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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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33 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Deep learning model to detect various synchrophasor data anomalies

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Additionally, four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.

42 ENGINEERING↗
DOE OSTI2023

Variability in soil redox response to seasonal flooding in a vernal pond

Vernal ponds are ephemeral landscape features that experience intermittent flooding and drying, leading to variable saturation in underlying soils. Redox potential (E h ) is an important indicator of biogeochemical processes that changes in response to these hydrological shifts; however, high-resolution measurements of E h in variably inundated environments remain sparse. In this study, the responses of soil E h to ponding, drying, and rewetting of a vernal pond were investigated over a 5-month period from late spring through early autumn. Soil E h was measured at 10-min frequencies and at multiple soil depths (2–48 cm below the soil surface) in shallow and deep sections within the seasonally ponded lowland and in unsaturated soils of the surrounding upland. Over the study period, average E h in surface soils (0–8 cm) was oxidizing in the upland (753 ± 79 mV) but relatively reducing in the shallow lowland (369 ± 49 mV) and deep lowland (198 ± 37 mV). Reducing conditions (E h <300 mV) in surface soils prevailed for up to 6 days in the shallow lowland and up to 24 days in the deep lowland after surface water dried out. Intermittent reflooding resulted in multiple shifts between reducing and oxidizing conditions in the shallow lowland while the deep lowland remained reducing following reflooding. Soil E h in the uplands was consistently oxidizing over the study period with transient increases in response to rain events. Reducing conditions in the lowland resulted in greater Fe-oxide dissolution and release of dissolved Fe and P into porewater than in the surrounding uplands. We determined that change in water depth alone was not a good indicator of soil E h , and additional factors such as soil saturation and clay composition should be considered when predicting how E h responds to surface flooding and drying. These findings highlight the spatial and temporal variability of E h within ponds and have implications for how soil processes and ecosystem function are impacted by shifts in hydrology at terrestrial-aquatic interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Recommendations for Uniform Variant Calling of SARS-CoV-2 Genome Sequence across Bioinformatic Workflows

Genomic sequencing of clinical samples to identify emerging variants of SARS-CoV-2 has been a key public health tool for curbing the spread of the virus. As a result, an unprecedented number of SARS-CoV-2 genomes were sequenced during the COVID-19 pandemic, which allowed for rapid identification of genetic variants, enabling the timely design and testing of therapies and deployment of new vaccine formulations to combat the new variants. However, despite the technological advances of deep sequencing, the analysis of the raw sequence data generated globally is neither standardized nor consistent, leading to vastly disparate sequences that may impact identification of variants. Here, we show that for both Illumina and Oxford Nanopore sequencing platforms, downstream bioinformatic protocols used by industry, government, and academic groups resulted in different virus sequences from same sample. These bioinformatic workflows produced consensus genomes with differences in single nucleotide polymorphisms, inclusion and exclusion of insertions, and/or deletions, despite using the same raw sequence as input datasets. Here, we compared and characterized such discrepancies and propose a specific suite of parameters and protocols that should be adopted across the field. Consistent results from bioinformatic workflows are fundamental to SARS-CoV-2 and future pathogen surveillance efforts, including pandemic preparation, to allow for a data-driven and timely public health response.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Quantum physics of stars

Stars are slowly developing objects; the lifetimes of the different burning phases are determined by the strength of nuclear reactions, which in turn are defined by the quantum structure of the associated nuclei at the threshold and the respective reaction mechanisms. Stars, from the nuclear physics perspective, are cold environments where only a few of the key nuclear reactions have been measured at the actual stellar plasma temperatures. This is also the case for more dynamic astrophysical phenomena from the big bang to stellar explosions. Most of the nuclear reaction rates are therefore based on theoretical extrapolations. A number of discrepancies between these predictions and the associated stellar signatures have been observed, and many may be due to low-energy or near-threshold quantum effects. These effects need to be understood in order to reliably model nuclear reaction processes, not only for stars but also for low-temperature plasma environments such as controlled magnetic or inertial confinement fusion systems, which operate in similar temperature regimes. This review summarizes the various theoretical techniques presently used for deriving reaction rates and discusses possible quantum effects that may impact the reaction cross section near the reaction threshold. These resemble enhanced single-particle and cluster structures near threshold and associated interference effects. New experimental techniques such as deep-underground accelerators or the study of transfer reactions to mimic the quantum-mechanical transition strength, the so-called Trojan horse method, provide ways to directly or indirectly probe the reaction features that determine the reaction rates at stellar energies. Furthermore, this is demonstrated on a number of key nuclear reactions for different nucleosynthesis environments. Finally, current inconsistencies between experimental predictions and observations are discussed.

