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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Cosmic dipoles from large-scale structure surveys

Large-scale structure surveys can be used to measure the dipole in the cosmic microwave background (CMB), in the luminosity distances inferred from type-Ia supernova observations, and in the spatial distribution of galaxies and quasars. The measurements of these cosmic dipoles appear to be mutually inconsistent, even though they are expected to indicate the common observer velocity. This observational tension may represent a significant challenge to the standard model of cosmology. Here, in this work, we study in detail what contributes to the cosmic dipoles from CMB, supernova, and galaxy survey in the standard Λ cold dark matter (Λ⁢ CDM) model, though our theoretical model can be applied beyond the standard model. While measurements of the cosmic dipoles yield the relative velocities between the source samples and the observer velocity, the motion of the observer is the dominant contribution in the conformal Newtonian gauge, and the intrinsic velocities of the samples fall steeply with increasing redshift of the sources. Hence the cosmic dipoles of CMB, type-Ia supernovae, and galaxies should be aligned but can have different amplitudes. We also clarify several misconceptions that are commonly found in the literature.

cosmic microwave background↗
DOE OSTI2025

Strategies for simulating the time evolution of Hamiltonian lattice field theories

Simulating the time evolution of quantum field theories given some Hamiltonian H requires developing algorithms for implementing the unitary operator e -iHt . A variety of techniques exist that accomplish this task, with the most common technique used so far being Trotterization, which is a special case of the application of a product formula. However, other techniques exist that promise better asymptotic scaling in certain parameters of the theory being simulated, the most efficient of which are based on the concept of block encoding. In this work we study the performance of such algorithms in simulating lattice field theories. We derive and compare the asymptotic gate complexities of several commonly used simulation techniques in application to Hamiltonian lattice field theories. Using the scalar $\hat{φ}$ 4 theory as a test, we also perform numerical studies and compare the gate costs required by product formulas and signal-processing-based techniques to simulate time evolution. For the latter, we use the linear combination of unitaries (LCU) construction augmented with the quantum Fourier transform circuit to switch between the field and momentum eigenbases, which leads to immediate order-of-magnitude improvement in the cost of preparing the block encoding. Further, this paper also includes a pedagogical review of the techniques used, in particular product formulas, LCU, qubitization, quantum signal processing, as well as the technique for simulating geometrically-local Hamiltonians developed by Haah, Hastings, Kothari, and Low.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Detection of uranium-photofission neutrons with a 4 He scintillation detector

The use of photon active interrogation to detect special nuclear material has held significant theoretical promise, as the interrogating source particles, photons, are fundamentally different from one of the main signatures of special nuclear material: neutrons produced in nuclear fission. However, neutrons produced by photonuclear reactions in the accelerator target, collimator, and environment can obscure the fission neutron signal. These (γ, n) neutrons could be discriminated from fission neutrons by their energy spectrum, but common detectors sensitive to the neutron spectrum, like organic scintillators, are typically hampered by the intense photon background characteristic of photon-based active interrogation. In contrast, high-pressure 4 ⁢He-based scintillation detectors are well-suited to photon active interrogation, as they are similarly sensitive to fast neutrons and can measure their spectrum, but show little response to gamma rays. Here, in this work, a photon active interrogation system utilizing a 4 ⁢He scintillation detector and a 9 MeV linac-bremsstrahlung x-ray source was experimentally evaluated. The detector was shown to be capable of operating in intense gamma-ray environments and detecting photofission neutrons from 238 ⁢U when interrogated by this x-ray source. The photofission neutrons show clear spectral separation from (γ, n) neutrons produced in lead, a common shielding material.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Averages of 𝑏-hadron, 𝑐-hadron, and 𝜏-lepton properties as of 2023

This paper reports world averages of measurements of 𝑏-hadron, 𝑐-hadron, and 𝜏-lepton properties obtained by the Heavy Flavor Averaging Group using results available before October 2023. In rare cases, significant results obtained several months later are also used. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters, 𝐶⁢𝑃 violation parameters, parameters of semileptonic decays, and Cabibbo-Kobayashi-Maskawa matrix elements.

