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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 OSTI2026

Investigation of the 244 Pu ⁢( 48 Ca,𝑥⁢𝑛) 292−𝑥 Fl reaction with the LBNL SHREC detector: Investigation of decay chains of isotopes of flerovium (𝑍=114)

The 244 Pu ⁢( 48 Ca,𝑥⁢𝑛)⁢ 292−𝑥 Fl reaction was investigated at Lawrence Berkeley National Laboratory’s 88 Inch Cyclotron using the Berkeley Gas-filled Separator (BGS), the newly installed Superheavy Recoil detector, along with an upgraded digital electronics and data acquisition system. Seven decay chains were observed starting with an evaporation residue, followed by a single 𝛼 decay and a spontaneous fission. The decay characteristics of these seven decay chains led to an assignment to 288 Fl , the product of the 4⁢𝑛 reaction channel. Two additional chains were (tentatively) assigned to the decay of the 3⁢𝑛 exit channel, 289 Fl . Cross sections for the 4⁢𝑛 and 3⁢𝑛 exit channels were 𝜎 prod =6.7⁢($^{36}_{25}$) pb and 𝜎 prod =1.6⁢($^{22}_{11}$) pb, respectively. Another decay chain, tentatively assigned to the 5⁢𝑛 exit channel through the 48 Ca + 244 Pu reaction or the 3⁢𝑛 exit channel of the 48 Ca + 242 Pu reaction, was also detected. Detailed information regarding the observed decay chains and their nuclear structure aspects is discussed, along with the performance of the BGS and the new detection system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Solar neutrino measurements using the full data period of Super-Kamiokande-IV

An analysis of solar neutrino data from the fourth phase of Super-Kamiokande (SK-IV) from October 2008 to May 2018 is performed and the results are presented. The observation time of the dataset of SK-IV corresponds to 2970 days and the total live time for all four phases is 5805 days. For more precise solar neutrino measurements, several improvements are applied in this analysis: lowering the data acquisition threshold in May 2015, further reduction of the spallation background using neutron clustering events, precise energy reconstruction considering the time variation of the PMT gain. The observed number of solar neutrino events in 3.49–19.49 MeV electron kinetic energy region during SK-IV is 65,443 − 388 + 390 ( stat . ) ± 925 ( syst . ) events. Corresponding B 8 solar neutrino flux is ( 2.314 ± 0.014 ( stat . ) ± 0.040 ( syst . ) ) × 10 6 cm − 2 s − 1 , assuming a pure electron-neutrino flavor component without neutrino oscillations. The flux combined with all SK phases up to SK-IV is ( 2.336 ± 0.011 ( stat . ) ± 0.043 ( syst . ) ) × 10 6 cm − 2 s − 1 . Based on the neutrino oscillation analysis from all solar experiments, including the SK 5805 days dataset, the best-fit neutrino oscillation parameters are sin 2 θ 12 , solar = 0.306 ± 0.013 and Δ m 21 , solar 2 = ( 6.1 0 − 0.81 + 0.95 ) × 10 − 5 eV 2 , with a deviation of about 1.5 σ from the Δ m 21 2 parameter obtained by KamLAND. The best-fit neutrino oscillation parameters obtained from all solar experiments and KamLAND are sin 2 θ 12 , global = 0.307 ± 0.012 and Δ m 21 , global 2 = ( 7.5 0 − 0.18 + 0.19 ) × 10 − 5 eV 2 . Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Tailoring spectral properties for entangled photon generation

The linear intensity scaling of entangled two-photon absorption (ETPA) offers a fundamental mechanism to enable nonlinear optical spectroscopy and microscopy under a substantially lower excitation power than is currently feasible. However, the long data acquisition time and low signal-to-noise ratio in reported ETPA-based spectroscopic and microscopic studies prevent its widespread application. To gain the full potential of this novel quantum light approach, it is essential to optimize entangled photon generation for significantly enhanced ETPA responses. Here, we report a new quantum light source by combining entangled photon generation with free-space femtosecond pump-pulse shaping. Here, through measurements of singles and coincidence counts by varying the patterns applied to a spatial light modulator to control the spectral widths of the pump pulses, we reveal strong dependence of the singles and coincidence count rates as well as their ratios on the spectral widths of pump pulses. An optimal spectral width for the highest ratios between the coincidence photon and the signal or idler photon count rates is also determined. Identification of such optimal spectral widths for entangled photon generation in the presence of postgeneration spectral selection makes this quantum light source a promising choice for ETPA-based spectroscopy and imaging.

