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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wave-function-based emulation for nucleon-nucleon scattering in momentum space

Emulators for low-energy nuclear physics can provide fast and accurate predictions of bound-state and scattering observables for applications that require repeated calculations with different parameters, such as Bayesian uncertainty quantification. In this paper, we extend a scattering emulator based on the Kohn variational principle (KVP) to momentum space (including coupled channels) with arbitrary boundary conditions, which enable the mitigation of spurious singularities known as Kohn anomalies. We test it on a modern chiral nucleon-nucleon (N N) interaction, including emulation of the coupled channels. We provide comparisons between a Lippmann-Schwinger equation emulator and our KVP momentum-space emulator for a representative set of neutron-proton (n p) scattering observables, and also introduce a quasi-spline-based approach for the KVP-based emulator. Furthermore, our findings show that while there are some trade-offs between accuracy and speed, all three emulators perform well. Self-contained Jupyter notebooks that generate the results and figures in this paper are publicly available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Development of ML FPGA Filter for Particle Identification and Tracking in Real Time

Real-time data processing is a frontier field in experimental particle physics. Machine Learning methods are widely used and have proven to be very powerful in particle physics. The growing computational power of modern FPGA boards allows us to add more sophisticated algorithms for real time data processing. Many tasks could be solved using modern Machine Learning (ML) algorithms which are naturally suited for FPGA architectures. The FPGA-based machine learning algorithm provides an extremely low, sub-microsecond, latency decision and makes information-rich data sets for event selection. We report work has started to evaluate an FPGA based Machine Learning (ML) algorithm for a real-time particle identification and tracking with Transition Radiation Detector (TRD) and e/m calorimeter. The first target is the GlueX experiment, with a plan to build a TRD based on GEM technology. GlueX trigger latency is 3.3 μs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Nuclear excitation functions for medical isotope production: Targeted radionuclide therapy via nat IR$(d, x)$ 193m Pt

193m Pt is an Auger emitting radionuclide which may have therapeutic potential, particularly when labeled to the chemotherapeutic drug cisplatin. One challenge to broader explorations of its clinical potential is the need for production routes with high specific activity. As part of a larger campaign to address gaps in reaction data for emerging medical radionuclides, this work seeks to characterize the nat Ir(d,x) reactions as a potential production pathway for 193m Pt. A stacked target irradiation, consisting of natural iridium, iron, nickel, and copper foils, was performed using a 33 MeV deuteron beam at the Lawrence Berkeley National Laboratory 88-Inch Cyclotron. This measurement, along with previous experimental data, suggests an energy window between 11 to 18 MeV to maximize the production and radiopurity of 193m Pt. This experiment has yielded cross sections for 43 channels of deuteron-induced reactions from threshold to 30 MeV, including the first experimental results of nat Ir(d,x) 188m1+g,190m1+g Ir (cumulative), nat Ni(d,x) 56,57,58 m,58g Co (independent), nat Cu(d,x) 61 Co (cumulative) and nat Fe(d,x) 53 Fe, 48 V (cumulative). The results were compared with literature data, the TENDL-2023 database, and default theoretical calculations from the TALYS-2.04, CoH-3.6.0, EMPIRE 3.2.3, and ALICE-2020 reaction modeling codes. Here, this work presents another example of the lack of predictive capabilities for this set of modern nuclear-reaction modeling codes, and highlights the unsatisfactory modeling of experimental cross sections. Experimental data are important to improve the codes in general, and new experimental results can be used to improve the models. Finally, this measurement has revealed the need for an updated evaluation of the nat Cu(d,x) 63 Zn deuteron monitor reaction.

193mPt↗
DOE OSTI2020

Lithium in T Coronae Borealis

T Coronae Borealis is a recurrent, symbiotic nova system currently in quiescence between its periodic ≈80 yr cycle of eruptions. Observations during inter-outburst epochs provide an opportunity to study properties of the accretion disk and the M red giant. Here we present new irradiated (blackbody veiling) models, incorporating modern molecular opacities and line lists, of spectra derived from high-resolution (22,000 ≲ R ≲ 120,000) optical echelle observations obtained at two epochs, one prior to and one post the 2015 rebrightening event at similar spectroscopic system phase. We find a lithium abundance in the secondary at both epochs to be comparable. The non-irradiated (classical) model atmospheres yield a lithium abundance, A(Li) = 1.3 ± 0.1. The irradiated model (veiled) atmospheres, which are likely a better representation of the system in which the white dwarf and accretion disk illuminate the red giant, give A(Li) = 2.4 ± 0.1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Photoenzymatic Csp 3 –Csp 3 bond formation via enzyme-templated radical–radical coupling

