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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Plasma electron acceleration driven by a long-wave-infrared laser

Laser-driven plasma accelerators provide tabletop sources of relativistic electron bunches and femtosecond x-ray pulses, but usually require petawatt-class solid-state-laser pulses of wavelength λ L ~ 1 μm. Longer-λ L lasers can potentially accelerate higher-quality bunches, since they require less power to drive larger wakes in less dense plasma. Here, we report on a self-injecting plasma accelerator driven by a long-wave-infrared laser: a chirped-pulse-amplified CO 2 laser (λ L ≈ 10 μm). Through optical scattering experiments, we observed wakes that 4-ps CO 2 pulses with < 1/2 terawatt (TW) peak power drove in hydrogen plasma of electron density down to 4 × 10 17 cm –3 (1/100 atmospheric density) via a self-modulation (SM) instability. Shorter, more powerful CO 2 pulses drove wakes in plasma down to 3 × 10 16 cm –3 that captured and accelerated plasma electrons to relativistic energy. Collimated quasi-monoenergetic features in the electron output marked the onset of a transition from SM to bubble-regime acceleration, portending future higher-quality accelerators driven by yet shorter, more powerful pulses.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Comparative genomic analysis of thermophilic fungi reveals convergent evolutionary adaptations and gene losses

Thermophily is a trait scattered across the fungal tree of life, with its highest prevalence within three fungal families (Chaetomiaceae, Thermoascaceae, and Trichocomaceae), as well as some members of the phylum Mucoromycota. We examined 37 thermophilic and thermotolerant species and 42 mesophilic species for this study and identified thermophily as the ancestral state of all three prominent families of thermophilic fungi. Thermophilic fungal genomes were found to encode various thermostable enzymes, including carbohydrate-active enzymes such as endoxylanases, which are useful for many industrial applications. At the same time, the overall gene counts, especially in gene families responsible for microbial defense such as secondary metabolism, are reduced in thermophiles compared to mesophiles. We also found a reduction in the core genome size of thermophiles in both the Chaetomiaceae family and the Eurotiomycetes class. The Gene Ontology terms lost in thermophilic fungi include primary metabolism, transporters, UV response, and O-methyltransferases. Comparative genomics analysis also revealed higher GC content in the third base of codons (GC3) and a lower effective number of codons in fungal thermophiles than in both thermotolerant and mesophilic fungi. Furthermore, using the Support Vector Machine classifier, we identified several Pfam domains capable of discriminating between genomes of thermophiles and mesophiles with 94% accuracy. Using AlphaFold2 to predict protein structures of endoxylanases (GH10), we built a similarity network based on the structures. We found that the number of disulfide bonds appears important for protein structure, and the network clusters based on protein structures correlate with the optimal activity temperature. Thus, comparative genomics offers new insights into the biology, adaptation, and evolutionary history of thermophilic fungi while providing a parts list for bioengineering applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Omnigenous umbilic stellarators

To better understand the dependence of the magnetic field structure in the plasma edge on the plasma boundary shape, in the context of X-point and island divertor designs, we define and develop a class of stellarators called umbilic stellarators. These equilibria are characterised by a single continuous high-curvature edge on the plasma boundary that goes around multiple times toroidally before meeting itself. We develop a technique that allows us to simultaneously optimise the plasma boundary along with a curve lying on the boundary on which we impose a high curvature while imposing omnigenity – a property of the magnetic field that ensures trapped particle confinement throughout the plasma volume. We find that umbilic stellarators naturally tend to favour piecewise omnigenity instead of omnigenity with a specific helicity. After generating omnigenous umbilic stellarators, we design coil sets for some of them and explore the fieldline structure in the edge and its sensitivity to small fluctuations in the plasma. Finally, using single-stage optimisation, we simultaneously modify the plasma and coil shape and propose an experiment to modify an existing tokamak to a finite-𝛽 stellarator using this technique and explore a potentially simpler way to convert a limited tokamak into a diverted stellarator.

fusion plasma↗
DOE OSTI2025

Investigating the ultraviolet photodissociation of bromocyclopropane with ultrafast electron diffraction