Photon pairs & parametric down-conversion↗
DOE OSTI2025

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.

Obata, Yoshihiro (ORCID:0000000303659129)↗
DOE OSTI2025

Nano-laminography with a transmission X-ray microscope

Nano-laminography combines the penetrating power of hard X-rays with a tilted rotational geometry to deliver high-resolution, three-dimensional images of laterally extended, flat specimens that are otherwise incompatible with, or difficult to image using, conventional nano-tomography. In this work, we demonstrate a full-field, X-ray nano-laminography system implemented with the transmission X-ray microscope at beamline 32-ID of the upgraded Advanced Photon Source at Argonne National Laboratory, USA. By rotating the sample around an axis inclined by 20° to the incident beam, the technique minimizes the long optical path lengths that would otherwise generate excessive artifacts when planar samples are imaged edge-on. The efficiency of the technique is demonstrated with 50 nm spatial resolution and minute-scale temporal resolution 3D imaging of a planar integrated circuit sample and targeted imaging of an individual particle within a powder sample, where mounting procedures are typically challenging in regular nano-tomography. The sample mounting strategy, data acquisition, and reconstruction method will also be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A Decision Support System to Compile Environmental Mitigations from Hydropower Licensing Documents

The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge in the respective field, resulting in a labor-intensive process when executed by humans. Furthermore, the task of identifying multiple class labels in extensive texts presents a challenge due to intra- and inter-reader variability, making the process time-consuming and costly.We’re introducing a user-friendly graphical interface, fortified with a BERT model-powered decision support system. This advanced system aims to augment efficiency, curtail data collection time, and sustain high precision in data acquisition. It is instrumental in deciphering and synthesizing intricate texts teeming with a spectrum of expressions, even within similar mitigation categories. Such tasks traditionally demand substantial human effort and specialized knowledge in the domain.Our system is specifically engineered for the task of extracting environmental mitigation information to promote sustainable hydropower development from licenses issued by the Federal Energy Regulatory Commission (FERC). These license documents are comprehensive, each containing over 15,000 words and requiring the identification of 135 different class labels. We anticipate that our system will boost reading speed, improve the consistency of classification outputs among readers, and contribute to the development of a robust scientific database of environmental mitigations associated with the 2,000+ non-federal hydropower facilities licensed by FERC in the United States.

Yoon, Hong-Jun [ORNL] (ORCID:0000000254505878)↗
DOE OSTI2024

Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification

Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs) at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring, Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases, distribution system state estimation (DSSE) and fault identification are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1 feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.

distribution system↗
DOE OSTI2025

Adaptive Computing for Scale-Up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Online Detection of Power Grid Anomalies via Federated Learning

Data from sensors is critical for advanced applica- tions that support efficient, reliable, and resilient electric grid operations. Historically, data from phasor measurement units (PMU) has been utilized to develop a wide variety of wide area control and protection applications suitable for power grid control centers. However, until now, most of these could not be deployed for automated operations due to a set of data corruption challenges and uncertainty in the incoming data pipeline. In this paper, we address the problem of detecting different variety of anomalies that are evident in different high- speed power grid measurements. The paper discusses a workflow for handling problems with data acquisition and highlights some of the key findings suitable for anomaly detection in a centralized and distributed environment. The effectiveness of the proposed method was demonstrated with results utilizing realistic PMU datasets

