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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency

Many workflows in high-energy-physics (HEP) stand to benefit from recent advances in transformer-based large language models (LLMs). While early applications of LLMs focused on text generation and code completion, modern LLMs now support orchestrated agency: the coordinated execution of complex, multi-step tasks through tool use, structured context, and iterative reasoning. We introduce the HEP Toolkit for Agentic Planning, Orchestration, and Deployment (HEPTAPOD), an orchestration framework designed to bring this emerging paradigm to HEP pipelines. The framework enables LLMs to interface with domain-specific tools, construct and manage simulation workflows, and assist in common utility and data analysis tasks through schema-validated operations and run-card-driven configuration. To demonstrate these capabilities, we consider a representative Beyond the Standard Model (BSM) Monte Carlo validation pipeline that spans model generation, event simulation, and downstream analysis within a unified, reproducible workflow. HEPTAPOD provides a structured and auditable layer between human researchers, LLMs, and computational infrastructure, establishing a foundation for transparent, human-in-the-loop systems.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗
DOE OSTI2026

LeWRON: Agentic Analysis of Electroweak Phase Transitions

The electroweak phase transition (EWPT) is a central topic in particle physics and cosmology, connecting collider phenomenology, baryogenesis, and gravitational-wave observatories. Its analysis requires a technically demanding, convention-sensitive, and model-dependent pipeline, from constructing the finite-temperature effective potential to tracking thermal histories, computing bubble nucleation rates, and predicting gravitational-wave spectra. We present LeWRON (Learning ElectroWeak phase tRansitiON), an agentic framework that orchestrates this pipeline starting from an input Lagrangian. LeWRON combines audited toolbox construction with an Explorer module that uses the generated model-specific code for further analysis, including scans and plots. Intermediate analytic outputs are checked by auditor agents and stored as structured artifacts, enabling reproducible human inspection and downstream use through both a command-line interface and a public Python API. The framework supports a reproduction mode, which infers conventions from the literature and reproduces published results, and a discovery mode, which guides users through structured checkpoints for new models. We demonstrate LeWRON across representative beyond-the-Standard-Model scenarios and release the code on GitHub.

Wang, Isaac R. [Fermilab] (ORCID:000000030789218X)↗
DOE OSTI2026

Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data

We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.

Chan, Jui-Kuan [Taiwan, Natl. Taiwan U.] (ORCID:00↗
DOE OSTI2026

Component-Level Inverse Design of Transmon Qubits Using Neural Networks

Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model and evaluate the loss in Hamiltonian space rather than in layout-parameter space. In validation against a conventional EM solver, 97% of generated designs produce usable geometries, and the inverse-plus-surrogate pipeline reaches mean percent errors of 0.73% for qubit frequency and 1.58% for anharmonicity, comparable to or below the fabrication and simulation-to-measurement uncertainty expected for academic-process transmon devices of this type. A single pipeline query takes ~60 ms on CPU, versus ~2 min for a conventional EM capacitance extraction on the same hardware, a speedup of approximately 2,000x. Batching minimizes the AI model inference overhead, reducing the runtime to 3.1 microseconds per sample on CPU and 2.6 microseconds per sample on GPU at a batch size of 2048, resulting in speedups of 3.9 x 10^7 and 4.6 x 10^7, respectively, relative to a single conventional CPU EM extraction. Our results indicate that component-level inverse design usefully extends and complements conventional EM simulation, including for small datasets on the order of 1,000 samples.

Seidel, Olivia [Fermilab; Texas U., Arlington]↗
DOE OSTI2026

Atmospheric Modeling and Denoising for Millimeter-Wave Line Intensity Mapping

Line-intensity mapping (LIM) offers a promising approach to mapping large-scale cosmic structure, and the greatest obstacle for ground-based observations at millimeter wavelengths is foreground contamination from atmospheric emission. In this work, we present a simulation and denoising framework designed to isolate and subtract atmospheric fluctuations from LIM data, modeled after the instrument parameters of the South Pole Telescope Summertime Line Intensity Mapper (SPT-SLIM). We generate mock observations spanning 125-175 GHz containing cosmic signals, precipitable water vapor screens, ice crystal fluctuations, and photon noise. We then implement a spatial-spectral atmospheric removal pipeline combining per-pixel linear template regression with a two-dimensional Fourier-domain filter. The framework is evaluated under simulated conditions in the South Pole and the Atacama Desert across three key metrics: cosmic signal preservation, foreground subtraction efficiency, and instrument noise injection. Our pipeline achieves atmospheric suppression at large spatial scales, and these results establish a physically grounded foundation for atmosphere removal in ground-based LIM data collection.

