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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Time managed virtualization for simulating systems of systems

Software is an integral part of almost every non-trivial system. Yet simulation technologies to understand how software affects system performance lag behind what is available to understand physical behavior. This is not surprising. An abstract model of software tells us what it is supposed to do, not what it actually does. In this respect software is very different from a physical system. Kirchhoff’s laws predict the behavior of an electrical circuit with great precision. The mathematics of fluids do an adequate job of anticipating how a wing will perform in flight. Only the software itself, observed in operation, can tell us how it behaves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI1995

Nuclear Safety [Vol. 36, No. 2, July-December 1995]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. THE CHORNOBYL ACCIDENT: 195 The Chornobyl Accident Revisited, Part III: Chernobyl Source Term Release Dynamics and Reconstruction of Events During the Active Phase, A. R. Sich; GENERAL SAFETY CONSIDERATIONS: 218 Second ANS Workshop on the Safety of Soviet-Designed Nuclear Power Plants, R. A. Bari; 234 Elements of a Nuclear Criticality Safety Program, C. M. Hopper; 243 Rickover, Excellence, and Criticality Safety Programs, R. E. Wilson; ACCIDENT ANALYSIS: 249 Transient Analysis of the PIUS Advanced Reactor Design with the TRAC-PF1/MOD2 Code, B. E. Boyack, J. L Steiner, S. C. Harmony, H. J. Stumpf, and J. F. Lime; 278 The Hierarchy-By-Interval Approach to Identifying Important Models that Need Improvement in Severe-Accident Simulation Codes, T. J. Heames, M. Khatib-Rahbar, J. E. Kelly, R. P. Jenks-Johnson, and Y.-S. Chen; 290 RELAP5/MOD3 Code Coupling Model, R. P. Martin; 299 Missiles Caused by Severe Pressurized-Water Reactor Accidents, R. Krieg; DESIGN FEATURES: 310 Validation of COMMIX with Westinghouse AP-600 PCCS Test Data, J. G. Sun, T. H. Chien, J. Ding, and W T. Sha; ENVIRONMENTAL EFFECTS: 321 Spent Nuclear Fuel Characterization for a Bounding Reference Assembly for the Receiving Basin for Off-Site Fuel, S. D. Kahook, R. L. Garrett, L. R. Canas, and M J. Beckum; OPERATING EXPERIENCES: 332 Reactor Shutdown Experience, Compiled by J. W. Cletcher; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 335 Reactor Coolant System Blowdown at Wolf Creek on September 17, 1994, J. V. Kauffman and S. L. Israel; RECENT DEVELOPMENTS: 344 Reports, Standards, and Safety Guides, D. S. Oueener; 349 Proposed Rule Changes as of June 30, 1995; ANNOUNCEMENTS: 320 Symposium on Acceptability of Risk From Radiation—Application to Manned Space Flight; 320 24th DOE/NRC Nuclear Air Cleaning and Treatment Conference; 361 Radiation Biology and Radiation Protection— Modern Developments and Tendencies in Radiation Biology; 362 1997 IEEE Sixth Conference on Human Factors and Power Plants; 354 The Authors; 360 Reviewers of Nuclear Safety, Vol. 36.

05 NUCLEAR FUELS↗
DOE OSTI2026

Enhancing the mass resolving power of FRIB’s proposed high-voltage MR-ToF mass separator and spectrometer: Addressing non-ideal conditions

Multi-reflection time-of-flight mass separators and spectrometers (MR-ToF MSs) are indispensable tools at radioactive ion beam (RIB) facilities. These electrostatic ion beam traps act as highly selective mass separators and high-precision mass spectrometers for rare and exotic nuclei. When well-tuned and designed to minimize higher-order flight-time aberrations, state-of-the-art MR-ToF MSs approach, and slightly exceed, mass resolving powers of m/Δm = 10 6 . Achieving m/Δm ≥ 3 ⋅ 10 6 would provide the ability to resolve > 90 % of all known isomeric states with half-lives above 10 ms. However, the ability to mass separate in all practical setups is limited by non-ideal conditions which place such resolving powers out of reach. Here, to this end, we present a simulated analysis of these conditions in the newly proposed high-voltage MR-ToF MS for the Facility for Rare Isotope Beams (FRIB). It is expected to store ions at 30 keV beam energy and increase ion throughput by two orders of magnitude compared to current devices. Existing efforts to mitigate the effects of non-ideal conditions employed for current MR-ToF devices storing ions at < 3 keV beam energy will already enable mass resolving powers approaching 10 6 for FRIB’s high-voltage MR-ToF device. Simulations of newly proposed mitigation strategies show that even mass resolving powers approaching 10 7 might become feasible.

