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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Evaluation of simulated HPGe detector efficiencies in OpenMC compared to MCNP

In late 2023, the open-source radiation transport code OpenMC introduced a pulse-height tally (PHT) feature, enabling users to track the total energy deposited by individual photons in OpenMC cells. This function represents an important improvement of the OpenMC code because it allows users to simulate the response function of a gamma detector without relying on closed-source alternatives. Despite this, limited work has been published evaluating OpenMC as a radiation transport code for simulating gamma spectroscopy experiments. This study attempts to demonstrate the usefulness of OpenMC in this space by directly comparing its PHT output to MCNP (a trusted industry-standard Monte Carlo code). In the first half of this study, a radiation transport experiment is described in which a detector with a complex internal geometry is exposed to various gamma-emitting isotopes ( 133 Ba, 137 Cs, and 60 Co) over a range of distances. The setup of this experiment was modeled in MCNP with sufficient detail to capture the efficiency characteristics of a high purity germanium (HPGe) detector for the primary gammas of each isotope. After good agreement between the MCNP model and experiment was achieved, an identical model was produced in OpenMC to allow direct comparison between the full energy peak (FEP) values produced in OpenMC and MCNP. The results show strong agreement between OpenMC and MCNP across the full range of tested energies, with each model’s FEP values typically within 2% of each other and most FEP areas within 4% of experimental data. Worse agreement was seen between the Monte Carlo codes and experiment below 300 keV (an expected result). For the 662 keV line of 137 Cs, both codes were found in poor agreement with experiment and each other over the full range of distances tested (possibly indicating an error with the 137 Cs experimental data). Ignoring the anomalous results of the 662 keV line, all other data show good qualitative and quantitative agreement between MCNP and OpenMC. This result demonstrates the accuracy of OpenMC’s PHT feature for spectroscopic applications in which detector efficiency is a primary concern.

07 - ISOTOPES AND RADIATION SOURCES↗
DOE OSTI2026

Physical properties investigation of tetragonal Ba T 2 P 2 (T = Ru, Pd)

The structural, thermal, electrical transport, and magnetic properties of ternary compounds BaT 2 P 2 (T = Ru, Pd) are reported together with electronic structure calculations. Our results show that BaRu 2 P 2 crystallises in the layered tetragonal ThCr 2 Si 2 -type structure (space group I4/mmm) whereas BaPd 2 P 2 adopts the primitive tetragonal CeMg 2 Si 2 -type structure (space group P4/mmm). The combined experimental results and electronic structure calculations suggest metallic and diamagnetic ground states for both materials. Electrical transport measurements further indicate non-Fermi liquid behaviour at low temperatures. Notably, BaRu 2 P 2 exhibits an anomalous feature in the magnetic susceptibility at T ~ 260 K, similar to that observed in BaRu 2 As 2 . A comparison between the bare density of states obtained from electronic structure calculations and the experimentally estimated values suggests the presence of strong correlation effects in the material. Furthermore, the heat capacity of BaPd 2 P 2 , calculated using the full phonon dispersion relation, is in good agreement with the experimental data, particularly at low temperatures, highlighting the significant contribution of optical phonon branches to C P in this temperature range.

flat bands↗
DOE OSTI2024

A novel methodology for gamma-ray spectra dataset procurement over varying standoff distances and source activities

The adoption of machine learning approaches for gamma-ray spectroscopy has received considerable attention in the literature. Many studies have investigated the deployment of various algorithm architectures to a specific task. However, little attention has been afforded to the development of the datasets leveraged to train the models. Such training datasets typically span a set of environmental or detector parameters to encompass a problem space of interest to a user. Variations in these measurement parameters will also induce fluctuations in the detector response, including expected pile-up and ground scatter effects. Fundamental to this work is the understanding that 1) the underlying spectral shape varies as the measurement parameters change and 2) the statistical uncertainties associated with two spectra impact their level of similarity. While previous studies attribute some arbitrary discretization to the measurement parameters for the generation of their synthetic training data, this work introduces a principled methodology for efficient spectral-based discretization of a problem space. A signal-to-noise ratio (SNR) respective spectral comparison measure and a Gaussian Process Regression (GPR) model are used to predict the spectral similarity across a range of measurement parameters. This innovative approach effectively showcased its capability by dividing a problem space, ranging from 5 cm to 100 cm standoff distances and 5 μCi–100 μCi of 137 Cs, into three unique combinations of measurement parameters. The findings from this work will aid in creating more robust datasets, which incorporate many possible measurement scenarios, reduce the number of required experimental test set measurements, and possibly enable experimental training data collection for gamma-ray spectroscopy.