We have studied the photodissociation of gas-phase bromocyclopropane by 200 nm wavelength ultraviolet radiation using ultrafast electron diffraction. Bromocyclopropane is a prototypical molecule in the study of organobromides, a class of molecules that have a significant impact on atmospheric ozone depletion through their photochemistry. Here, previous studies have revealed two possible reaction pathways for the photodissociation of bromine from bromocyclopropane; either the C–Br bond dissociates, leaving behind a cyclopropyl ring, or there is a concerted opening of the cyclopropyl ring along with the C–Br bond dissociation. In this work, both our experimental and simulation results indicate that the majority of the UV-photoexcited BCP molecules (88% ± 11% in the experiment) follow the first reaction pathway, in which the cyclopropyl ring remains closed after homolytic C–Br bond cleavage. This direct bond dissociation occurs within the experimental time resolution of 270 fs. In order to differentiate between the possible reaction end-products, both of which have diffraction signals dominated by the bromine atom, a new analysis method has been employed, which is more sensitive to the structure of the end-products.

Atmospheric chemistry↗
DOE OSTI2024

Focussing Protons from a Kilojoule Laser for Intense Beam Heating Using Proximal Target Structures

Proton beams driven by chirped pulse amplified lasers have multi-picosecond duration and can isochorically and volumetrically heat material samples, potentially providing an approach for creating samples of warm dense matter with conditions not present on Earth. Envisioned on a larger scale, they could heat fusion fuel to achieve ignition. We have shown in an experiment that a kilojoule-class, multi-picosecond short pulse laser is particularly effective for heating materials. The proton beam can be focussed via target design to achieve exceptionally high flux, important for the applications mentioned. The laser irradiated spherically curved diamond-like-carbon targets with intensity 4×10 18 W/cm 2 , producing proton beams with 3MeV slope temperature. A Cu witness foil was positioned behind the curved target, and the gap between was either empty or spanned with a structure. With a structured target, the total emission of Cu Kα fluorescence was increased 18 fold and the emission profile was consistent with a tightly focussed beam. Transverse proton radiography probed the target with ps order temporal and 10 μm spatial resolution, revealing the fast-acting focussing electric field. Complementary particle-in-cell simulations show how the structures funnel protons to the tight focus. The beam of protons and neutralizing electrons induce the bright Kα emission observed and heat the Cu to 100eV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Testing meson portal dark sector solutions to the MiniBooNE anomaly at the Coherent CAPTAIN Mills experiment

A solution to the MiniBooNE excess invoking rare three-body decays of the charged pions and kaons to new states in the MeV mass scale was recently proposed as a dark-sector explanation. This class of solution illuminates the fact that, while the charged pions were focused in the target-mode run, their decay products were isotropically suppressed in the beam-dump-mode run in which no excess was observed. This suggests a new physics solution correlated to the mesonic sector. We investigate an extended set of phenomenological models that can explain the MiniBooNE excess as a dark sector solution, utilizing long-lived particles that might be produced in the three-body decays of the charged mesons and the two-body anomalous decays of the neutral mesons. Over a broad set of interactions with the long-lived particles, we show that these scenarios can be compatible with constraints from LSND, KARMEN, and MicroBooNE, and evaluate the sensitivity of the ongoing and future data taken by the Coherent CAPTAIN Mills experiment to a potential discovery in this parameter space. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Detonation Soot: A New Class of Ice Nucleating Particle

Abstract Temperatures and pressures from high explosive detonations far exceed atmospheric conditions in typical combustion reactions, and consequently, detonation soot forms with physiochemical properties distinct from soot formed by combustion. In this study, samples of detonation soot from two high explosives, PBX 9502 and Composition B‐3, were analyzed. Ice nucleation experiments on soot collected after controlled detonations were conducted in the laboratory to probe immersion and contact mode freezing. Samples nucleated ice at temperatures warmer than commercially available nanodiamonds, which has a mean nucleation temperature of −20.7°C. Ice nucleation rate coefficients increase rapidly by two to three orders of magnitude below −20°C for every sample. Size‐selected 137 μm diameter particles produced during detonation in an ambient air atmosphere yield bimodal distributions of freezing temperature with primary and secondary nucleation modes centered at −20°C and −13°C, respectively. The presence of a secondary mode allows for enhanced ice nucleation rate coefficients (one to two orders of magnitude greater than samples without a secondary mode) at temperatures outside the influence of the primary mode (>−17°C). Given the observed onset nucleation temperatures of −9.2°C, our results imply that detonation soot of the type studied here would only need to reach an altitude of approximately 4 km to facilitate ice formation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Materials laboratories of the future for alloys, amorphous, and composite materials