Electrostatic ion beam traps↗
DOE OSTI2023

Observation of Slow Eigen-Zundel Interconversion in H + (H 2 O) 6 Clusters upon Isomer-Selective Vibrational Excitation and Buffer Gas Cooling in a Cryogenic Ion Trap

The formation of isomers when trapping floppy cluster ions in a temperature-controlled ion trap is a generally observed phenomenon. This involves collisional quenching of the ions initially formed at high temperature by buffer gas cooling until their internal energies fall below the barriers in the potential energy surface that separate them. Here we explore the kinetics at play in the case of the two isomers adopted by the H + (H 2 O) 6 cluster ion that differ in the proton accommodation motif. One of these is most like the Eigen cation with a tricoordinated hydronium motif (denoted E), and the other is most like the Zundel ion with the proton equally shared between two water molecules (denoted Z). After initial cooling to about 20 K in the radiofrequency (Paul) trap, the relative populations of these two spectroscopically distinct isomers are abruptly changed through isomer-selective photoexcitation of bands in the OH stretching region with a pulsed (~6 ns) infrared laser while the ions are in the trap. We then monitor the relaxation of the vibrationally excited clusters and reformation of the two cold isomers by recording infrared photodissociation spectra with a second IR laser as a function of delay time from the initial excitation. The latter spectra are obtained after ejecting the trapped ions into a time-of-flight photofragmentation mass spectrometer, thus enabling long (~0.1 s) delay times. Excitation of the Z isomer is observed to display long-lived vibrationally excited states that are collisionally cooled on a ms time scale, some of which quench into the E isomer. These excited E species then display spontaneous interconversion to the Z form on a ~10 ms time scale. Furthermore, these qualitative observations set the stage for a series of experimental measurements that can provide quantitative benchmarks for theoretical simulations of cluster dynamics and the potential energy surfaces that underlie them.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Absolute efficiency response of the γ-ray spectrometer GRETINA for high-energy γ rays up to 6 MeV

GRETINA (Gamma-Ray Energy Tracking In-beam Nuclear Array), a state-of-the-art γ-ray tracking spectrometer, has been instrumental in advancing nuclear structure studies, particularly in experiments using fast beams of rare isotopes where the γ rays emitted in flight are subject to large Doppler shifts. This work presents an experimental determination of the γ-ray detection efficiency of GRETINA at energies up to 6 MeV. The high-energy γ rays originated from states that were populated in nucleon-removal reactions from a projectile beam at an intermediate beam energy of about 85 MeV/nucleon. Utilizing γ-γ coincidences, efficiency values were extracted in-beam and translated into a source efficiency at rest employing GEANT4 Monte Carlo simulations. A systematic discrepancy in the efficiency of the forward-positioned detectors was identified which is attributed to specific dead-time effects caused by high-energy light-particle events prevalent at forward angles, generating signals in the Ge crystals that saturate the preamplifier’s first stage. Furthermore, this study provides refined efficiency benchmarks for in-beam experiments, improving the accuracy of quantitative γ-ray spectroscopy analyses with GRETINA and GRETA (Gamma Ray Energy Tracking Array) in the future.

GRETINA↗
DOE OSTI2021

Trap-Assisted Dopant Compensation Prevents Shunting in Poly-Si Passivating Interdigitated Back Contact Silicon Solar Cells