Bottom quark production↗
DOE OSTI2025

Enhancing the Survivability of Power Systems With Grid-Edge DERs Against DoS Attacks

Power system survivability, defined as the ability of a system to maintain steady-state functionality under varying operational conditions, reflects its resilience against disturbances. While existing research primarily focuses on physical-layer disturbances, the increasing prevalence of grid-edge DERs, which are primarily used for integrating renewable energy, has significantly expanded the cyber attack surface. As a result, operational disruptions caused by cyber threats are posing significant challenges to system survivability and cannot be overlooked. To fill this gap, we redefine system survivability to incorporate the cyber layer’s status and propose a Distributionally Robust Optimization (DRO) approach to enhance power system survivability against potential cyber-physical threats. In this paper, we first analyze the operational guidelines of systems with a high penetration of DERs under various cyber network conditions and redefine survivability in this context. Next, we focus on the most common cyber threat, Denial-of-Service (DoS) attacks, and develop a corresponding attack model. This model allows for the creation of a kernel-based ambiguity set that captures attack uncertainties using historical data. Finally, we transform the proposed DRO model as a tractable optimization problem, with its solution providing an optimal cyber redundancy plan to enhance system survivability in DoS attack scenarios. Simulation results on the IEEE 13-node and 123-node test feeders demonstrate the effectiveness of our proposed model in improving system survivability. This model can also be expanded to include other types of common attacks and serve as a comprehensive planning tool to improve overall cyber physical survival of the system.

cybersecurity↗
DOE OSTI2025

A Novel Protection Scheme for Unbalanced Faults in Inverter Dominated Networks: A Computationally Efficient Algorithm for Entry-Level Relays

Microgrids are now a common practice in distribution systems to increase resilience and reliability. However, microgrid protection remains a critical challenge, considering its requirement to operate in both grid connected and islanded, and the variability in fault characteristics under each mode of operation. This paper presents unbalanced power (S unb ) based fault detection algorithm, which considers local voltage and current unbalances to determine faults in the system. S unb is a computationally efficient fault detection algorithm that is suitable for implementation in the programmable logic of entry level protective relays. In addition, the difference in current and voltage unbalance (D n ) is used to determine the fault type. The proposed method demonstrates high sensitivity and selectivity for line-to-ground (LG), line-to-line (LL), and double line-to-ground (LLG) faults, representing the most common faults in distribution systems. It also allows relay coordination with upstream and downstream protection devices in both island and grid connected operation, while preserving grading margins. The same pickup and time multiplier settings of a particular relay for both modes of operation eliminates the need for adaptive settings, which rely on communication networks. Validation was performed with a hardware-in-the-loop (HIL) setup using Typhoon HIL real time simulator interfaced with three entry-level, SEL 751 relays. Results confirmed the algorithm’s ability to discriminate fault conditions, and determine the fault type under both operating modes, maintain fast detection times, and ensure proper protection coordination.

fault classification↗
DOE OSTI2024

Converged Computing: A Best of Both Worlds of High-Performance Computing and Cloud

Collaboration between Cloud and High Performance Computing (HPC) communities has accelerated in the last half decade. A common goal to run batch workloads combined with a desire for reproducibility, automation, and optimization has led to successful projects that range from container technologies to workload management and security. This span of current and future work defines a novel “Converged Computing” paradigm that aims to combine the best of both worlds, both from a technological and cultural standpoint. Furthermore, in this Special Issue, we review common themes in the space, showcasing current work and encouraging a continued effort toward innovative ideas that will enable the next generations of scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗
DOE OSTI2024

Cloud-based Testbed for Adaptive Under-Frequency Load Shedding with High DER Penetration

Increasing penetration of distributed energy resources and behind-the-meter renewables may soon disrupt the efficacy of critical protection schemes, such as under-frequency load shedding (UFLS). Improved data exchange and coordination across the transmission-distribution boundary will be required to maintain reliability of bulk electric system. Standards-based data integration platforms using agreed-upon semantic vocabularies, such as the Common Information Model, will be key to enabling adaptive protection schemes requiring synthesized data from both the bulk power system and behind-the-meter resources. This paper introduces a cloud-based open-source data integration environment and UFLS clustering algorithm being developed to enable adaptive relay coordination between transmission and distribution utilities in the state of Vermont.