In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A New Way to Discover Strong Gravitational Lenses: Pairwise Spectroscopic Search from DESI DR1

We present a new method to search for strong gravitational-lensing systems by pairing spectra that are close together on the sky in a spectroscopic survey. We visually inspect 26,621 spectra in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 that are selected in this way. We further inspect the 11,848 images corresponding to these spectra in the DESI Legacy Imaging Surveys Data Release 10, and obtain 2046 conventional strong gravitational lens candidates, of which 1906 are new. This constitutes the largest sample of lens candidates identified to date in spectroscopic data. Besides the conventional candidates, we identify a new class of systems that we term “dimple lenses.” These systems have a low-mass foreground galaxy as a lens, typically smaller in angular extent and fainter compared with the lensed background source galaxy, producing subtle surface brightness indentations in the latter. We report the discovery of 318 of these “dimple lens” candidates. We suspect that these represent dwarf galaxy lensing. With follow-up observations, they could offer a new avenue to test the cold dark matter model by probing their mass profiles, stellar mass–halo mass relation, and halo mass function for M Halo ≲ 10 13 M ⊙ . Thus, in total, we report 2164 new lens candidates. Our method demonstrates the power of pairwise spectroscopic analysis and provides a pathway complementary to imaging-based and single-spectrum lens searches.

Hsu, Yuan-Ming 許淵明 [National Taiwan University, Ta↗
DOE OSTI2025

Are light curve classification metrics good proxies for SN Ia cosmological constraining power?

Context. When selecting a light curve classifier for use as part of a photometric supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, such as the contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would eliminate the computational expense of a full cosmology forecast in the analysis pipeline design process. Aims. This study tests the assumption that light curve classification metrics are an appropriate proxy for cosmology metrics. Methods. We emulated photometric SN Ia cosmology light curve samples with controlled contamination rates of individual contaminant classes and evaluated each of them under a set of classification metrics. We then derived cosmological parameter constraints from all samples under two common analysis approaches and quantified the impact of contamination by each contaminant class on the resulting cosmological parameter estimates. Results. We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are shown to be insensitive to the latter. Conclusions. Based on these findings, we discourage any exclusive reliance on light curve classification-based metrics for analysis design decisions, which (counterintuitively) include but are not limited to the classifier choice. Instead, we recommend optimising science analysis pipeline design choices using a metric of the information gained about the physical parameters of interest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

RU Net for Automatic Characterization of TRISO Fuel Cross Sections

TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

Convolutional Neural Networks↗
DOE OSTI2026

Validation of SPH code Spheral to model interacting solid bodies in a supersonic flow

Contemporary discussions of planetary defense involve analyzing the risks posed by smaller sized, 20 to 200 m diameter, asteroids which are capable of breaking up in the atmosphere and generating a blast wave. Consequence assessments for this size class of asteroids are performed through fast-running analytic or semi-analytic models which are informed by high-fidelity hydrocode simulations of asteroid entry and breakup. However, insufficient historical data necessitates validating the independent physical processes which dominate airburst events. Here, the Fluid Solid Interface Smoothed Particle Hydrodynamics solver was previously used by Pearl et al. in 2023 to model the Chelyabinsk airburst and is used here to perform a series of validation simulations. The first effort involves modeling a cylinder in a hypersonic flow and comparing the bow shock geometry to that predicted by analytic theory. The second effort involves modeling the separation of two spherical bodies in supersonic flow and validating against experimental footage. Combined, these exercises demonstrate the ability of the code to model the flight-path of interacting solid bodies in a hypersonic flow.