data science↗
DOE OSTI2025

MINE: a new way to design genetics experiments for discovery

Abstract The Maximally Informative Next Experiment or MINE is a new experimental design approach for experiments, such as those in omics, in which the number of effects or parameters p greatly exceeds the number of samples n (p > n). Classical experimental design presumes n > p for inference about parameters and its application to p > n can lead to over-fitting. To overcome p > n, MINE is an ensemble method, which makes predictions about future experiments from an existing ensemble of models consistent with available data in order to select the most informative next experiment. Its advantages are in exploration of the data for new relationships with n < p and being able to integrate smaller and more tractable experiments to replace adaptively one large classic experiment as discoveries are made. Thus, using MINE is model-guided and adaptive over time in a large omics study. Here, MINE is illustrated in two distinct multiyear experiments, one involving genetic networks in Neurospora crassa and a second one involving a genome-wide association study in Sorghum bicolor as a comparison to classic experimental design in an agricultural setting.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Understanding coarsening of a post-corrosion microstructure in a molten salt by combining phase-field modeling and in situ tomography

Alloys corroding in molten salt have been observed to form bicontinuous, nanoporous microstructures via dealloying, which subsequently undergo coarsening due to facile transport in high-temperature conditions. In this work, we describe a methodology to elucidate the underlying transport mechanisms during coarsening of a bicontinuous microstructure via quantitative comparisons between phase-field simulations and four-dimensional in situ experiments, in this case X-ray nanotomography of the coarsening of a dealloyed 80 wt% Ni-20 wt% Cr microwire in molten KCl-MgCl 2 at 800°C. We conduct phase-field simulations initialized from experimental data to model coarsening via three different transport mechanisms: surface diffusion, solid bulk diffusion, and liquid bulk diffusion. These simulations reproduce key features of the experiment, such as the densification of the outer layer of the dealloyed wire and the reduction in radius over time. We quantitatively compare different microstructural characteristics between the simulations and experiment and extract temporal scaling factors that optimally match the time scales of the simulations to that of the experiment. This allows us to evaluate morphological similarity between the simulations and experiment and relate the experimental coarsening kinetics to fundamental material properties. We find that surface diffusion is most likely to be the dominant coarsening mechanism, and its kinetics imply a surface diffusivity of D S = 8.9 x 10 -20 m 3 /s, which is within the range of reported values for Ni-vacuum interfaces at 800°C. However, the difference between the experiment and the surface diffusion simulation increases substantially at late times, suggesting that other mechanisms, such as the dissolution of residual Cr, may be at play.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Exploring the QCD phase diagram through correlations and fluctuations

The exploration of the Quantum Chromodynamics (QCD) phase diagram is a central goal of relativistic heavy-ion collision experiments. This review focuses on the role of fluctuations and correlations as sensitive probes of the phase structure. We discuss theoretical advancements and experimental methodologies employed to map the QCD phase diagram, highlighting constraints derived from both lattice QCD calculations and existing experimental data. Key observables such as cumulants and factorial cumulants of conserved charges (e.g., net-proton, net-charge) are explored as promising signatures of phase transitions and the QCD critical point. We discuss how these quantities are measured experimentally and compared with theoretical predictions, addressing challenges and best practices for meaningful comparisons. Special attention is given to predictions and current experimental results at high baryon density, including recent findings from the STAR collaboration at RHIC. Finally, we identify open issues and future directions for fluctuation and correlation studies at lower collision energies, relevant for future measurements, for example by the CBM experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A review of NEST models for liquid xenon and an exhaustive comparison with other approaches

