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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

MEASUREMENT OF Fn 2 /Fp 2 FROM DEEP INELASTIC ELECTRON SCATTERING OFF A=3 MIRROR NUCLEI AT JEFFERSON LAB

This research is part of the Je?erson Lab MARATHON experiment (MeAsurement of the Fn 2 /Fp 2 , d/u RAtios and A = 3 EMC E?ect in Deep Inelastic Electron Scattering Off the Tritium and Helium MirrOr Nuclei). MARATHON is an experiment on electron deep inelastic scattering off the 3H, 3He mirror nuclei. It has extracted, from the measured deep inelastic scattering cross section ratio o? 3H and 3He, the ratio of the neutron to proton inelastic structure functions Fn 2 /Fp 2 . The extraction has taken advantage of the isospin symmetry of the A = 3 mirror nuclei within a novel technique, which avoids the theoretical uncertainties that are present in the SLAC experiments of the 1970s, which used hydrogen and deuterium targets. The experiment was performed using the upgraded 11 GeV beam of the Jefferson Lab electron accelerator, the Hall A Experimental Facility, and a newly designed cryogenic, high-pressure gas target system containing 3H and 3He cells. Electrons scattered o? the gas targets were detected in the two superconducting, high-resolution magnetic spectrometers of Hall A. The cross section data cover the four-momentum transfer squared range 2.0 < Q2 < 14.0 (GeV/c)2, and the Bjorken x scaling variable range 0.19 < x < 0.83. The experiment also measured, for calibration purposes, electron deep inelastic scattering off hydrogen and deuterium at selected kinematics.

Su, Tong↗
DOE OSTI2025

Integrating Crack Detection and Pipe Shape Optimization for Enhanced Sewage System Durability

Crack detection in underground reinforced concrete pipes has been essential in determining the state of stormwater infrastructure. Detection models have been implemented for detecting cracks and other defects in pipes using CCTV footage for stormwater drainage systems. In addition, Finite element models have been used to determine optimum shapes and pipe thickness for different boundary conditions such as header pipes in power plants. The concept of shape optimization emerges as a crucial factor in power plant design and operation, with the potential to maximize performance while minimizing the use of materials. Shape optimization not only enhances efficiency but also contributes to reducing the environmental footprint. This paper discusses the integration of both topics by using the cracks detected in underground pipes as boundary conditions for shape optimization of the pipes. A machine learning model has been developed which uses limited data for training and outlines the location of detected cracks. A shape optimization methodology is proposed in which ANSYS modules are used to analyze fluid flow and then optimize the shape of the pipe. The crack detection model developed has been applied to a crack detected in lab setting and machine learning model used has an accuracy of 98% using a random forest algorithm.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Infrared spectroscopic method for uranium isotopic analysis

A high performance infrared (HPIR) system was developed and demonstrated for the infrared absorption analysis of {sup 235}U and {sup 238}U isotopes in uranium hexafluoride gas samples. Sweeping the quantum cascade laser light source over the spectral range and sampling via a high-rate analog-to-digital converter provided 0.0005 cm-1 spectral resolution, which allowed for high-precision measurements of the isotopic peak shift. A data analysis method was developed using principal component analysis to predict the isotope weight % content of {sup 235}U. The HPIR precision, accuracy, and error were evaluated for a wide range of isotopic ratio samples (0.287 – 93.7 weight % {sup 235}U), and the results were compared to the International Target Values (ITVS) set forth by the International Atomic Energy Agency (IAEA) for non-destructive and destructive analytical techniques. The method meets or surpasses the IAEA ITVs for non-destructive analysis of samples with isotopic content of depleted to highly enriched. The results also demonstrated the capability of the HPIR system to correctly predict the {sup 235}U weight % content of a mislabeled sample whose isotopic distribution was validated by mass spectroscopic measurements. The HPIR measurement is nondestructive and, thus, allows for confirmatory analyses of the exact sample at a designated IAEA lab if higher-resolution or a certified analysis is needed. (author)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2020

