving particle accelerators and secondary radiation sources of x rays, gamma rays, neutrons, positrons, muons, and protons. Next-generation laser drivers will require a 10 3 -10 4 times increase in pulse repetition rates, producing multi-joule energies at multi-kilowatt average powers to enable practical applications in nuclear engineering, advanced materials, medicine, biology, homeland security, and high-energy physics. Spatially coherently combined femtosecond fiber lasers are recognized as a pathway to these next-generation drivers, with significant practical advantages including high efficiency and the possibility of compact integration. However, chirped pulse amplification in fibers is capable of extracting only a small fraction (usually ~1%) of the maximum stored energy. Here we demonstrate near-complete maximum stored energy extraction with low accumulated nonlinearity from a large-core fiber amplifier using coherent pulse stacking amplification. We have amplified a 81-pulse stacking burst in a 85 µm core chirally coupled core Yb-doped fiber, extracting up to 9.5 mJ (~90% of stored energy) with < 4.5 radians of accumulated nonlinear phase, temporally combined this burst into a single pulse, and achieved 4.2 mJ pulses of 313 fs bandwidth-limited duration after compression. This represents, to our knowledge, the highest energy extracted and compressed into a femtosecond pulse from a single fiber amplifier, enabling approximately two orders of magnitude size reduction of future high-energy coherently spatially combined fiber laser arrays.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

MontePy: a Python library for reading, editing, and writing MCNP input files.

The Monte Carlo N-Particle (MCNP) radiation transport code is a highly capable and accurate code with a long legacy. MCNP uses the Monte Carlo simulation process to simulate the path of particles (e.g., neutrons, photons, charged particles, etc.), and their interaction with materials. It is widely used in nuclear engineering, high-energy physics, and other fields. Its origins in the mid-twentieth century predate many modern software conventions. MCNP users provide an input file to MCNP, which it then uses to create an internal representation of the simulation problem. These input files originally had to be stored as punchcard decks, and the user manual still uses the terminology of cards and decks, despite moving beyond punchcards. MCNP predates nearly all modern human readable markup or data serialization languages, such as the extensible Markup Language (XML), the Standard Generalized Markup Language (SGML), YAML (YAML Ain’t Markup Language), and Javascript Object Notation (JSON). Due to this, MCNP uses an entirely custom defined syntax language for its input, making off-the-shelf libraries for XML, YAML, and JSON impossible to use for scripting various operations on MCNP input files (Kulesza et al., 2022).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Phase I Closeout Report: Invoking Artificial Neural Networks to Measure Insider Threat Mitigation

Researchers from Sandia National Laboratories (Sandia) and the University of Texas at Austin (UT) conducted this study to explore the effectiveness of commercial artificial neural network (ANN) software to improve insider threat detection and mitigation (ITDM). This study hypothesized that ANNs could be "trainee to learn patterns of organizational behaviors, detect off-normal (or anomalous) deviations from these patterns, and alert when certain types, frequencies, or quantities of deviations emerge. The ReconaSense ANN system was installed at UT's Nuclear Engineering Teaching Laboratory (NETL) and collected 13,653 access control data points and 694 intrusion sensor data points over a three-month period. Preliminary analysis of this baseline data demonstrated regularized patterns of life in the facility, and that off-normal behaviors are detectable under certain situations -- even for a facility with anticipated highly non-routine, operational behaviors. Completion of this pilot study demonstrated how the ReconaSense ANN could be used to identify expected operational patterns and detect unexpected anomalous behaviors in support of a data-analytic approach to ITDM. While additional studies are needed to fully understand and characterize this system, the results of this initial study are overall very promising for demonstrating a new framework for ITDM utilizing ANNs and data analysis techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Rules of Use Report Engineering evaluations of nuclear material storage containers against the packaging requirements at TA-55

A set of obligations in the yearly storage container surveillance program, ensures that a retrospective approach is applied with loaded containers in inventory to confirm compliance with all “users” to ensure nuclear material storage container requirements are being followed. Validation that packaged containers are properly used, within a specific set of container types prescribed “bounding conditions of use,” is based on meeting requirements as identified in operating procedures associated with TA55-DOP-091, TA-55 Nuclear Material Packaging, and PA-RD-01022, Nuclear Material Packaging Requirements. The surveillance plan obligation requires the application of local area nuclear material accountability software or (LANMAS) to produce queries. The data queries are used to assess attributes of containers in storage these investigations are conducted on a bi-annual basis. This rules of use (ROU) compliance process ensures proper usage of storage containers as containment systems and therefore provides effective worker protection. It is structured as a supporting effort by implementing aspects of PA-AP-01207, Nuclear Material Container Safety Management at TA-55.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Improved Axisymmetric and High Temperature Material Structural Modeling in MOOSE and NEML

This report describes improvements made to the solid mechanics formulation in the MOOSE open source finite element simulation environment and the open source Nuclear Engineering Material model Library (NEML) for mechanical constitutive models. The focus of these improvements is to improve the usability and performance of simulations involving one or both pieces of software. Specifically, this work completes a new system for solid mechanics simulations in the MOOSE ecosystem providing exact linearizations and optimal (quadratic) convergence, for a variety of coordinate systems and material types, including large deformation simulations. This work then provides users a framework to build highly efficient mechanical simulations of structures or materials or to couple in additional MOOSE physics modules to build complex, scalable multiphysics simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SISGR: Chemomechanics of Far-From-Equilibrium Interfaces (COFFEI)