This paper discusses the microphysical simulation of interactions in liquid xenon, the active detector medium in many leading rare-event searches for new physics, and describes experimental observables useful for understanding detector performance. The scintillation and ionization yield distributions for signal and background are presented using the Noble Element Simulation Technique (NEST), a toolkit based on experimental data and simple empirical formulas, which mimic previous microphysics modeling but are guided by data. The NEST models for light and charge production as a function of the particle type, energy, and electric field are reviewed, along with models for energy resolution and final pulse areas. NEST is compared with other models or sets of models and validated against real data, with several specific examples drawn from XENON, ZEPLIN, LUX, LZ, PandaX, and table-top experiments used for calibrations.

WIMPs↗
DOE OSTI2026

Development of an Amine Oxide Polyzwitterion Brush Martini Model with Polarizable Water and Ions

Abstract In order to accurately simulate the fouling process of proteins onto polyzwitterion brushes, models that accurately capture the hydration properties and chain conformations of such brushes must first be established. We developed a Martini coarse-grained (CG) model for amine oxide polyzwitterion (PNOMA) brushes, a promising class of antifouling materials, in polarizable water and ions by fitting to all-atom bond and angle distributions, monomer hydration free energy, monomer–monomer distance potential of mean force (PMF), and monomer–salt radial distribution functions (RDFs). Martini 2.2P was selected for compatibility with the established polarizable water and ion models. For comparison with PNOMA, we also constructed models for conventional sulfobetaine (PSBMA) and phosphorylcholine (PMPC) polyzwitterions and the polycation PMETAC using established nonbonded bead types from the literature and refitting bond and angle potentials. We simulated each polymer brush chemistry for varying grafting density and chain length, validating brush height scaling relations against experimental data. The CG models captured the relative hydration strengths among different polyzwitterion chemistries, and brush heights extrapolated to higher molecular weights are in agreement with experimental ellipsometry data. We find that chain swelling of the superhydrophilic PNOMA brushes lies between that of the traditional polyzwitterions PSBMA/PMPC and the polycation PMETAC. For PNOMA brushes in NaCl solution, simulated brush height decreases with salt concentration due to the selectively strong interactions between amine oxide and sodium ions.

Walker, Christopher C. [Oak Ridge National Laborat↗
DOE OSTI2026

Biopolymer-Templated Titania Film Formation for Nanostructured Coatings Revealed by Machine Learning-Supported Time-Resolved Analysis

This study presents a machine learning approach to derive the film formation of biopolymer-templated titania nanostructures during spray deposition, in combination with in situ grazing-incidence small-angle X-ray scattering (GISAXS). A neural network trained on synthetic GISAXS data directly predicts domain-size distributions from experimental two-dimensional scattering patterns, capturing the full kinetics of nanostructure evolution with high temporal resolution. The predictions reveal hierarchical size distributions and periodic growth features, consistent with layer-by-layer spray deposition and validated by complementary scanning electron microscopy (SEM) imaging. Quantitative comparison with conventional parametric GISAXS fits shows good qualitative agreement, with systematic differences explained by domain-shape assumptions and resolved by applying a geometric scaling factor. Simulated SEM-like surfaces derived from neural network outputs reproduce the porous, foam-like nanoscale morphology observed experimentally, reinforcing the method’s credibility. This integrated approach enables real-time, nondestructive, statistically averaged monitoring of bulk nanostructure development in functional coatings, offering a scalable methodology to accelerate the characterization and process control of sustainably manufactured nanostructured titania films for energy-related applications such as photocatalysis and photovoltaics.