Next Generation Noble Liquid Detectors

The research program here combines Generic and Directed R\&D for Liquid Noble Gas detectors for neutrino physics and dark matter. In neutrino physics and dark matter research, we are addressing some of the most fundamental questions in particle physics today by studying these tiny particles in the electron family. To study them, detectors must be large, high precision, and ideally have the ability to collect detailed information from both charge and light from neutrino and dark matter interactions. Precision detection combined with large scales can be challenging. Liquid Argon detectors mitigate some of these challenges given the nature of its interaction medium and it’s relatively inexpensive cost per ton. Significant progress has been made in the last 15 years in developing these kinds of detectors to be built and operated at large scales. However there are still challenges and new ideas in moving these detectors from ton scale to kiloton scale. To be able to get the most out of these kinds of detectors directed questions with respect to specific components running and colleting data in the detectors must be understood. As well, new ideas on how to best combine charge and light measurements may lead to new ways to learn new things with these detectors. Experimental test stands to conduct this work, as is done in this proposal, to accomplish both of these goals are critical to address both of these questions. In the test stand enabled under this grant at Yale University’s Wright lab, new developments in efficient small scale setups were developed, instrumentation developed for running and future experiments (the SBND experiment at Fermilab at present, the DUNE experiment in the future) were and will be tested, and new ideas for charge and light determination for new measurements are under study. The long term impact of this work in neutrino physcis is both for the short and long baseline programs originating at Fermi National Accelerator Laboratory. At long baselines, the US flagship DUNE experiment will measure neutrino properties through neutrino oscillations using a beam originating at Fermilab and a massive LArTPC detector sited about a mile underground at the Sanford Underground Research Facility in Lead, South Dakota. Studies here on the high voltage for thes detectors, and ongoing work on charge and light production and collection in the TPC are relevant for the design and data taking of this and other LArTPC detectors to enable them to best measure the neutrinos created at Fermilab that then pass through the detector in South Dakota. What we learn in these detectors may ultimately change the course of how we understand neutrino’s impact in the early universe. What we learn about how to improve this detection technology and develop new ideas in Lar detectors in general may impact both future neutrino experiments and dark matter experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

On extraction of twist-two Compton form factors from DVCS observables through harmonic analysis

We investigate the exercise of locally extracting the real and imaginary parts of the four twist-2 Compton form factors (CFFs) {$\mathcal{H,E,\tilde H,\tilde E}$} which arise in the deeply virtual Compton scattering (DVCS) process e + p → e + p + y. Neglecting dynamical higher-twist contributions, we find that there are a sufficient number of DVCS observables and degrees of freedom to extract all 8 leading quantities model-independently, exploiting the azimuthal dependence of the absolute cross sections across all possible beam and target polarizations at a common kinematical point in {Q, t,x b , y}. As an example, for typical JLab lab-frame kinematics, we simplify the reduced DVCS observables to their dominant terms, providing a sufficient number of equations for local determination of the twist-2 CFFs. We demonstrate the feasibility using harmonic fitting to both cross sections and beam spin asymmetries with both real and pseudo-data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Instabilities and phase transitions in architected metamaterials: a gradient-enhanced continuum approach

Architected metamaterials such as foams and lattices exhibit a wide range of properties governed by microstructural instabilities and emerging phase transitions. Their macroscopic response–including energy dissipation during impact, large recoverable deformations, morphing between configurations, and auxetic behavior–remains difficult to capture with conventional continuum models, which often rely on discrete approaches that limit scalability. In this work, we propose a nonlocal continuum formulation that captures both stable and unstable responses of elastic architected metamaterials. The framework extends anisotropic hyperelasticity by introducing nonlocal variables and internal length scales reflective of microstructural features. Local polyconvex free-energy models are systematically augmented with two families of non-(poly)convex energies, enabling both metastable and bistable responses. Implementation in a finite element framework enables solution using a hybrid monolithic–staggered strategy. Simulations capture densification fronts, forward and reverse transitions, hysteresis loops, imperfection sensitivity, and globally coordinated auxetic modes. Overall, this framework provides a robust foundation for accelerated modeling of instability-driven phenomena in architected metamaterials, while enabling extensions to anisotropic, dissipative, and active systems as well as integration with data-driven and machine learning approaches.