Cross-couplings are essential reactions in modern chemical synthesis, enabling the rapid construction of complex molecules from simple precursors. Transition metal catalysts are prized for these transformations because their reactivity and selectivity can be tuned via judicious selection of the metal and ligand. Although enzymes offer analogous opportunities for tuning via protein engineering, their application to cross-coupling remains limited, as nature relies on alternative paradigms for building molecular complexity. Here, we report the cross-coupling of alkyl halides and benzylic carboxylic acids using an engineered flavin-dependent lactate monooxygenase—a photoenzyme. The enzyme achieves this feat by exploiting the redox versatility of the flavin cofactor. Stoichiometric experiments, ultrafast spectroscopy, and computational studies support a mechanism in which photoexcited flavin quinone initiates the reaction via oxidative decarboxylation to generate a benzylic radical. The resulting flavin semiquinone can reduce the alkyl halide to form a second organic radical within the protein active site, which rapidly engages in C(sp 3 )–C(sp 3 ) bond formation. A variant was engineered to control the stereochemical outcome of this radical–radical coupling event, highlighting the ability of the protein to alter the energetic barrier for a mechanistic step that is traditionally understood to be near barrierless. This work demonstrates that the scope for nonnative reaction mechanisms in biocatalysis far exceeds previously established bounds and has potential to solve a variety of reactivity challenges in cross-coupling chemistry.

biocatalysis↗
DOE OSTI2022

Functional Data Analysis for Extracting the Intrinsic Dimensionality of Spectra: Application to Chemical Homogeneity in the Open Cluster M67

High-resolution spectroscopic surveys of the Milky Way have entered the Big Data regime and have opened avenues for solving outstanding questions in Galactic archeology. However, exploiting their full potential is limited by complex systematics, whose characterization has not received much attention in modern spectroscopic analyses. In this work, we present a novel method to disentangle the component of spectral data space intrinsic to the stars from that due to systematics. Using functional principal component analysis on a sample of 18,933 giant spectra from APOGEE, we find that the intrinsic structure above the level of observational uncertainties requires ≈10 functional principal components (FPCs). Our FPCs can reduce the dimensionality of spectra, remove systematics, and impute masked wavelengths, thereby enabling accurate studies of stellar populations. To demonstrate the applicability of our FPCs, we use them to infer stellar parameters and abundances of 28 giants in the open cluster M67. We employ Sequential Neural Likelihood, a simulation-based Bayesian inference method that learns likelihood functions using neural density estimators, to incorporate non-Gaussian effects in spectral likelihoods. By hierarchically combining the inferred abundances, we limit the spread of the following elements in M67: Fe ≲ 0.02 dex; C ≲ 0.03 dex; O, Mg, Si, Ni ≲ 0.04 dex; Ca ≲ 0.05 dex; N, Al ≲ 0.07 dex (at 68% confidence). Our constraints suggest a lack of self-pollution by core-collapse supernovae in M67, which has promising implications for the future of chemical tagging to understand the star formation history and dynamical evolution of the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Plasma treating water for nitrate based nitrogen fertilizer - A review of recent device designs

Atmospheric pressure air plasma treatment of water is a promising alternative to the industrially intensive Haber-Bosch process for the production of nitrogen fertilizer. Nitrogen fertilizer is vital for plant life, and the environmental impacts of modern fertilizer application cannot be neglected. In order to maintain or increase food production in the future, more sustainable alternatives must be investigated. Plasma can be easily operated at smaller scales, onsite, utilizing green energy sources, cutting down transportation costs and impacts. This article focuses on nitrogen fixation by plasma treating water for use as nitrogen fertilizer. Here, we highlight recent advances (within the last two years) regarding water treating plasma devices, the challenges still facing the field, and recommend more thorough collaborative investigations be carried out in order to focus on the design and optimization of these devices for the future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Using Information Automation and Human Technology Integration to Implement Integrated Operations for Nuclear