Shinkle, Matthew W.↗
DOE OSTI2024

PMU Data Quality and Sensor Health Monitoring

Phasor Measurement Units (PMUs) play a critical role in the evolution of the electric power industry by providing high-precision, real-time monitoring of essential power system metrics. However, effectively detecting abnormalities and critical events from PMU data is a complex task, complicated by intricate temporal patterns, a scarcity of labeled data for training algo- rithms, and constraints on online computational power. In this study, we apply TranAD, an innovative algorithm that combines transformer architectures with the refinement of adversarial learning, to both synthetic and real-world PMU datasets for developing a data quality and sensor online health monitoring platform for utilities. Our findings reveal that TranAD not only provides efficient detection and localization but also enhances the detail with which abnormalities are detected, marking a a significant step forward in the field of clean data acquisition processes for power system monitoring

deep neural network, machine learning (ML)↗
DOE OSTI2024

Reinforcement Learning-Based Approach for EMT Automation of Large-Scale PV Plants

In the pursuit of efficient and precise modeling of large-scale power systems, particularly utility-scale photovoltaic (PV) plants, Electromagnetic Transient (EMT) simulations play a crucial role. As utility-scale PV plants increase in size and complexity, traditional computational methods become inadequate, necessitating more advanced techniques. This paper highlights the progressive efforts made to accelerate EMT simulations. A novel continuous reinforcement learning (RL) strategy is explored to automate the differentiation and categorization of stiff and non-stiff differential algebraic equations (DAEs). The use of stiff and non-stiff integration methods applied to relevant parts of the DAEs assists with the speed-up of the simulations. The paper details the data acquisition, development and offline training of the RL model, leading to its validation that demonstrates a high precision in optimizing simulation methods. The proposed RL promises to significantly enhance the efficacy of EMT simulations, offering a robust framework for the future of power system analysis.

Xia, Qianxue↗
DOE OSTI2026

Commissioning of the large-scale lead tungstate scintillating calorimeter

Here, we report on the installation and initial commissioning of a large-scale lead tungstate (PbWO4) scintillating crystal calorimeter developed for high-rate photon detection and precise energy measurement. The calorimeter comprises 1596 high-granularity, high-resolution scintillating crystals optimized for electromagnetic-shower detection over a wide energy range. Scintillation light from each crystal is read out by Hamamatsu R4125 photomultiplier tubes equipped with a custom voltage divider and front-end amplifier to ensure stable gain at high rates. All calorimeter modules were fabricated and characterized using a light-emitting diode–based optical test system prior to installation to verify uniformity and photodetector performance. After installation, the electromagnetic calorimeter was fully integrated into the experiment data acquisition and energy-based trigger systems. The optical response of the modules was equalized using the light-monitoring system, cosmic-ray muons, and photons from Compton-scattering events. Commissioning results demonstrate a reliably calibrated optical response and stable detector performance during the first run. These results validate the calorimeter design and commissioning methodology for large-scale scintillator-based photonic instrumentation.

Analog to digital converters↗
DOE OSTI2025

Fine structure in the α decay of $^{179}$Hg and $^{177}$Au

Abstract The$$\upalpha $$ α -decay fine structure of$$^{179}$$ 179 Hg and$$^{177}$$ 177 Au was studied by means of decay spectroscopy. Two experiments were performed at the Accelerator Laboratory of the University of Jyväskylä (JYFL), Finland, utilizing the recoil separator RITU and a digital data acquisition system. The heavy-ion induced fusion-evaporation reactions$$^{82}_{36}$$ 36 82 Kr + $$^{100}_{44}$$ 44 100 Ru and$$^{88}_{38}$$ 38 88 Kr + $$^{92}_{42}$$ 42 92 Mo were used to produce the$$^{179}$$ 179 Hg and$$^{177}$$ 177 Au nuclei, respectively. Studying the evaporation residues (ER, recoils)-$$\alpha _1$$ α 1 -$$\alpha _2$$ α 2 correlations and$$\upalpha $$ α -$$\gamma $$ γ coincidences, a new$$\upalpha $$ α decay with E$$_\alpha $$ α = 6156(10) keV was observed from$$^{179}$$ 179 Hg. This decay populates the (9/2$$^-$$ - ) excited state at an excitation energy of 131.3(5) keV in$$^{175}$$ 175 Pt. The internal conversion coefficient for the 131.3(5) keV transition de-exciting this state was measured for the first time. Regarding the$$^{177}$$ 177 Au nucleus, a new$$\upalpha $$ α decay with E$$_\alpha $$ α = 5998(9) keV was observed to populate the 156.1(6) keV excited state in$$^{173}$$ 173 Ir. Two de-excitation paths were observed from this excited state. Moreover, a new 215.7(13) keV transition was observed to depopulate the 424.4(13) keV excited state in$$^{173}$$ 173 Ir. Properties of the$$^{179}$$ 179 Hg and$$^{177}$$ 177 Au$$\upalpha $$ α decays were examined in a framework of reduced widths and hindrance factors. For clarity and simplicity, the spin and parity assignments (e.g.$$J^{\pi }$$ J π ) are presented without brackets throughout the text.