Saye, L. K. [UC, Berkeley (main)] (ORCID:000900078↗
DOE OSTI2022

Moving small files in a networked environment

Globally distributed computing infrastructures, such as clouds and supercomputers, are currently used to manage data that is generated with an unprecedented speed from a variety of resources. Coping with this trend, the volume of data exchanged across distant sites increases substantially. To accelerate data transfer, high-speed networks are provided to connect remote sites. Most existing data movement solutions are optimized for moving large files. However, it is still challenging to transfer a large number of small files across networks. This disadvantage not only lowers data transfer performance, but also decreases overall system utilization. Here, we identify that moving small files is mainly constrained by degraded file system throughput, not just network performance as might be suspected. We have built a data transfer pipeline model to analyze the impact of small network I/O and storage I/O on data movement. Extending one of the widely used open source data movement solutions, GridFTP, we demonstrate several appropriate engineering approaches that mitigate the bottleneck and increase data transfer efficiency. We show optimizations that improve data transfer performance more than 5 times. In comparison to existing solutions, our approaches can save a significant amount of system resources for moving lots of small files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Effects of hydrogen partial pressure on crack initiation and growth rate in vintage X52 steel

There is an increasing interest in ensuring compatibility of existing natural gas infrastructure with conveyance of hydrogen, especially for vintage (pre-1970s) pipeline steels. While hydrogen is known to affect the fatigue and fracture resistance of these materials, there is a lack of data on its effects on crack initiation at low hydrogen partial pressure. To address this gap, the current work presents a series of fatigue tests on vintage API Grade X52 steel in pure nitrogen, air and gaseous hydrogen at partial pressures of 1 and 207 bar. Circumferential notch tension (CNT) specimens with direct current potential difference (DCPD) measurements are used to get S–N curves for crack initiation and failure, and compact tension (CT) tests to assess crack growth rates. Results indicate that air accelerates crack initiation, low hydrogen partial pressure affects the crack growth rate, and higher pressures have the largest impact on both. Surprisingly, crack initiation is faster in air than at 1 bar hydrogen partial pressure. The CNT tests also reveal that the orientation of the banded microstructure has no effect on the crack initiation site, but it influences its growth behavior. Moreover, fractography shows that hydrogen also promotes instances of quasi-cleavage and intergranular fracture, linked to embrittlement of the material. Here, these results demonstrate that, while hydrogen may impact the entirety of the fatigue process, its effects on the early stages of damage accumulation become relevant only at larger pressures. More comprehensive studies of crack initiation in hydrogen atmospheres will be critical to future codes and standards for hydrogen infrastructure.

Circumferential notch tension↗
DOE OSTI2022

High energy X-ray diffraction and small-angle scattering measurements of hydrogen fatigue damage in AISI 4130 steel

Accurate lifetime predictions are critical for repurposing existing pipelines for hydrogen transmission as well as for developing novel steels which are minimally susceptible to lifetime degradation by hydrogen. Ultimately, lifetime prediction models assess the amount of damage a material undergoes during a typical service cycle and the cumulative damage a material can withstand prior to failure. However, not all damage processes are equal, and neither is the manner in which mechanical loading translates to damage the same when materials are in inert environments compared to in hydrogen environments. For example, in the three leading proposed mechanisms of hydrogen embrittlement (Hydrogen-Enhanced Decohesion (HEDE), the Hydrogen-Enhanced Localized Plasticity (HELP), and the Nano-Void Coalescence (NVC)), hydrogen is proposed to enhance the manifestation of grain separation, dislocation generation/movement, and void coalescence, respectively. A full understanding of the damage modes requires a measurement capable of probing all three mechanisms at once. Here we present simultaneous High Energy X-ray Diffraction (HEXRD) and Small-Angle X-ray Scattering (SAXS) during fatiguing of steel in hydrogen. HEXRD measurements probe strain and dislocation density; SAXS measurements probe nano-pore generation and coalescence. We will discuss the differences in damage modes between steels fatigued in air and in hydrogen and the role these difference play in lifetime predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tracking the ancestry of known and ‘ghost’ homeologous subgenomes in model grass Brachypodium polyploids

Unraveling the evolution of plant polyploids is a challenge when their diploid progenitor species are extinct or unknown or when genome sequences of known progenitors are unavailable. Existing subgenome identification methods cannot adequately infer the homeologous genomes that are present in the allopolyploids if they do not take into account the potential existence of unknown progenitors. We addressed this challenge in the widely distributed dysploid grass genus Brachypodium, which is a model genus for temperate cereals and biofuel grasses. We used a transcriptome-based phylogeny and newly designed subgenome detection algorithms coupled with a comparative chromosome barcoding analysis. Our phylogenomic subgenome detection pipeline was validated in Triticum allopolyploids, which have known progenitor genomes, and then used to infer the identities of three subgenomes derived from extant diploid species and four subgenomes derived from unknown diploid progenitors (ghost subgenomes) in six Brachypodium polyploids (B. mexicanum, B. boissieri, B. retusum, B. phoenicoides, B. rupestre and B. hybridum), of which five contain undescribed homeologous subgenomes. The existence of the seven Brachypodium progenitor genomes in the polyploids was confirmed by their karyotypic barcode profiles. Comparative phylogenomics of nuclear versus plastid trees allowed us to formulate hypothetical homoploid hybridizations and allo- and autopolyploidization scenarios that could have generated the six Brachypodium polyploids.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A high-throughput structural system biology approach to increase structure representation of proteins from Clostridioides difficile