Using a trap-assisted compensation model, we explain why polycrystalline Si (poly-Si) passivating contacts are able to achieve low leakage current between the doped fingers of interdigitated back contact (IBC) monocrystalline Si solar cells despite mixing of boron and phosphorus dopants in the isolation region. The fill factor of IBC solar cells is strongly affected by the electrical isolation region between n- and p-type fingers, as this region is critical in minimizing shunting losses. During fabrication of monocrystalline Si solar cells with poly-Si passivating contacts, the intrinsic poly-Si isolation region inevitably gets contaminated with both p- and n-type dopants. Using dopant profiles measured with time-of-flight secondary ion mass spectrometry and scanning probe measurements of the isolation region, we demonstrate that despite the dopant spreading during cell processing, a well-compensated region between the doped fingers exists that prevents shunting. The trap-assisted dopant compensation mechanism significantly widens the compensated region to tens of microns, where the residual dopant densities are below the trap density. This enables a high-resistivity region, resulting in low shunt current. Using one-dimensional (1-D) finite element diode simulations, we identify the design parameters and experimental conditions under which a sufficiently resistive region can form. Furthermore, our measurements of 2-D local resistivity and work function maps across the isolation region using scanning spreading resistance microscopy and Kelvin probe force microscopy demonstrate the existence of a highly resistive, wide compensated region and confirm our proposed compensation mechanism. For our structures, this region is ~25 µm in width within a ~150 µm wide finger isolation region with nearly 3 orders of magnitude higher resistivity than the regions dominated by a single type of dopant.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine learning based rate optimization under geologic uncertainty

We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. The training data for the model is generated by running the forward simulator with randomly assigned well rates using multiple geologic realizations. A reduced order representation of the permeability heterogeneity in each of the realizations is derived using a grid connectivity transformation (GCT). This step involves finding basis functions corresponding to the different modal frequencies of the grid connectivity represented by the grid Laplacian. The projection of the heterogeneous property field along these basis functions gives the basis coefficients that form the reduced order representation. Subsequently, for each training datapoint, streamlines are traced and the minimum time of flight (TOF) representing the tracer breakthrough time at each producer is recorded. The basis coefficients and well rates are fed to a machine learning model as input and the minimum TOF at the producers forms the output of the model. This trained model can then be used along with an optimizer for computing the optimal injection rates to maximize the injection sweep efficiency. This corresponds to minimizing the variance in the minimum TOF within each well group. Different architectures of neural network are tested using 5-fold cross validation to decide the best ML model to compute the streamline time of flight. The trained model is used to perform well rate optimization over multiple realizations of geology by using a risk tolerance penalty. The optimal well rates thus obtained are compared with two cases: a) equal well rates assigned to all injectors and producers and b) well rates obtained by optimizing over a single realization without considering the uncertainty in geology. The optimal well rates are seen to offer better oil recovery and sweep efficiency than both cases.

02 PETROLEUM↗
DOE OSTI2020

Coupling of Laminar-Turbulent Transition with RANS Computational Fluid Dynamics

This project combines several new concepts to create a boundary layer transition prediction capability that is suitable for analyzing modern hypersonic flight vehicles. The first new concept is the use of ''optimization'' methods to detect the hydrodynamic instabilities that cause boundary layer transition; the use of this method removes the need for many limiting assumptions of other methods and enables quantification of the interactions between boundary layer instabilities and the flow field imperfections that generate them. The second new concept is the execution of transition analysis within a conventional hypersonics CFD code, using the same mesh and numerical schemes for the transition analysis and the laminar flow simulation. This feature enables rapid execution of transition analysis with less user oversight required and no interpolation steps needed.

42 ENGINEERING↗
DOE OSTI2026

An Open Source Perovskite Solar Cell Curve Tracer

When developing systems for perovskite solar cells (PSCs), it is important to have a testing platform that closely resembles your deployed systems. While developing a 1 U CubeSat, the Big Red Sat-1 at the University of Nebraska-Lincoln, we found a lack of passive curve tracing instrumentation, which we needed for our mission. To that end, we developed a benchtop passive curve tracer that leverages the Ossila Solar Simulator as an AM1.5 light source. This device utilizes a ladder of switched resistors and a precision voltage and current measurement to achieve the curve tracing. It also integrates a common-anode multiplexer to switch through multipixel PSCs. Finally, the solar cell holder is separate from the main measurement system, so various substrates sizes and pixel counts can be supported. This system is tested using various PSCs. It has been open sourced to enable reuse in future systems that may benefit from the lower power consumption and flight heritage.