Models & methods for nuclear reactions↗
DOE OSTI2026

SIDIS Unpolarized Cross Sections from a 3 He Target with the Solenoidal Large Intensity Device at JLab

In this paper we present a detailed impact study of semi-inclusive deep inelastic scattering unpolarized cross sections' measurements using the proposed SoLID apparatus at Jefferson Lab. This type of data, collected at large Bjorken x bj , moderate values of Q 2 and small values of the transverse momentum of produced hadrons, P hT , allows to study transverse momentum dependent (TMD) parton distribution and fragmentation functions in a still poorly explored region. We present the projected results for charged light mesons based on simulated data. For the azimuthal-angle integrated cross sections we adopt the TMD framework up to the next-to-next-to-next-to-leading-logarithmic accuracy, while a simpler TMD parton model is employed for the study of azimuthal angular dependencies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Probabilistic simulation of quantum circuits using a deep-learning architecture

The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasistochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits using an attention network based on a powerful neural network ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits that build Greenberger-Horne-Zeilinger and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on several system sizes. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Study of SIDIS Unpolarized Cross Sections from a 3 He Target with the Solenoidal Large Intensity Device at JLab

In this paper we present a detailed impact study of semi-inclusive deep inelastic scattering unpolarized cross sections' measurements using the proposed SoLID apparatus at Jefferson Lab. This type of data, collected at large Bjorken $x_{bj}$, moderate values of $Q^2$ and small values of the transverse momentum of produced hadrons, $P_{hT}$, allows to study transverse momentum dependent (TMD) parton distribution and fragmentation functions in a still poorly explored region. We present the projected results for charged light mesons based on simulated data. For the azimuthal-angle integrated cross sections we adopt the TMD framework up to the next-to-next-to-next-to-leading-logarithmic (N3LL) accuracy, while a simpler TMD parton model is employed for the study of azimuthal angular dependencies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Collins function for pion-in-jet production in polarized pp collisions: a test of universality and factorization

We present an updated study of the Collins azimuthal asymmetries for pion-in-jet production in polarized pp collisions. To this aim, we employ a recent extraction of the transversity and Collins fragmentation functions from semi-inclusive deep inelastic scattering and e + e - annihilation into hadron pairs processes, obtained within a simplified transverse momentum dependent (TMD) approach at leading order in the strong coupling constant α s . In the present case we adopt a collinear configuration for the initial state, keeping transverse momentum effects only in the fragmentation mechanism. Our theoretical estimates, when compared against 200 GeV and 510 GeV data from the STAR Collaboration, show a generally good agreement for the distributions in the transverse momentum of the jet, the pion longitudinal momentum fraction and its transverse momentum with respect to the jet direction. While not being a proof, due the assumptions and limitations behind the entire approach, these findings corroborate the hypothesis of TMD factorization for such processes as well as of the universality of the Collins function and, once again, of a reduced impact of the proper TMD evolution on azimuthal asymmetries. We will also present predictions based on an extraction of the Collins and transversity distributions where information from data on single spin asymmetry for inclusive pion production in p ↑ p collisions is included through a Bayesian reweighting procedure.

Azimuthal asymmetries↗
DOE OSTI2022

Modeling the Economic and Environmental Impacts of Land Scarcity Under Deep Uncertainty