Airburst↗
DOE OSTI2025

Measurement of the inclusive isolated-photon production cross section in pp and Pb–Pb collisions at $\mathbf {\sqrt{\textit{s}_{NN }} = 5.02}$ TeV

The ALICE Collaboration at the CERN LHC has measured the inclusive production cross section of isolated photons at midrapidity as a function of the photon transverse momentum (p$^{γ}_{T}$), in Pb–Pb collisions in different centrality intervals, and in pp collisions, at centre-of-momentum energy per nucleon pair of $\sqrt{s_{NN}}$ = 5.02 TeV. The photon transverse momentum range is between 10–14 and 40–140 GeV/c, depending on the collision system and on the Pb–Pb centrality class. The result extends to lower p$^{γ}_{T}$ than previously published results by the ATLAS and CMS experiments at the same collision energy. The covered pseudorapidity range is |η γ | < 0.67. The isolation selection is based on a charged particle isolation momentum threshold p$^{iso, ch}_{T}$ GeV/c within a cone of radii R = 0.2 and 0.4. The nuclear modification factor is calculated and found to be consistent with unity in all centrality classes, and also consistent with the HG-PYTHIA model, which describes the event selection and geometry biases that affect the centrality determination in peripheral Pb–Pb collisions. The measurement is compared to next-to-leading order perturbative QCD calculations and to the measurements of isolated photons and Z 0 bosons from the CMS experiment, which are all found to be in agreement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Parametric matrix models

We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation.

Computational science↗
DOE OSTI2026

Entropy-mode-driven gas optics

We propose a class of gaseous diffractive optical elements created by imprinting an entropy mode in a gas. Previous approaches to gaseous diffractive optics have relied on the simultaneous excitation of a standing acoustic wave and an entropy mode to produce one-dimensional periodic structures. However, the presence of acoustic oscillations in the gas imposes stringent constraints on some of the operational parameters of these optical elements, such as their lifetime and diffraction angle. Here, in this work, we introduce an approach that eliminates the acoustic mode, relying solely on the entropy mode. This enables control of the lifetime and temporal profile of gaseous optical elements, and also allows the creation of arbitrary structures with greater contrast, including nonperiodic patterns such as chirped gratings or lenses. This approach should allow operation over a wider parameter space, including larger diffraction angles and compatibility with laser pulse durations ranging from femtoseconds to microseconds.

Optics and optical instruments↗
DOE OSTI2025

Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications

We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo package, and (ii) AMReX-Castro, an adaptive mesh astrophysical code. We analyze power, temperature, utilization, and energy traces from double-/single (mixed)-precision benchmarks on NVIDIA’s A100 and H100 and AMD’s MI250X GPUs using queries in NVML and rocm_smi_lib, respectively. We explore application-specific metrics to provide insights on energy vs. performance trade-offs. Our results suggest that mixed-precision energy savings range between 6–25% on QMCPACK and 45% on AMReX-Castro. Also, we found gaps in the AMD tooling used on Frontier GPUs that need to be understood, while query resolutions on NVML have little variability between 1 ms-1 s. Overall, application level knowledge is crucial to define energy-cost/science-benefit opportunities for the codesign of future supercomputer architectures in the post-Moore era.

Godoy, William [ORNL] (ORCID:0000000225905178)↗
DOE OSTI2024

New systems in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) serves as a common library of classes between applications developed for advanced reactor analysis, fusion device engineering, spent fuel cask analysis, geochemistry studies, among other fields. These applications drive the development of the framework to meet their needs. Systems in MOOSE group capabilities that share a common purpose and generally common code. They can be leveraged by all downstream applications, providing extensive code re-use and shared maintenance. They facilitate the discovery by new users of the classes meeting at least partially their needs, and offer the same opportunities for customization as other systems. The addition of a new system to MOOSE opens new ways of solving or discretizing nonlinear problems, of performing distributed postprocessing, and a plethora of other needs. While new systems can be introduced in downstream applications rather than at the framework level, the framework team monitors common needs across the community and often triggers their addition. Documentation, training material, development needs can be centralized, limiting duplicated work across the community. The last three years have seen a large expansion in the capabilities of MOOSE. The supporting role of the framework in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has created numerous feature requests to support neutronics, thermal hydraulics, computational fluid dynamics and thermo-mechanics simulations in the Griffin, SAM, Pronghorn and Bison applications respectively. Similarly, laboratory-directed research and development (LDRD) projects in additive manufacturing, high-Reynolds flow simulations, structure optimization also necessitate an expansion of the framework capabilities. This summary reports on the new systems created in MOOSE, their design, their capabilities and some of the relevant interfaces.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