14 SOLAR ENERGY↗
DOE OSTI2022

Evaluating Image Classification Deep Convolutional Neural Network Architectures for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition-based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available, and physical modeling is often not feasible. Additionally, using data-based models, which make decisions based on raw sensor data, allow features to be learned instead of manually determined. In this work, deep convolutional neural network (CNN) architectures are investigated for their ability to estimate the RUL of turbofan engines. To improve the accuracy of the models, CNN architectures, which have proven successful in image classification, are implemented and tested. Specifically, the blocks used in the Visual Geometry Group (VGG) architecture, inception modules used in the GoogLeNet architecture, and residual blocks used in the ResNet architecture are incorporated. To account for varying flight lengths, the input to the models is a window of time series data collected from the engine under test. Window locations at the climb, cruise, and descent stages are considered. To further improve the RUL estimations, multiple overlapping windows at each location are used. This increases the amount of training data available and is found to increase the accuracy of the resulting RUL estimations by averaging the estimates from all overlapping segments. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set, and high prognosis accuracy was achieved. Furthermore, this work expands on the model developed and used in the 2021 PHM Society Data Challenge, which received second place.

convolutional neural networks↗
DOE OSTI2021

Reconstruction of β-Delayed Neutron Energy Spectra from Recoil-Ion Spectroscopy of Trapped Ions

Here Beta-delayed one-neutron (βn) emission has been investigated by confining radioactive ions in an ion trap and detecting the β particles and recoiling nuclei that emerge following decay. In this approach, the βn energy spectrum and branching ratio can be deduced without needing to detect the neutrons, as the neutron emission is inferred from the observed time of flight of the recoiling ions. This paper details the dominant effects that influence the extraction of the neutron energy from the time-of-flight measurement and explores the impact they have on the energy calibration and resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Comparative RNA-Seq transcriptome analyses reveal dynamic time-dependent effects of 56 Fe, 16 O, and 28 Si irradiation on the induction of murine hepatocellular carcinoma

One of the health risks posed to astronauts during deep space flights is exposure to high charge, high-energy (HZE) ions (Z > 13), which can lead to the induction of hepatocellular carcinoma (HCC). However, little is known on the molecular mechanisms of HZE irradiation-induced HCC. We performed comparative RNA-Seq transcriptomic analyses to assess the carcinogenic effects of 600 MeV/n 56 Fe (0.2 Gy), 1 GeV/n 16 O (0.2 Gy), and 350 MeV/n 28 Si (0.2 Gy) ions in a mouse model for irradiation-induced HCC. C3H/HeNCrl mice were subjected to total body irradiation to simulate space environment HZE-irradiation, and liver tissues were extracted at five different time points post-irradiation to investigate the time-dependent carcinogenic response at the transcriptomic level. Our data demonstrated a clear difference in the biological effects of these HZE ions, particularly immunological, such as Acute Phase Response Signaling, B Cell Receptor Signaling, IL-8 Signaling, and ROS Production in Macrophages. Also seen in this study were novel unannotated transcripts that were significantly affected by HZE. To investigate the biological functions of these novel transcripts, we used a machine learning technique known as self-organizing maps (SOMs) to characterize the transcriptome expression profiles of 60 samples (45 HZE-irradiated, 15 non-irradiated control) from liver tissues. A handful of localized modules in the maps emerged as groups of co-regulated and co-expressed transcripts. The functional context of these modules was discovered using overrepresentation analysis. We found that these spots typically contained enriched populations of transcripts related to specific immunological molecular processes (e.g., Acute Phase Response Signaling, B Cell Receptor Signaling, IL-3 Signaling), and RNA Transcription/Expression. A large number of transcripts were found differentially expressed post-HZE irradiation. These results provide valuable information for uncovering the differences in molecular mechanisms underlying HZE specific induced HCC carcinogenesis. Additionally, a handful of novel differentially expressed unannotated transcripts were discovered for each HZE ion. Taken together, these findings may provide a better understanding of biological mechanisms underlying risks for HCC after HZE irradiation and may also have important implications for the discovery of potential countermeasures against and identification of biomarkers for HZE-induced HCC.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Observationally constrained analysis on the distribution of fine- and coarse-mode nitrate in global models