Portable, reliable, and deployable devices for energy storage and conversion require fundamental changes in design of solid-state composites comprising ceramics and metals. These materials comprise the electrodes and electrolytes of next-generation solid-oxide fuel cells and all solid-state batteries, forming solid-state functional composites. The advent of solid-state batteries – which replace liquid electrolytes with solid electrolytes capable of lithium ion transport for reliable energy storage in portable batteries – and the increased demand space for all solid-state fuel cells capable of oxygen reduction at intermediate temperatures remain important challenges for improved material stability and decreased system cost. However, little is understood about three fundamental facets of materials that enable such solid-state energy applications. First, how do such materials deform, fracture, or delaminate under operando conditions? Second, how does such mechanical deformation limit or facilitate electronic and ionic transport within and across such material interfaces? Third, how we can predictably design interface-rich composites to engineer both structural and electrochemical stability? This COFFEI Group comprised expertise from Materials Science & Engineering and Nuclear Science & Engineering to integrate unique in situ experiments, simulations, and fabricated interfaces that address these fundamental questions in solid-state interfaces of nanoscale composites that will guide solid-state electrochemistry, transport kinetics, and mechanical deformation for nonstoichiometric materials that enable such applications. In particular, we built on COFFEI’s understanding of chemomechanical coupling among defect concentrations, ionic transport, electron transport, and stored elastic energy that is particularly acute in the far-from-equilibrium conditions typical of energy device applications. By tailoring our focus to solid-state interfaces, we addressed these important issues by (a) developing and applying advanced in situ and ex situ characterization tools to characterize model materials and interfaces synthesized with molecular-level control, under both laboratory-controlled and extreme environments representative of energy device operation; and (b) employing computational modeling and simulation frameworks to predict transport mechanisms, reactivity and stability of these model materials and interfaces under significant chemical strains typical of energy device operation. Recent progress provided insights to additional materials systems and electrochemomechanical fatigue and fracture that were not fully envisioned when the program was initiated. Specifically, in the final three years of COFFEI we pursued two integrated thrusts, with complementary focus. Thrust I focused on failure-resistant electrochemomechanical composites, while Thrust II focused on strain-modulated conductivity and reactivity across interfaces. In contrast to our initial COFFEI focus, these thrusts concentrated wholly on solid-state material interfacial interactions and included greater integration of multiscale visualization including in situ electron microscopy of strained structures/interactions and mesoscale simulations. Successful development of functionally superior and long-lived battery and fuel cell systems and stress adaptable oxides requires a deeper, fundamental understanding of the coupling among the historically important subfields of solid-state electrochemistry, transport kinetics, and mechanical deformation for nonstoichiometric metal oxide electrodes. In this program, the understanding and the application of chemomechanical coupling of defect concentrations, ionic transport, electro-catalytic activity and stored elastic energy, particularly acute in the far-from-equilibrium conditions typical of energy device applications, are being refined and implications for device operation clarified, including for miniaturized solid-state batteries and fuel cells.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling Tungsten Boride Neutronics in ORIGEN for Z-Facility

ORIGEN is one of the main transmutation software packages used in nuclear engineering Modeling Tungsten Boride Neutronics in ORIGEN for Z-Facilityproblems. For the case of this study, tungsten borides are studied using a coupled framework between MCNP and the ORIGEN package of scale. The input used four compositions of tungsten boride: WB with natural boron- 10 abundance, WB with 80wt% B-10 per isotope of boron, WB4 with natural boron-10 abundance, and WB4 with 80wt% B-10 per isotope of boron. Isotopic inventories were produced for WB which show the time dependent change up to 2 years after a 6-Month irradiation. This will allow for further studies of the materials to assess things material composition changes, dose contribution, and waste management requirements.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A probabilistic inverse prediction method for predicting plutonium processing conditions

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

Over the past decade, a variety of innovative methodologies have been developed to better characterize the relationships between processing conditions and the physical, morphological, and chemical features of special nuclear material (SNM). Different processing conditions generate SNM products with different features, which are known as “signatures” because they are indicative of the processing conditions used to produce the material. These signatures can potentially allow a forensic analyst to determine which processes were used to produce the SNM and make inferences about where the material originated. This article investigates a statistical technique for relating processing conditions to the morphological features of PuO 2 particles. We develop a Bayesian implementation of seemingly unrelated regression (SUR) to inverse-predict unknown PuO 2 processing conditions from known PuO 2 features. Model results from simulated data demonstrate the usefulness of the technique. Applied to empirical data from a bench-scale experiment specifically designed with inverse prediction in mind, our model successfully predicts nitric acid concentration, while results for Pu concentration and precipitation temperature were equivalent to a simple mean model. Our technique compliments other recent methodologies developed for forensic analysis of nuclear material and can be generalized across the field of chemometrics for application to other materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