The purpose of the research effort described in this report is to develop and demonstrate an approach to the design and implementation of advanced, automated systems intended to increase operational efficiencies at nuclear power plants. In particular, we describe methods for considering human-technology integration (HTI) issues throughout the various phases of system design, test, and implementation and how these considerations help promote effective design. This research project will develop planning tools and comprehensive guidance on how HTI principles and methods, in combination with information automation technologies, can enable effective data integration and coordination for full nuclear plant modernization. Specifically, this research project will develop an approach to automate the mapping of data from plant systems and processes to application needs, thereby significantly reducing the amount of human workload currently required for the execution of these tasks. In addition to developing an automated solution as a replacement for these tasks, this research will also analyze digitalization’s effectiveness in reducing operational costs of compliance related activities. Compliance activities are estimated to account for as much as 50% of operations and maintenance (i.e., non-fuel and non-capital) costs of plant operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Cryo-quenched Fe–Ni–Cr alloy decorative steel single crystals II: Alloy phases, structure, hardness, tensile, tribological, magnetic and electronic properties

We have previously reported (Boatner et al., J. Alloys Compd. 691 (2017) 666–671) on the discovery, formation, processing, and application of a new decorative ternary steel alloy and on its use in a wide range of practical applications including: custom and commercial knives, art objects, jewelry, electronics, and furniture. This decorative property results when polished single crystals of the 70 wt%Fe-15 wt%Ni-15 wt% Cr austenitic alloy are cryo-quenched into the martensitic phase resulting in the formation of a three-dimensional raised pattern of mixed austenitic/martensitic laths. These laths propagate across large dimensions of the material due to the absence of grain boundaries in the single crystal. The macroscopic optically reflective 3-D decorative pattern reproduces the structural symmetry inherent in the single-crystal orientation. This pattern results solely from the metallurgical phase properties of the ternary alloy and is completely distinct from the properties of Damascus steels or more modern so-called pattern-welded steels. Here we use X-ray diffraction, nanoindentation, hardness measurements/scratch testing, tribological measurements, and alloy resistivity and magnetization methods to obtain a more fundamental and comprehensive study of the effects of the austenitic/martensitic phase transition on the structural and physical properties of the 70 wt%Fe-15 wt%Ni-15 wt% Cr alloy. These results reveal new insight into irreversible phenomena that are associated with the inhibited phase transition on heating of the two-phase, mixed austenitic/martensitic structure that is formed subsequent to cryo-quenching the alloy crystal to 77 K.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Factors associated with emerging multimodal transportation behavior in the San Francisco Bay Area

Abstract This paper identifies the influence of demographic, local transportation environment, and individual preferences for transportation attributes on multimodal transportation behavior in an urban environment with emergent transportation mode availability. Multimodality is the use of more than one mode of transportation during a given timeframe. Multimodality has been considered a key component of sustainable and efficient transportation systems, as this travel behavior can represent a shift away from personal vehicle use to more sustainable transportation modes, especially in urban environments with diverse transportation systems and emergent shared transportation alternatives (e.g., carsharing, ridehailing, bike sharing). However, it is unclear what factors contribute towards people being more likely to exhibit multimodal transportation behavior in modern urban environments. We assessed commuting behavior based on a survey administered in the San Francisco Bay Area according to whether residents commuted (i) exclusively by vehicle, (ii) by a mix of vehicle and non-vehicle modes, or (iii) exclusively by non-vehicle modes. A classification tree approach identified correlations between commuting classes and demographic variables, preferences for transportation attributes, and location-based information. The characterization of commuting styles could inform regional transportation policy and design that aims to reduce vehicle use by identifying the demographic, preference, and location-based considerations correlated with each commuting style.