Heger, JulianEliah↗
DOE OSTI2025

Thermal Hydraulics Validation Activities in the ART-GCR Campaign

The Advanced Reactor Technologies - Gas Cooled Reactor Campaign (ART-GCR) is part of the United States Department of Energy - Office of Nuclear Energy's Advanced Reactor Technologies program aimed at developing a High Temperature Gas-cooled Reactor (HTGR) which will offer enhancements in safety and efficiency. The Design, Methods, and Validation arm of the ART-GCR program provides experience and advanced tools for HTGR design and analysis. The Methods and Validation arm is developing data to validate predictions of decay heat removal through an extensive experimental campaign the Natural Convection Shutdown Heat Removal Test Facility at Argonne National Laboratory. In-core thermal hydraulics validation is being facilitated through the OECD/NEA's Thermal Hydraulic Code Validation Benchmark for High Temperature Gas-Cooled Reactors using HTTF Data. That benchmark contains code-to-code and code-to-data comparisons for 3 sets of HTGR thermal hydraulics phenomena. This ongoing benchmark activity has 16 participants from 9 countries developing leading insights into HTGR thermal hydraulics code validation. The ART-GCR campaign is also working on the development of state-of-the-art MOOSE-based models of the High Temperature Engineering Test Reactor (HTTR). Through the OECD/NEA's HTTR Loss of Forced Cooling (LOFC) program, the ART-GCR campaign is developing a multiphysics transient code validation benchmark based on 3 HTTR experiments that will provide an opportunity for international collaboration on multiphysics code validation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

In-situ strain behavior and BISON simulations of Zircaloy cladding subjected to temperature cycling separate-effects tests in a steam environment

Understanding fuel system performance during anticipated transients without scram (ATWSs) in boiling water reactors (BWRs) is necessary for refining current and future safety limits. High-fidelity material models and simulations are fundamental to rigorous assessment of zirconium-based cladding performance. However, experimental thermomechanical data during simulated ATWSs to validate these modes are limited. To provide relevant in-situ data, Zircaloy-4 cladding was subjected to cyclic heating in a steam environment to simulate an out-of-pile BWR ATWS. Digital image correlation was used to capture the cladding strain behavior in-situ for comparison against simulations using the BISON finite element code. Conventional high-temperature models were compared using multiple schemes to gain a better understanding of the applicability of three BISON models to BWR ATWS: (1) the default combination of creep models in BISON, (2) the high-temperature Erbacher model alone, and (3) the low-temperature Limback-Andersson model alone. The cases run with the Limback-Andersson model alone produced the lowest root mean square error (RMSE). The lowest RMSE for the Limback-Andersson model alone was 0.659%, and the highest RMSE reported was 4.22%. A data gap within the model in the temperature regime of interest was also identified, and to account for this gap, the current model in BISON is linearly interpolated between two separate datasets. In conclusion, this evaluation highlights the need to either develop a new model or to improve the existing model to capture transient creep effects resulting from a cyclic temperature transient.

Anticipated transient without SCRAM↗
DOE OSTI2024

FLUKA simulation validation to measure the beam energy of a cyclotron

FLUKA is a fully integrated particle physics MonteCarlo simulation package which has many applications in high energy experimental physics and engineering. When using a cyclotron for medical radionuclide production, it is important to know that the beam energy is suitable to cause the intended nuclear reaction and minimize unwanted impurities. Here, this work focuses on the comparison of FLUKA as a tool to measure the beam energy of a cyclotron with experimental results using the method of stacked copper foils interspaced by an aluminum degrader. We compared two methods to determine the activity ratio: determining the activity in each foil using the experimentally measured cross-sections from the National Nuclear Data Center (NNDC) cross-section data and calculating the activity in the two Cu foils using the FLUKA program. These results were compared to those reported by Gagnon et al., 2011. The FLUKA results gave energy measurements that were different than the true energy by a percentage in the range of 2.4% to 13%, suggesting that FLUKA is not recommended for pre-calculating the cyclotron beam energy using these low Z materials without experimental confirmation. It also confirms that the experimental cross-section data from the NNDC database is superior for this application giving energy values that ranged from 0.1% to 1.6% from the true energy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

New approaches to Bayesian uncertainty quantification for Nuclear Science (Final Technical Report)