ABSTRACT Clostridioides difficile causes life-threatening gastrointestinal infections. It is a high-risk pathogen due to a lack of effective treatments, antimicrobial resistance, and a poorly conserved genomic core. Herein, we report 30 X-ray structures from a structure genomics pipeline spanning 13 years, representing 10.2% of the X-ray structures for this important pathogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Tidal Resource Data from Sequim Bay Inlet, WA, August 2020

Data from a Nortek Signature1000 deployed on a lander for 14 days in Aug 2020 in the entrance to Sequim Bay, WA. Raw data were processed using the DOLfYN python package and standardized using the ME Data Pipeline python package, tsdat version 0.2.12. Processed data were partitioned into 24 hour increments and saved in the NETCDF file format.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Offshore Advanced Infrastructure Integrity Model (AIIM) Dashboard

The Advanced Infrastructure Integrity Model (AIIM) is a multivariate, multi-machine learning modeling technology applied to evaluate the integrity of offshore energy infrastructure (e.g., pipelines, platforms) in the U.S Gulf Region. Offshore energy infrastructure plays an essential role in ensuring access to safe and secure energy for the United States. According to the U.S. Energy Information Administration (EIA), production in the U.S. Gulf Region accounts for 15% of total crude and 5% of total natural gas from the United States. Many of these structures have been operating for close to or past their design life, while others have the chance of attrition before return on investment. To better understand the potential for reuse or life extension opportunities, an assessment of the infrastructure integrity is critical to inform safe decision making. Assessing structural integrity, AIIM provides key insights that inform infrastructure use and reuse, as well as hazard prevention planning, in support of stakeholders including researchers and industry.

Advanced Infrastructure Integrity Model↗
DOE OSTI2022

Phase I: Natural Gas-Based Energy Storage at Abbott Power Plant

Compressed Natural Gas Energy Storage (CNGES), uses the potential energy of compressed natural gas within an integrated energy storage system. Specifically, an inventory of high-pressure natural gas is stored during off-peak hours in a first stage by controlling the balance of gas entering a pipeline from the transmission system, and in a second stage by using new compressors to fill vessels onsite. The natural gas storage medium is then discharged through an expander-generator to use the potential energy from the gas to generate electricity without combustion. The excess chemical energy stored in the natural gas is integrated with an existing fossil-based asset to provide power generation during peak hours and increase the reliability of the associated fossil generating facility.

03 NATURAL GAS↗
DOE OSTI2020

Sensor Enabled Coatings for Methane Release Mitigation

Methane as a greenhouse gas is as much as 30 times more efficient than carbon dioxide. Given the increasing reliance on natural gas, methane release mitigation has become an increasingly important issue to address. In this investigation, PPG, RTI, and GTI focused on the exploration of resonant frequency identification (RFID) sensors embedded in natural gas pipeline coatings and their ability to detect coating damage at an early stage. Various coating formulations and their respective physical properties were examined to determine their effect on sensor performance in various accelerated aging environments. Overall, it was found that the RFID sensor responses could not be correlated to the coating properties and formulation variables examined in this investigation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Resilient Energy Delivery and Control Systems (REDCS) (Final Technical Report)

US critical infrastructure is increasingly the target of cyberattacks, where disturbances could cause considerable damage and disruption. To help provide a new layer of cyber-physical protection for one key energy delivery system, natural gas pipelines, GE Vernova Advanced Research along with partners Florida State University and Intel Corporation created an innovative technology called "Resilient Energy Delivery and Control Systems" (REDCS). This cybersecurity package helps detect anomalies caused by cyberattacks, isolate the subsystem being impacted by the attack, and provide functions that can allow for resiliency – giving better situational awareness to the operators and cybersecurity specialists or in the future perform closed loop control for continued operation while compromised.

03 NATURAL GAS↗
DOE OSTI2024

New Energy Infrastructure Outlook: Data as of December 31, 2024 [Slides]

This report provides a perspective on energy infrastructure under development in the continental U.S. as of the end of 2024, focusing on those making significant progress toward achieving commercial operation. Infrastructures covered in this report include power plants, electric transmission, natural gas pipelines and liquefied natural gas facilities. Additionally, this report includes a section on stockpiled volumes of coal, natural gas, and petroleum.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Simple, Tough, and Effective Porous Coating for Anti-Corrosion Application

This presentation discusses how a change in polymeric binder, the material used for assembling hydrophobic nanoparticles into a porous structure, led to better mechanical toughness and a simpler fabrication process based on 100% commercially available components. Most importantly, the optimized porous coating can reduce the corrosion rate by two orders of magnitude (0.003 mm/year) due to higher diffusion barrier based on a more resistant tortuous diffusion path. The combination of simplicity, toughness, effectiveness, and scalability greatly enhanced the prospect of commercializing this technology as an internal anti-corrosion coating for natural gas pipelines.

internal corrosion↗