Anderson, Alexander A.↗
DOE OSTI2024

JACC: Leveraging HPC Meta-Programming and Performance Portability with the Just-in-Time and LLVM-based Julia Language

We present JACC (Julia for Accelerators), the first high-level, and performance-portable model for the just-in-time and LLVM-based Julia language. JACC provides a unified and lightweight front end across different back ends available in Julia, enabling the same Julia code to run efficiently on many HPC CPU and GPU targets. We evaluated the performance of JACC for common HPC kernels as well as for the most computationally demanding kernels used in applications, HPCCG, a supercomputing benchmark test for sparse domains, and HARVEY, a blood flow simulator to assist in the diagnosis and treatment of patients suffering from vascular diseases. We carried out the performance analysis on the most advanced US DOE supercomputers: Aurora, Frontier, and Perlmutter. Overall, we show that JACC has a negligible overhead versus vendor-specific solutions, reporting GPU speedups with no extra cost to programmability.

Valero-Lara, Pedro↗
DOE OSTI2025

Challenges and Gaps in the Development of Pulsed Power for Fusion Applications: A Preroadmapping Perspective From Industry, Academia, and National Laboratory Experts

Fusion energy meets the twenty-first century World Grand Challenge of sustainable, ubiquitous, and safer energy sources. However, harnessing the promise of fusion energy has proven elusive. The competing approaches to fusion power plant design include inertial confinement fusion, National Ignition Facility (ICF-NIF, Z machine, etc.,) magnetic confinement fusion (MCF-Tokamak, stellarators, etc.), and other approaches that show promise in small- (flow stabilized Z pinches) or large-scale applications. These approaches are being accelerated with private and public funding and seek to demonstrate the feasibility of different approaches to fusion-based power plants. Yet, how can the necessary pulsed power technologies for these disruptive technology bases be accelerated with no clear “Dominant Design?” Roadmapping holds the promise to identify and develop common critical pulsed power components for laboratory, prototype, and commercial fusion, and can accelerate the commercialization of fusion reactor designs. A preroadmapping Workshop on Pulsed Power for Fusion was held at the IEEE International Pulsed Power Conference in San Antonio, TX, USA, in June 2023. The workshop had 177 attendees. Here, the common elements for many of the ICF technologies vying for dominant design were identified. The advancement of these technologies through roadmapping will enhance commercial expectations that require their rapid and innovative development in the next five years, as well as the next five to ten years. The key technologies identified that underpin and limit the advancement of fusion power include pulsed power technologies such as energy storage, high-voltage switching, additive manufacturing, and modular pulsed power circuit topologies. In conclusion, they are the focus of our effort in the following roadmap scenario, which will delineate potential paths to technology development.

Curry, Randy D. [I-Pulse Group, Albuquerque, NM (U↗
DOE OSTI2025

Which Plant Traits Increase Soil Carbon Sequestration? Empirical Evidence From a Long‐Term Poplar Genetic Diversity Trial

Plants play a key role in mediating soil response to global change, and breeding or engineering crops to increase soil organic carbon (SOC) storage is a potential route to land-based carbon dioxide removal in agricultural systems. However, due to limited observational datasets plus shifting paradigms of SOC stabilization, it is unclear which plant traits are most important for enhancing different types of soil organic matter. Existing long-term common gardens of genetically diverse plant populations may provide an opportunity to evaluate biological controls on SOC, separate from environmental or management variability. Here we report on soil and root chemical data collected for 24 genotypes within a 13-year-old common garden in northwestern Oregon planted with a large natural variant population of Populus trichocarpa. Fractionating surface soil (0–15 cm) revealed substantial variation in stocks of mineral-associated organic matter (MAOM; 18–67 t C/ha) and particulate organic matter (POM; 2–22 t C/ha). Tree genotype explained 24% and 26% of the MAOM and POM stock variability, respectively, after controlling for background variability. We found minimal association between SOC concentration and either aboveground tree productivity or root biomass recalcitrance (C/N ratios and lignin content). In contrast, root elemental content appeared influential for MAOM-C concentration, which showed a strong positive association with root aluminum (Al) and a strong negative association with root boron (B) and magnesium (Mg). Furthermore, root concentrations of these elements were highly heritable (57%–78%) and not simply a reflection of background variation in soil elemental concentrations. We estimate that surface SOC stocks under these 24 genotypes have diverged at rates of up to 1.2–4.3 t C/ha/year. These results suggest that long-term genetic diversity trials have value for elucidating biological controls on soil organic matter dynamics, and that traits associated with root elemental content may be a useful target for enhancing biosequestration.