Nitrate plays an important role in the Earth system and air quality. A key challenge in simulating the life cycle of nitrate aerosol in global models is to accurately represent mass size distribution of nitrate aerosol. In this study, we evaluate the performance of the Energy Exascale Earth System Model version 2 (E3SMv2) and the Community Earth System Model version 2 (CESM2), along with Aerosol Comparisons between Observations and Models (AeroCom) phase III models, in simulating spatial distribution of fine-mode nitrate, the mass size distribution of fine- and coarse-mode nitrate, and the gas–aerosol partitioning between nitric acid gas and nitrate, using long-term ground-based observations and measurements from multiple aircraft campaigns. We find that most models underestimate the annual mean PM 2.5 (particulate matter with diameter less than 2.5 µm) nitrate surface concentration averaged over all sites. The observed nitrate PM 2.5 / PM 10 and PM 1 / PM 4 ratios are influenced by the relative contribution of fine sulfate or organic particles and coarse dust or sea salt particles. Overall, the ground-based observations give an annual mean surface nitrate PM 2.5 / PM 10 ratio of 0.7. Most models underestimate the annual mean PM 2.5 / PM 10 ratio in all regions. There are large spreads in the modeled nitrate PM 1 / PM 4 ratios, which span the full range from 0 to 1. Most models underestimate the surface molar ratio of nitrate to total inorganic nitrate averaged across all sites. Our study indicates the importance of gas–aerosol partition parameterization and the simulation of dust and sea salt in correctly simulating the mass size distribution of nitrate.

Nitrate↗
DOE OSTI2025

FORECASTOR – II. Simulating galaxy surveys with the Cosmological Advanced Survey Telescope for Optical and UV Research

The Cosmological Advanced Survey Telescope for Optical and UV Research (CASTOR) is a planned flagship space telescope, covering the blue-optical and UV part of the spectrum. Here, we introduce the CASTOR image simulator, a python GalSim package-based script capable of generating mock CASTOR images from an input catalogue. We generate example images from the CASTOR Wide, Deep, and Ultra-Deep surveys using simulated lightcones from the Santa Cruz semi-analytic model. We make predictions for the performance of these surveys by comparing galaxies that are extracted from each image using Source Extractor to the input catalogue. We find that the Wide, Deep, and Ultra-Deep surveys will be 75 per cent complete for point sources down to $\sim 27$, 29, and 30 mag, respectively, in the UV, u, and g filters, with the UV-split and u-split filters reaching a shallower depth. With a large area of $\sim 2200$ deg$^2$, the Wide survey will detect hundreds of millions of galaxies out to $z\sim 4$, mostly with $M_\ast \gtrsim 10^{9}\,{\rm M}_{\odot }$. The Ultra-Deep survey will probe to $z\sim 5$, detecting galaxies with $M_\ast \gtrsim 10^{7}{\rm M}_{\odot }$. These galaxy samples will enable precision measurements of the distribution of star formation in the cosmic web, connecting the growth of stellar mass to the assembly of dark matter haloes over two thirds of the history of the Universe, and other core goals of CASTOR’s legacy surveys. These image simulations and the tools developed to generate them will be a vital planning tool to estimate CASTOR’s performance and iterate the telescope and survey designs prior to launch.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

New particle pusher with hadronic interactions for modeling multimessenger emission from compact objects

We propose novel numerical schemes based on the Boris method in curved spacetime, incorporating both hadronic and radiative interactions for the first time. Once the proton has lost significant energy due to radiative and hadronic losses, and its gyroradius has decreased below typical scales on which the electromagnetic field varies, we apply a guiding center approximation (GCA). We fundamentally simulate collision processes either with a Monte-Carlo method or, where applicable, as a continuous energy loss, contingent on the local optical depth. To test our algorithm for the first time combining the effects of electromagnetic, gravitational, and radiation fields including hadronic interactions, we simulate highly relativistic protons traveling through various electromagnetic fields and proton backgrounds. We provide unit tests in various spatially dependent electromagnetic and gravitational fields and background photon and proton distributions, comparing the trajectory against analytic results. We propose that our method can be used to analyze hadronic interactions in black hole accretion disks, jets, and coronae to study the neutrino abundance from active galactic nuclei.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Aerosol responses to precipitation along North American air trajectories arriving at Bermuda