42 ENGINEERING↗
DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Performance Study of CXL Memory Topology

This paper presents a comprehensive evaluation of the performance impact of various Compute Express Link (CXL) memory topologies, with a particular emphasis on CXL switches, in the context of High- Performance Computing (HPC) and Large Language Model (LLM) inference workloads. Our study unveils significant performance variations across different topologies, demonstrating that certain configurations yield superior performance for specific workloads. These findings underscore the critical importance of tailored topol- ogy selection in optimizing system performance. Additionally, we address the inherent challenges associated with integrating CXL switches, including overhead considerations and routing complex- ities. Our research highlights the necessity for thorough evalua- tion methodologies to fully leverage CXL technology’s potential in contemporary computing environments. These insights provide valuable guidance for system architects and data center operators in designing and optimizing CXL-based infrastructures for diverse workload requirements.

CXL, memory, Artificial Intelligence (AI), HPC↗
DOE OSTI2025

The Baby Universe is Fine and the CFT Knows It: On Holography for Closed Universes

Big bang/big crunch closed universes can be realized in AdS/CFT, even though they lack asymptotically AdS boundaries. With enough bulk entanglement, the bulk Hilbert space of a closed universe can be holographically encoded in the CFT. We clarify the relation of this encoding to observer-clone proposals and refute recent arguments about the breakdown of semiclassical physics in such spaces. In the limit of no bulk entanglement, the holographic encoding breaks down. The oft-cited one-dimensional nature of the closed universe Hilbert space represents the limitation of the external (CFT) Hilbert space to access the quantum information in the closed universe, similar to the limitations imposed on observers outside a perfectly isolated quantum lab. We advocate that the CFT nevertheless continues to determine the physical properties of the closed universe in this regime, showing how to interpret this relationship in terms of a final state projection in the closed universe. We provide a dictionary between the final state wavefunction and CFT data. We propose a model of the emergence of an arrow of time in the universe with a given initial or final state projection. Finally, we show that the conventional EFT in the closed universe, without any projection, can be recovered as a maximally ignorant description of the final state. This conventional EFT is encoded in CFT data, and it can be probed by computing coarse-grained observables. We provide an example of one such observable. Taken together, these results amount to a clean bill of health for baby universes born of AdS/CFT.

FOS: Physical sciences↗
DOE OSTI2025

GeoBridge: Unearthing Insights from Connecting Communities to Geothermal Information and Opportunities: Preprint

Knowledge is essential for overcoming obstacles in the development and adoption of geothermal technologies, and the geothermal community is home to numerous tools, events and organizations dedicated to sharing knowledge. However, many of these tools can be difficult to find, their resources undiscoverable by search engines, available only to members, or hidden away behind pay walls (Weers et al., 2024). The Department of Energy's (DOE) GeoBridge was developed by the National Renewable Energy Laboratory (NREL) to help bridge gaps in information and connect the geothermal community to the resources it needs. Launched in October 2024, GeoBridge aspires to expand the pool of geothermal stakeholders by providing in-roads to geothermal information, tools, and community resources. It helps to make these resources available to the broader geothermal community as well as those looking to join, such as entrepreneurs or innovators in adjacent industries looking to expand into geothermal energy. This paper explores a post-launch analysis of GeoBridge including data from analytics, feedback from GeoBridge users, the geothermal community, and the GeoBridge Advisory Group as well as an analysis of efficacy of various promotions for GeoBridge.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Measuring recoiling nucleons from the nucleus with the future Electron Ion Collider

Short range correlated nucleon-nucleon (NN) pairs are an important part of the nuclear ground state. They are typically studied by scattering an electron from one nucleon in the pair and detecting its spectator correlated partner (spectator-nucleon tagging). The Electron Ion Collider (EIC)should be able to detect these nucleons, since they are boosted to high momentum in the lab frame by the momentum of the ion beam. To determine the feasibility of these studies with the planned EIC detector configuration, we have simulated quasi-elastic scattering for two electron and ion beam energy configurations: 5 GeV electrons and 41 GeV/A ions, and 10 GeV electrons and 110 GeV/A ions. We show that the knocked-out and recoiling nucleons can be detected over a wide range of initial nucleon momenta. We also show that these measurements can achieve much larger momentum transfers than current fixed target experiments. By detecting both low and high initial-momentum nucleons,the EIC will provide the data that should allow scientists to definitively show if the EMC effect and short-range correlation are connected, and to improve our understanding of color transparency