biomass recalcitrance↗
DOE OSTI2025

The joys and jitters of high‐temperature calorimetry

Abstract High‐temperature calorimetry (HTC) originated in the 20th century as a niche method to enable measurements not easily accomplished with acid solution calorimetry, combustion calorimetry, vapor pressure, or EMF methods. Over time, HTC has evolved into a versatile approach to accurately quantify formation, phase transition, surface and interfacial enthalpies of a wide range of materials including minerals and refractory inorganic compounds. This evolution has been the result of numerous adjustments to experimental setups and procedures, followed by rigorous testing. The commercial availability and the scientific success of this technique have led to an increase in the number of laboratories applying HTC. However, the knowledge acquired by researchers over the past 70 years is scattered throughout the literature or only available as laboratory internal documentation and personal experience. This publication is a collaborative effort among several leading HTC laboratories to summarize and unify current state‐of‐the‐art HTC techniques and procedures. The text starts by summarizing various HT techniques that are commonly used for readers with an interest in HTC in general. It is then directed toward HTC users and includes a brief section on data evaluation procedures as well as a comprehensive compilation of reference data utilizing molten sodium molybdate and lead borate solvents. Finally, for experienced HTC users, an in‐depth discussion of some common difficulties and a discussion of uncertainties are presented.

Scharrer, Manuel [Navrotsky Eyring Center for Mate↗
DOE OSTI2024

On the compatibility of established methods with emerging artificial intelligence and machine learning methods for disaster risk analysis

Abstract There is growing interest in leveraging advanced analytics, including artificial intelligence (AI) and machine learning (ML), for disaster risk analysis (RA) applications. These emerging methods offer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on “learning” through datasets. There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. These existing methods are generally accepted by the risk community and are grounded in use across various risk application areas. The next frontier in RA with emerging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consideration of usefulness, trust, explainability, and other factors. This article leverages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI‐based method for disaster RA applications. This article utilizes empirical evidence from expert perspectives to support key insights on those methods and the compatibility of those methods. This article will be of interest to researchers and practitioners in risk‐analytics disciplines who leverage AI/ML methods.

Mathematical Methods In Social Sciences↗
DOE OSTI2025

Tribology and Tribocorrosion of Case-Hardened Steels: A Review

This report reviews the tribological and tribocorrosion performance of steels that are case-hardened via boriding, chromizing, carburizing, nitriding, nitrocarburizing, and carbonitriding. Case-hardening is commonly used to improve the hardness, impact durability, wear resistance, and corrosion resistance of steel alloys and has been successfully applied in various industries, providing a cost-effective, high-throughput solution for applications involving contact and sliding interfaces in complex service environments. This article summarizes the literature results of the wear and friction behavior of common case-hardening methods for steel alloys under various conditions, including corrosive environments. Special attention is given to the influences of case-hardening process parameters and alloy composition on tribological performance. Furthermore, by discussing key findings from the literature, this review provides insights into optimizing case-hardening processes for improving the tribological and tribocorrosion performance of steel alloys.

Alloys↗
DOE OSTI2026

Contrasting effects of glutamate and branched-chain amino acid metabolism on acid tolerance in a Castellaniella isolate from acidic groundwater