Abstract Land scarcity is increasing over time, driven by complex multisector dynamics. The impacts of land scarcity on the economy and environment are multi‐faceted and regional, so any action to convert land will contain inherent tradeoffs. These impacts are complicated by the deeply uncertain evolution of the various sectors influencing land scarcity. A need therefore exists to provide multi‐metric and multi‐sector assessments that are robust to myriad uncertainties. Land conservation effectively limits the supply of productive land, while biofuel consumption increases the demand and competition for that land, and how these dynamics individually and jointly propagate to economic and environmental impacts is an important open question. To address this, we adopt the Global Change Analysis Model (GCAM) that has representations of various important systems including the climate, macroeconomic, energy, agriculture and land, and water resources systems. Various scenarios of increased land demand (from biofuels) and decreased land supply (from conservation) under various socioeconomic scenarios drawn from the SSPs were simulated using GCAM. We find that while biofuel consumption and land conservation reduce carbon emissions, this comes at the cost of higher food prices, reduced crop production, and increased water withdrawals. Additionally, some regions experience these tradeoffs more severely than others and are more heavily impacted from the same biofuel mandate or by an additional percent of protected land. These and other findings highlight the importance of multisector modeling frameworks that capture many cross‐sector linkages, and acknowledge the important uncertainties confronting the human‐Earth system when making any analysis of land scarcity impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Extraordinary March 2022 East Antarctica “Heat” Wave. Part I: Observations and Meteorological Drivers

Abstract Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30°–40°C temperature anomalies across the ice sheet. This record-shattering event saw numerous monthly temperature records being broken including a new all-time temperature record of −9.4°C on 18 March at Concordia Station despite March typically being a transition month to the Antarctic coreless winter. The driver for these temperature extremes was an intense atmospheric river advecting subtropical/midlatitude heat and moisture deep into the Antarctic interior. The scope of the temperature records spurred a large, diverse collaborative effort to study the heat wave’s meteorological drivers, impacts, and historical climate context. Here we focus on describing those temperature records along with the intricate meteorological drivers that led to the most intense atmospheric river observed over East Antarctica. These efforts describe the Rossby wave activity forced from intense tropical convection over the Indian Ocean. This led to an atmospheric river and warm conveyor belt intensification near the coastline, which reinforced atmospheric blocking deep into East Antarctica. The resulting moisture flux and upper-level warm-air advection eroded the typical surface temperature inversions over the ice sheet. At the peak of the heat wave, an area of 3.3 million km 2 in East Antarctica exceeded previous March monthly temperature records. Despite a temperature anomaly return time of about 100 years, a closer recurrence of such an event is possible under future climate projections. In Part II we describe the various impacts this extreme event had on the East Antarctic cryosphere. Significance Statement In March 2022, a heat wave and atmospheric river caused some of the highest temperature anomalies ever observed globally and captured the attention of the Antarctic science community. Using our diverse collective expertise, we explored the causes of the event and have placed it within a historical climate context. One key takeaway is that Antarctic climate extremes are highly sensitive to perturbations in the midlatitudes and subtropics. This heat wave redefined our expectations of the Antarctic climate. Despite the rare chance of occurrence based on past climate, a future temperature extreme event of similar magnitude is possible, especially given anthropogenic climate change.

Wille, Jonathan D.↗
DOE OSTI2023

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery

Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations: lack of interoperability (repetitive manual tool-integration is required across scenarios) and the need for scalable coordination (unstructured communication and memory become brittle as the number of agents grows). In this Perspective, we argue that the next phase of agentic scientific discovery requires the development of an ecosystem of protocol-native agents and tools organized through hierarchies inspired by human society, beyond the current paradigm of a single monolithic “AI scientist”. We use Model Context Protocol (MCP) as a concrete example of an emerging interoperability layer for scientific tool and context exchange, and we propose three complementary pathways to increase the scaling capabilities of an MCP-native scientific ecosystem by addressing the composability issues: (1) MCP servers for high-value scientific tools maintained by domain experts, (2) automated transformation of existing code repositories into MCP services, and (3) autonomous invention and evolution of new agents and workflows. Finally, we provide a practical roadmap for scaling AI-driven scientific discovery by expanding tool supply and coordination in MCP-native scientific ecosystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multidimensional measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic charged-kaon electroproduction off protons in the valence region

Measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic electron scattering (SIDIS) with positively charged kaons off protons have been performed with 10.6 and 10.2 GeV incident electron beams using the CLAS12 spectrometer at Jefferson Lab. We report an analysis of the electroproduction of positively charged kaons over a large kinematic range of fractional energy, Bjorken 𝑥, transverse momentum, and photon virtualities 𝑄 2 ranging from 1 GeV 2 up to 6 GeV 2 . This is the first published multidimensionally binned CLAS12 measurement of a kaon SIDIS single-spin asymmetry in the valence quark regime. The data provide constraints on the structure function ratio $𝐹^{sin \space𝜙}_{𝐿⁢𝑈}$⁡/$𝐹_{𝑈⁢𝑈}$, where $𝐹^{sin \space𝜙}_{𝐿⁢𝑈}$ is a quantity with a leading twist of twist-3 that can reveal novel aspects of the quark-gluon correlations within the nucleon. The impact of the data on understanding the underlying reaction mechanisms and their kinematic variation is explored using theoretical models for the different contributing twist-3 parton-distribution functions (PDFs) and fragmentation functions (FFs).

form factors↗
DOE OSTI2026

Reconstruction of atmospheric neutrinos in DUNE’s horizontal-drift far-detector module

This paper reports on the capabilities in reconstructing and identifying atmospheric neutrino interactions in one of the Deep Underground Neutrino Experiment’s (DUNE) far detector modules, a liquid argon time projection chamber (LArTPC) with horizontal drift (FD-HD) of ionization electrons. The reconstruction is based upon the workflow developed for DUNE’s long-baseline oscillation analysis, with some necessary machine-learning models’ retraining and the addition of features relevant only to atmospheric neutrinos such as the neutrino direction reconstruction. Where relevant, the impact of the detection of the charged particles of the hadronic system is emphasized, and comparisons are carried out between the case when lepton-only information is considered in the reconstruction (as is the case for many neutrino oscillation experiments), versus when all particles identified in the LArTPC were included. Three neutrino direction reconstruction methods have been developed and studied for the atmospheric analyses: using lepton-only information, using all reconstructed particles, and using only correlations from reconstructed hits. The results indicate that incorporating more than just lepton information significantly improves the resolution of both neutrino direction and energy reconstruction. The angle reconstruction algorithms developed in this work result in no strong dependence on particle direction for reconstruction efficiencies or neutrino flavor identification. This comprehensive review of the reconstruction of atmospheric neutrinos in DUNE’s FD-HD LArTPC is the first step towards developing a first neutrino oscillation sensitivity analysis, which will ready DUNE for its first measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

The SPT-deep Cluster Catalog: Sunyaev–Zel’dovich Selected Clusters from Combined SPT-3G and SPTpol Measurements over 100 Square Degrees

We present a catalog of 500 galaxy cluster candidates in the SPT-Deep field: a 100 deg$^{2}$ field that combines data from the SPT-3G and SPTpol surveys to reach noise levels of 3.0, 2.2, and 9.0 μK-arcmin at 95, 150, and 220 GHz, respectively. Candidates are selected via the thermal Sunyaev–Zel’dovich (SZ) effect with a minimum significance of ξ = 4.0, resulting in a catalog of purity ∼89%. Optical data from the Dark Energy Survey and infrared data from the Spitzer Space Telescope are used to confirm 442 cluster candidates. The clusters span 0.12 < z ≲ 1.8 and 1.0 × 10$^{14}$M$_{⊙}$/h$_{70}$ < M$_{500c}$ < 8.7 × 10$^{14}$M$_{⊙}$/h$_{70}$. The sample’s median redshift is 0.74, and the median mass is 1.7 × 10$^{14}$M$_{⊙}$/h$_{70}$; these are the lowest median mass and highest median redshift of any SZ-selected sample to date. We assess the effect of infrared emission from cluster member galaxies on cluster selection by performing a joint fit to the infrared dust and tSZ signals by combining measurements from SPT and overlapping submillimeter data from Herschel/SPIRE. We find that at high redshift (z > 1), the tSZ signal is reduced by $17.9_{−3.2}^{+3.8}$%$(3.8_{−0.7}^{+0.9}$%$)$ at 150 GHz (95 GHz) due to dust contamination. We repeat our cluster finding method on dust-nulled SPT maps and find the resulting catalog is consistent with the nominal SPT-Deep catalog, suggesting dust contamination does not significantly impact the SPT-Deep selection function; we attribute this lack of bias to the inclusion of the SPT 220 GHz band.

79 ASTRONOMY AND ASTROPHYSICS↗