North American pollution outflow is ubiquitous over the western North Atlantic Ocean, especially in winter, making this location a suitable natural laboratory for investigating the impact of precipitation on aerosol particles along air mass trajectories. We take advantage of observational data collected at Bermuda to seasonally assess the sensitivity of aerosol mass concentrations and volume size distributions to accumulated precipitation along trajectories (APT). The mass concentration of particulate matter with aerodynamic diameter less than 2.5 µm normalized by the enhancement of carbon monoxide above background (PM 2.5 /ΔCO) at Bermuda was used to estimate the degree of aerosol loss during transport to Bermuda. Results for December–February (DJF) show that most trajectories come from North America and have the highest APTs, resulting in a significant reduction (by 53 %) in PM 2.5 /ΔCO under high-APT conditions (> 13.5 mm) relative to low-APT conditions (< 0.9 mm). Moreover, PM 2.5 /ΔCO was most sensitive to increases in APT up to 5 mm (–0.044 µg m –3 ppbv –1 mm –1 ) and less sensitive to increases in APT over 5 mm. While anthropogenic PM 2.5 constituents (e.g., black carbon, sulfate, organic carbon) decrease with high APT, sea salt, in contrast, was comparable between high- and low-APT conditions owing to enhanced local wind and sea salt emissions in high-APT conditions. The greater sensitivity of the fine-mode volume concentrations (versus coarse mode) to wet scavenging is evident from AErosol RObotic NETwork (AERONET) volume size distribution data. A combination of GEOS-Chem model simulations of the 210 Pb submicron aerosol tracer and its gaseous precursor 222 Rn reveals that (i) surface aerosol particles at Bermuda are most impacted by wet scavenging in winter and spring (due to large-scale precipitation) with a maximum in March, whereas convective scavenging plays a substantial role in summer; and (ii) North American 222 Rn tracer emissions contribute most to surface 210 Pb concentrations at Bermuda in winter (~75 %–80 %), indicating that air masses arriving at Bermuda experience large-scale precipitation scavenging while traveling from North America. A case study flight from the ACTIVATE field campaign on 22 February 2020 reveals a significant reduction in aerosol number and volume concentrations during air mass transport off the US East Coast associated with increased cloud fraction and precipitation. These results highlight the sensitivity of remote marine boundary layer aerosol characteristics to precipitation along trajectories, especially when the air mass source is continental outflow from polluted regions like the US East Coast.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering

Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals, especially when leveraging modern time-of-flight (ToF) spectrometers with large detectors. However, efficiently and robustly extracting interatomic force constants (FCs) parameterizing phonon dynamics from experimental spectra remains a bottleneck due to the complexity and high dimensionality of ToF INS datasets. Here, we present a machine learning approach for the direct inversion of FCs from single-crystal INS measurements. The framework leverages synthetic training data generated using universal machine-learned force fields and an efficient physics-based forward model. We benchmark two neural architectures–one emphasizing structured latent representation learning and the other direct, supervised spectral regression–across simulated datasets for two materials under idealized and noisy conditions. The latent-representation model is subsequently applied to experimental single-crystal INS data on germanium. The model is shown to reproduce FCs derived from both first-principles simulations and from iterative optimization, and furthermore achieves reliable inference even from sparse, single-orientation measurements representing short data acquisitions. Analysis of the learned latent space reveals semantically continuous and physically interpretable encodings that support strong cross-domain generalization. By bridging theoretical and experimental domains, we establish a path toward rapid inversion of experimental spectra and data-driven interpretation of temperature-dependent lattice dynamics.

42 ENGINEERING↗
DOE OSTI2025

Characterization of a mock up nuclear waste package using energy resolved MeV neutron analysis

Reliable radiographic methods for characterizing nuclear waste packages non-destructively (without the need to open containers) have the potential to significantly contribute to safe handling and future disposal options, particularly for legacy waste of unknown content. Due to required shielding of waste containers and the need to characterize materials consisting of light elements, X-ray methods are not suitable. Here, energy-resolved MeV neutron radiography is demonstrated as a first-of-its-kind application for non-destructive and remote examination of mock up nuclear waste packages from a safe position using time-of-flight techniques enabled by a novel event-mode imaging detector system. Energy-resolved neutron transmission spectra were measured spatially, permitting the detection of analogue materials to actual nuclear waste such as water, melamine, and ion exchange resin within a 2.54 cm wall thickness steel pipe. The results demonstrate the capability to locate the materials through this wall thickness by radiography and tomographic reconstruction, revealing detailed 3D distributions and structural anomalies. The method effectively detects residual water in ion exchange resin, highlighting its sensitivity to moisture content, a crucial parameter for nuclear waste characterization. Monte Carlo simulations are in agreement with the experimental findings, providing a pathway to simulate waste forms more difficult to tackle experimentally. This work paves the way to apply sub-nanosecond intense MeV neutron sources, such as laser-driven neutron sources under development, to nuclear waste characterization.

36 MATERIALS SCIENCE↗