Groundwater acidification co-occurring with nitrate pollution is a common, global environmental health hazard. Denitrifying bacteria have been leveraged for the in situ removal of nitrate in groundwater. However, co-existing stressors—such as low pH—reduce the efficacy of biological removal processes. Castellaniella sp. str. MT123 is a complete denitrifier that was isolated from acidic, nitrate-contaminated groundwater. The strain grows robustly by nitrate respiration at pH < 6.0, completely reducing nitrate to dinitrogen gas. Genomic analyses of MT123 revealed few previously characterized acid tolerance genes. Thus, we utilized a combination of proteomics, metabolomics, and competitive mutant fitness to characterize the genetic mechanisms of MT123 acclimation to growth under mildly acidic conditions. We found that glutamate accumulation is critical in the acid acclimation of MT123, possibly through consumption of intracellular protons via glutamate decarboxylation to GABA. This is despite the fact that MT123 lacks the canonical glutamate decarboxylase-glutamate/GABA antiporter system implicated in acid tolerance in other bacteria. In contrast, branched-chain amino acid (BCAA) accumulation was detrimental to cell growth at lower pHs, possibly through indirect mechanisms impacting the cellular glutamate pool. Genetic analysis previously linked MT123 to a population of Castellaniella that bloomed—concurrent to nitrate removal—during a biostimulation effort to reduce groundwater nitrate concentrations at MT123’s location of origin. Thus, our analyses provide novel insight into mechanisms of acclimation to acidic conditions in a strain with significant potential for nitrate bioremediation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Many paths, similar destinations: viruses and bacterial microcompartments form polyhedra inside cells

A large number of biological entities assemble into icosahedral structures, and these are ubiquitous throughout nature. Examples include eukaryotic and prokaryotic viral capsids and more recently discovered bacterial microcompartments. Viral capsids and bacterial microcompartments are both composed of pentameric and hexameric subunits; however, they differ in the type of cargo they encapsulate: nucleic acid or protein. Also, both depart from strict icosahedral symmetry: while this is less common in viruses, among bacterial microcompartments, diverse and heterogeneous polyhedra are common. We review shared principles and key distinctions between the self-directed assembly of various icosahedral architectures and their polyhedral variants in nature and explore the concept that there are multiple paths, influenced by their cargo, to arriving at similar protein cage morphologies.

bacterial microcompartments↗
DOE OSTI2025

Hybridization capture sequencing for Vibrio spp. and associated virulence factors

ABSTRACT Proliferation ofVibriospp. in aquatic ecosystems is associated with climate change and, concomitantly, increased incidence of vibriosis. They are autochthonous to aquatic environments globally, but traditional metagenomic methods for detecting and typing pathogenicVibriospp. are challenged by their presence in relatively low abundance and ability to persist in a viable but nonculturable state. In the study reported here, hybridization capture sequencing (HCS) was employed to profile low-abundanceVibriospp. in environmental samples. The HCS panel targeted a family of molecular chaperones (CPN60) specific to 69Vibriospp. and 162Vibrio-specific virulence factors. This approach was evaluated in parallel with traditional whole-community shotgun sequencing in a metagenomic analysis of water and oyster samples collected from the Chesapeake Bay. In addition,Vibrio parahaemolyticusandVibrio vulnificusstrains isolated from the samples were subjected to whole-genome sequencing to determine the genetic characteristics of pathogenicVibriospp. circulating in an aquatic environment. HCS, employed to determine the incidence and characterization of specificVibriospp., yielded significantly greater metagenomic insight, notably a variety of otherVibriospp., including detection ofVibrio cholerae,Vibrio fluvialis, andVibrio aestuarianus, in addition toVibrio parahaemolyticusandVibrio vulnificus, and also important virulence factors not detectable using traditional molecular methods. Thus, pathogenicVibriospp. in aquatic ecosystems may be far more common than currently understood. It is concluded that environmental surveillance should include HCS, a valuable tool for the detection and characterization of pathogenic agents in aquatic ecosystems, notably vibrios. IMPORTANCE The increasing prevalence of pathogenicVibriospp. in aquatic ecosystems, driven by climate change, is closely linked to a rise in cholera and vibriosis cases, emphasizing the need for improved environmental surveillance. Vibrios are naturally occurring in aquatic environments globally, but traditional metagenomic methods for detecting and typing pathogenicVibriospp. are challenged by their presence in relatively low abundance and ability to persist in a viable but nonculturable state. In the study reported here, hybridization capture sequencing was employed to profile low-abundanceVibriospp. in metagenomic samples, namely water and oysters collected from the Chesapeake Bay. This approach was evaluated in parallel with traditional whole-community shotgun sequencing and whole-genome sequencing ofVibrio parahaemolyticusandVibrio vulnificusstrains isolated from the samples. Results suggest pathogenicVibriospp. in aquatic ecosystems may be far more common than currently understood, when multiple methods are considered for environmental surveillance.

Microbiology↗