Inverse problems play a central role in experimentation and theory/data comparisons for many areas of modern Nuclear Physics (NP) and High-Energy Physics (HEP). Bayes’s Theorem is a powerful tool for solving Inverse Problems, providing conceptually transparent and unbiased constraints on theoretical parameters and their uncertainties (“Bayesian Inference”) and enabling the quantification of agreement or tension between models and data. However, analyses based on Bayesian Inference are often challenging for NP and HEP applications, either because of the large number of parameters in the problem, the high computational cost, or both. We propose a multi-institutional collaboration to develop and deploy novel Bayesian analysis tools that advance the scientific scope of a broad range of current and future NP experiments. This project brings together NP domain scientists working on several high-profile NP projects for which new, high-performance Bayesian Uncertainty Quantification (“Bayesian UQ”) methods are essential to carry out the science, and data scientists who are developing state-of-the-art methods applicable to these problems. The NP projects in this proposal comprise measurements of the mass and fundamental nature of the neutrino; study of the Quark-Gluon Plasma that filled the early universe; and mapping of natural and anthropogenic radiation environments. While these NP projects have very different scientific goals, with datasets and analysis approaches that differ significantly, they share common requirements for improving computationally intensive Bayesian analyses using advanced Machine Learning algorithms and will benefit strongly from a coherent effort to develop general solutions. This proposal brings together these projects and forefront ML-based data science algorithms to develop such general solutions. The methods developed in this project will also be more widely applicable, thereby advancing science in the larger Nuclear Physics portfolio.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

A lumped particle direct simulation Monte-Carlo method combined with the collisional-radiative model for simulations of non-equilibrium laser-induced plasma plumes

Collisional plasma plumes induced by laser irradiation of material targets exhibit large variations in local density as well as ionization and excitation states, making purely hydrodynamic or kinetic simulations inaccurate or infeasible. To address this challenge and capture non-equilibrium effects in laser-induced plasma plumes at arbitrary degrees of ionization, we develop a hybrid computational approach that combines the kinetic direct simulation Monte Carlo (DSMC) method with a collisional-radiative model (CRM). This ℓDSMC-CRM approach utilizes a lumped particle method to represent minor fractions of excited ions in particle-based simulations and a special coarse-graining technique for atomic spectra and photoionization rates, ensuring numerical convergence at reduced computational cost. The hybrid approach is applied to simulate spatially homogeneous relaxation as well as one- and two-dimensional expansions of plasma plumes induced by irradiation of a copper target by a nanosecond laser pulse in a vacuum or background gas. The comparison with an equilibrium model, where local Saha-Boltzmann equilibrium is enforced, shows that the non-equilibrium effects play a dominant role. The equilibrium model can fail to predict the flow structure and strongly underestimate the degree of absorption of laser radiation by the plume. The ℓDSMC-CRM approach is validated against experimental data demonstrating reasonable agreement with the experimental electron density and temperature, while the equilibrium model is found to dramatically underestimate electron density and temperature. The flexibility of the ℓDSMC-CRM approach allows for its seamless integration into existing DSMC frameworks, making it a valuable tool for high-fidelity plasma modeling in laser-material interactions, laser-based manufacturing, and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Elucidating texture and grain morphology contributions to the micromechanical response of additively manufactured Inconel 625

Microstructural variation of additively manufactured (AM) metal components in comparison to wrought counterparts makes certification for critical applications a challenge. Microscale simulations leveraging modern computational tools may be used to supplement testing of AM microstructures, thus accelerating certification by reducing the number of experiments needed. However, as micromechanical response is closely tied to critical properties like fatigue-life and fracture, utilization of these simulations with macroscale experimental data alone is insufficient. One means to attain microscale experimental data is in situ diffraction data collected from synchrotron X-ray sources. In this work, such data were collected during in situ compression of AM Inconel 625 superalloy. Interpretation of experimental results was assisted by massive (8M element) complementary micromechanical simulations performed on sets of virtual microstructures generated using cellular automata. Together, micromechanical data from diffraction experiments and simulations were used to probe the effects of textured “track” microstructures generated during laser powder bed fusion and directional strength-to-stiffness on micromechanical response. Though fiber-averaged directional strength-to-stiffness ratios were expected to dominate given the high elastic anisotropy of the material, the combination of small variations in texture and specific grain configurations unique to AM microstructures lead to significant variability in micromechanical response after yield. The findings emphasize the importance of high-fidelity microstructural representation that captures key texture components and AM-specific morphology for property prediction of AM metals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Longitudinal spin transfer to Λ hyperons in semi-inclusive deep inelastic scattering with the CLAS12 spectrometer