Physics↗
DOE OSTI2024

Measurement of the electric potential and the magnetic field in the shifted analysing plane of the KATRIN experiment

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after 5 years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN’s main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous 83m Kr, which enables the neutrino-mass measurements in the SAP configuration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models

Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images from diverse sources—such as varying physical groundings or data acquisition systems—and to learn spatio-temporal correlations using transformer architectures. However, tokenizing and aggregating images can be compute-intensive, a challenge not fully addressed by current distributed methods. In this work, we introduce the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach designed for datasets with a large number of channels across image modalities. Our method is compatible with any model-parallel strategy and any type of vision transformer architecture, significantly improving computational efficiency. We evaluated D-CHAG on hyperspectral imaging and weather forecasting tasks. When integrated with tensor parallelism and model sharding, our approach achieved up to a 75% reduction in memory usage and more than doubled sustained throughput on up to 1,024 AMD GPUs on the Frontier Supercomputer.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗
DOE OSTI2024

Digital Twin for Chemical Science (DTCS) v0.01

Directly visualizing the trajectories of chemistry can unravel novel insights into the behavior of catalysts, gas phase reactions, photo-induced dynamics, and building blocks for quantum information processing. The ability of explicitly identifying, tracking, and tagging the exchange of matter, hence the annihilation and creation of new chemical species, can be best realized through a close coupling of theory and experiment. While the synchrotron-based characterization facilities propelled rapidly in its hardware, providing higher brightness, better resolution, and more precision, the software infrastructure is lagging. We developed DTCS (Digital Twin for Chemical Science) v.01, a central platform that faithfully mimics advanced instrumentations in Scientific User Facilities, by solving a variety of technical challenges in data acquisition, analysis, and model-driven interpretation. Rooted in physics and accelerated by AI, we validated this concept by direct comparison with precise experimental X-ray Photoelectron Spectroscopy (XPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O as our main narrative. The DTCS v.01 input mirrors how the bench chemists work, with the output directly linked to the end station computer, thereby providing a user-friendly, knowledge-driven, and accessible user experience with mechanistic insights standardized in a way that are ready to be published, versioned, and transferred flexibly.

Qian, Jin↗
DOE OSTI2025

otsdaq

otsdaq is a Ready-to-Use data-acquisition (DAQ) solution aimed at scaling down to test-beam, detector development, and other rapid-deployment scenarios; and scaling up through the development cycle to fullscale production and operation. otsdaq uses the artdaq DAQ framework under-the-hood, providing flexibility and scalability to meet evolving DAQ needs. otsdaq provides a library of supported front-end boards and firmware modules which implement a custom UDP protocol. Additionally, an integrated Run Control GUI and readout software are provided, preconfigured to communicate with otsdaq firmware.

Rivera, Ryan [Fermi National Accelerator Laborator↗
DOE OSTI2025

ACTIVE

The Automated Control Testbed for Integration, Verification, and Emulation (ACTIVE) framework is a software platform designed to support the optimized operation and management of a wide range of building types. It enables the development, testing, and validation of diverse control strategies, including AI-based, rule-based, and model-based approaches. The platform facilitates a seamless transition from simulation-based evaluation of control strategies to real-world field validation and deployment. ACTIVE supports the full building management lifecycle, encompassing data acquisition and management, system monitoring, optimized control, adaptive learning services, device dispatch and coordination, as well as advanced analytics and visualization. Together, these capabilities provide an integrated environment for improving building performance, operational efficiency, reducing energy cost, and reliability.

Smith, Robert [Oak Ridge National Laboratory (ORNL↗