McAuliffe Wells, Emily↗
DOE OSTI2023

Data Archive and Portal (DAP) Platform for Solid Phase Processing Technologies

The scale and speed of data generated by modern scientific experiments have constantly challenged the research community to store, curate, manage and optimally use it to drive scientific discoveries. In this work, we have developed a data archive and portal (DAP) platform including analytics capabilities to collect, curate, and manage data and metadata stream for solid phase processing (SPP) techniques. We successfully hosted around ~347K files of data related to processing parameters, microscopic images, and spectroscopic data related to solid phase processing. The DAP platform for SPP will establish an enduring capability to support machine learning and grow collaboration at the intersection of materials science and data science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

State Energy Offices’ Engagement in Electric Distribution Planning to Meet State Policy Goals

NASEO and Berkeley Lab released a new publication on State Energy Offices’ Engagement in Electric Distribution Planning to Meet State Policy Goals. State and Territory Energy Offices develop plans, programs, policies, and projects that have a substantial impact on electric distribution systems. They also can participate in distribution system planning (DSP) processes to help ensure that utilities – consumer- and investor-owned – meet the state’s future energy needs. This paper recognizes the wide spectrum of roles that State Energy Offices can play in DSP processes, including planning for distributed energy resources and grid modernization. It highlights various examples of non-regulatory activities by State Energy Offices including planning, conducting studies, convening stakeholder processes, and implementing programs that inform and contribute to distribution system planning. It also provides examples of State Energy Offices’ engagement in proceedings before their respective public utility commissions. As State Energy Offices face myriad challenges associated with meeting state policy goals, preparing for anticipated rates of distributed energy resource deployment, addressing concerns regarding grid reliability and resilience, and recommending and making long-term investment decisions, the examples provided through this guide can serve as a resource in navigating those challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Locating Undocumented Wells Using Historical Oil and Gas Exploration Maps: A Case Study in Osage County, Oklahoma

Undocumented oil and gas wells lack reliable information about their locations and characteristics, making them difficult to identify. These wells can result in unanticipated delays and costs in the development of nearby surface and subsurface resources, and, if improperly plugged, can cause contamination. This study leverages historical petroleum exploration maps to locate such wells, focusing on Osage County, Oklahoma. Two sets of early 20th century oil and gas exploration maps by the United States Geological Survey were georeferenced and analyzed using a computer vision model to detect well symbols. The locations of detected wells were compared to the location of known wells in the database from the Bureau of Indian Affairs Osage Agency to identify potential undocumented wells. The analysis yielded over 500 potential undocumented wells, with dry holes constituting the largest fraction. Field verification confirmed the presence of some undocumented wells. Comparison with prior work revealed limited overlap, underscoring the complementary value of historical oil and gas maps for locating undocumented wells. This approach demonstrates the utility of integrating historical cartographic resources with modern geospatial and machine learning techniques to improve the identification and management of undocumented wells.

Energy - Petroleum↗
DOE OSTI2024

Radiative corrections: from medium to high energy experiments

Abstract Radiative corrections are crucial for modern high-precision physics experiments, and are an area of active research in the experimental and theoretical community. Here we provide an overview of the state of the field of radiative corrections with a focus on several topics: lepton–proton scattering, QED corrections in deep-inelastic scattering, and in radiative light-hadron decays. Particular emphasis is placed on the two-photon exchange, believed to be responsible for the proton form-factor discrepancy, and associated Monte-Carlo codes. We encourage the community to continue developing theoretical techniques to treat radiative corrections, and perform experimental tests of these corrections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Relative Impact of Sea Ice and Temperature Changes on Arctic Marine Production

We use a modern Earth system model to approximate the relative importance of ice versus temperature on Arctic marine ecosystem dynamics. We show that while the model adequately simulates ice volume, water temperature, air-sea CO 2 flux, and annual primary production in the Arctic, itunderestimates upper water column nitrate across the region. This nitrate bias is likely responsible for the apparent underestimation of ice algae production. Despite this shortcoming, the model appears to be a useful tool for exploring the impacts of environmental change on phytoplankton production and carbon dynamics over the Arctic Ocean. Our experiments indicate that under a warmer climate scenario, the percentage of ocean warming that could be apportioned to a reduction in ice area ranged from 11% to 100%, while decreasing ice area could account for 22–100% of the increase in annual ocean primary production. The change to CO2 air-sea flux in response to ice and temperature changes averaged an Arctic-wide 5.5 Tg C yr –1 (3.5%) increase, into the ocean. This increased carbon sink may be short-lived, as ice cover continues to decrease and the ocean warms. The change in carbon fixation from phytoplankton in response to increased temperatures and reduced ice was generally more than a magnitude larger than the changes to CO 2 flux, highlighting the importance of fully considering changes to the marine ecosystem when assessing Arctic carbon cycle dynamics. Our work demonstrates the importance of ice dynamics in controlling ocean warming and production and thus the need for well-behaved ice and BGC models within Earth system models if we hope to accurately predict Arctic changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition

Background Fire is a foundational ecological process that shapes ecosystem structure, diversity, and resilience. Quantifying paleofire regime attributes such as frequency, severity, and intensity is essential for understanding the historical range of variability in fire behavior and its ecological effects. While frequency and severity are often reconstructed in paleofire studies, quantitative reconstructions of fire intensity remain limited. Recent work has shown that maximum pyrolysis temperature—a proxy for fire intensity—and plant species type can be inferred from charcoal using transmission Fourier-transform infrared (FTIR) spectroscopy. However, the sample preparation for transmission FTIR is destructive and time-consuming, limiting application and reuse of materials for other analyses. We evaluated reflectance FTIR spectroscopy as a non-destructive alternative for reconstructing combustion temperature and plant species from laboratory-generated charcoal. We also examined the influence of contrasting airflow environments (ambient air versus nitrogen-rich) on pyrolysis temperature and plant species reconstruction prediction accuracies and compared predictive performance between a novel, neural network–based deep learning model with the traditional modern analogue technique (MAT) using k-nearest neighbor functions. As proof of concept, we apply our enhanced methodology to ancient charcoal to demonstrate applicability at improving long-term fire regime reconstructions and the ability to link paleofire records with contemporary fire ecology. Results Our analysis shows that transmission and reflectance FTIR spectra yield comparable spectral profiles. However, sample preparation for reflectance FTIR is minimal and non-destructive, unlike transmission FTIR which is destructive. We demonstrate that oxygen environments improved reconstruction accuracy relative to nitrogen-rich conditions. Finally, our deep learning neural network (DL) achieved testing accuracies of 98.7% for temperature and 96.2% for species identification, outperforming MAT’s k-NN approach (89.8% and 65.9%, respectively). A Shapley importance analysis identified 5 key spectral regions that greatly influenced the model’s temperature or species categorization. When applied to ancient charcoal, our results show historic fires from the most recent past primarily burned at low intensities (400–500 °C), reflective of natural fire regimes in ponderosa pine forests. Our results corroborate charcoal morphology data that suggests all ancient charcoal originated from burned woody plant types. Conclusions By combining reflectance FTIR spectroscopy with a deep learning approach, we provide the first accuracies high enough to confidently identify both species and temperature from laboratory-produced charcoal, improving quantitative reconstructions of fire intensity and fuel composition from paleofire records. This opens a wide range of research into the link between fire and larger drivers (i.e., climate or human) and greater ecological understanding of fire regimes beyond that of burn scars or recent observations. These methodological improvements have direct relevance for fire management by improving interpretation of historical fire behavior, informing fuel–fire relationships, and providing a scalable analytical framework applicable to both long-term ecological studies and contemporary fire science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Valuing the Future Electric Grid: A Bid-Based Approach

Energy storage resources (ESRs) and other zero marginal cost (ZMC) resources have unique characteristics that are not fully captured in today’s electricity planning and operations modeling tools. Because the modeling assumptions used in these tools are simplified approximations of how operations and investment decisions occur in the real-world, accurately representing cost and operational characteristics are key for determining how these resources impact price formation. Questions such as—Where should we build new transmission? Will a small modular reactor earn enough revenue to participate in the future electric grid? Is retrofitting a coal plant with carbon capture technology economically feasible?—all require accurate electricity prices, which aren’t available from today’s electricity planning and operations modeling tools. As an example, production cost models (PCMs) are heavily utilized tools that determine the cost and reliability of the electric system. However, as PCMs were developed to help thermal generators manage their fuel inventories, production cost modeling is largely based on fuel prices. Because ESRs do not incur fuel costs, they are often modeled as ZMC resources. In reality, ESRs incur opportunity costs as well as technology-specific (degradation) costs that are non-trivial to calculate but are important for price formation. In this research, we identify options to incorporate more realistic opportunity and degradation costs in ESR bidding algorithms. Expanding available bidding assumptions allows energy system modelers to develop more accurate economic valuations for ESRs, leading to more accurate price formation from leading energy system modeling tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