The polarization of Λ hyperons is preserved in the angular distribution of their decay products. This property allows one to study the spin structure of the Λ. In semi-inclusive deep inelastic scattering where a high energy lepton interacts with a nucleon target and one or more hadrons and the scattered lepton are detected in the final state, the probability for a struck quark to impart the polarization of the lepton to the Λ may be measured. In particular, in electron-proton scattering this quantity may be related to the longitudinal light quark polarization of the Λ. Currently, limited experimental data cannot discriminate between different models of Λ spin structure. This work reports on the measurement of the longitudinal spin transfer 𝐷$^{Λ}_{𝐿⁢𝐿′}$ to the Λ using data taken by the CLAS12 spectrometer at Jefferson Lab with a 10.6 GeV longitudinally polarized electron beam and an unpolarized hydrogen target. This measurement is the most precise to date, and, in comparison with theory predictions, it offers valuable insight into the relative dominance of current and target fragmentation in Λ production.

deep inelastic scattering↗
DOE OSTI2026

Quantifying Microstructure Variability in Laser Powder Bed Fusion 316 L Stainless Steel Microstructures with Spatial Statistics

Here, we have explored data-driven methods for material microstructure quantification that improve sensitivity to microstructural changes compared to traditional approaches. The methods integrate multiple microstructural properties, including grain morphology, crystallographic orientation, and material phase information. The simpler method employs maps of the Euclidean distance transformation metric to evaluate the morphology of grain boundary networks. The more intensive approach employs generalized spherical harmonic mapping for crystallographic orientations, per-pixel phase information, and a variational auto-encoder for dimensionality reduction and results in a multidimensional clustering of by microstructure similarity. Applied to an experimental dataset of additively manufactured steel, both methods detected slight variations in samples produced under nominally identical processing conditions. Both methods were able to distinguish between samples from multiple (nominally identical) builds, while the generalized spherical harmonics-based method could additionally cluster data samples rotated at two orientations on the build plate. The improved sensitivity of the methods, demonstrated through comparison with traditional microstructure characterization techniques, offers advantages for microstructure quantification and comparisons in advanced manufacturing applications.

SS316L↗
DOE OSTI2025

Hybrid chemical characterization of latent images in EUV resist with 12 nm half-pitch features

With the advancement of high numerical aperture extreme ultraviolet (EUV) lithography, the new platform will enable chipmakers to achieve critical dimensions of 8 nm. However, resist materials face significant challenges in delivering increased sensitivity while managing rising stochastic variations. We aim to develop comprehensive techniques to characterize the chemical profile of latent images, stored in EUV resists after exposure and postexposure baking, which is essential for understanding the origin of stochastic effects. Infrared photo-induced force microscopy (IR PiFM) is a bimodal atomic force microscopy technique combined with an infrared light source, allowing for simultaneous sub-5 nm topographic and chemical characterization within a localized environment. Critical-dimension resonant soft X-ray scatterometry (CD-RSoXS) provides statistical data that reveal structural and chemical information for comparative analysis. For the first time, IR PiFM has been used to chemically map the latent images of EUV resists (after exposure and postexposure baking) at a record high resolution of 12 nm half-pitch, enabling nondestructive analysis of patterns prior to development. Furthermore, CD-RSoXS offers direct experimental observation and comparison of exposed, postexposure baked, and developed patterns, which align with the IR PiFM results. We demonstrate that the IR PiFM technique offers valuable insights into both high spatial resolution and local chemical information simultaneously. In addition, CD-RSoXS provides statistical results that support our main findings. This hybrid metrology approach leverages a multifaceted dataset by combining the most reliable information from each source, which is essential for a comprehensive understanding of the stochastic effects in EUV lithography processes.

O’Reilly, Padraic↗