Hauenstein, F. (ORCID:0000000212652212)↗
DOE OSTI2020

Materials Development and Advanced Process Simulation for Additive Manufacturing with Fiber-Reinforced Thermoplastics (Final Technical Report)

DuPont, Local Motors, and the Composites Manufacturing and Simulation Center (CMSC) at Purdue worked together to advance process simulation capabilities for Additive Manufacturing (AM) and develop printable short fiber reinforced semi-crystalline engineering thermoplastics suitable for vehicle components. DuPont provided one candidate thermoplastic material, which was evaluated against the following three criteria: 1) Printability using extrusion additive manufacturing processes. 2) Suitability for vehicle applications based on mechanical coupon screening tests. 3) Predictive capability of process simulations. Indiana Manufacturing Institute provided modeling and simulation technology and access to Purdue’s lab scale AM printer. Local Motors provided mechanical property requirements and printed demonstration prototype components using their Thermwood Large Scale Additive Manufacturing (LSAM) printer. To meet the performance and cost requirements of the automotive industry, high fiber content semi-crystalline engineering materials are required. Printing experience and property data for a 50% by weight short glass fiber reinforced material were gathered. Further, material properties required for simulating the printing process with the glass-fiber reinforced material were characterized. Knowledge was acquired with regards to the processability and performance of this thermoplastic material. Predictions of part deformation and crystallinity level were verified against experimental observations. Any significant results other than verification of simulation, e.g., novel material identified here would be impactful.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Convolutional Neural Networks for Signal Detection

Currently, traditional methods such as short-term average/long-term average (STA/LTA) are used to detect arrivals in three-component seismic waveform data. Accurately establishing the identity and arrival of these waves is helpful in detecting and locating seismic events. Convolutional Neural Networks (CNNs) have been shown to significantly improve performance at local distances. This work will expand the use of CNNs to more remote distances and lower magnitudes. Sandia National Labs (SNL) will explore the advantages and limits of a particular approach and investigate requirements for expanding this technique to different types, distances, and magnitudes of events in the future. The team will describe detailed performance results of this method tuned on a curated dataset from Utah with its expert-defined arrival picks.

58 GEOSCIENCES↗
DOE OSTI2024

BENEFIT with Northeastern University: HVAC Hardware-in-the-Loop Experimental Testing of a Heat Pump and Air Conditioner

This dataset includes HVAC Hardware-in-the-Loop (HIL) experimental results for a single stage, SEER 16, HSPF 9.5, 3-ton single-speed air source heat pump with 15 kW of backup auxiliary heating tested in both cooling and heating mode, and a two stage, SEER 21, 2-ton central air conditioner tested in cooling mode for a set of outdoor temperatures and indoor setpoint temperatures. In addition to these tests, experimental tests focused on the operation of auxiliary heating for the heat pump for winter condition were also conducted. The laboratory experiments for transient testing of the heat pump and air conditioner were conducted using the two HIL systems in the Systems Performance Laboratory (SPL) at NREL’s Energy Systems Integration Facility (ESIF). Further information on laboratory design and capabilities of the SPL along with the architecture of HVAC HIL system can be found in: Sparn, B. F. 2018. Laboratory Resources and Techniques to Evaluate Smart Home Technology (No. NREL/CP-5500-71696). National Renewable Energy Laboratory (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy18osti/71696.pdf and the experimental setup and validation of HVAC HIL platform can be found in: Ramaraj, S. and Sparn, B. 2022. Validation of HVAC Hardware-In-the-Loop Simulation for Advanced Control Strategies in Smart Homes (No. NREL/CP-5500-82562). National Renewable Energy Lab (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy22osti/82562.pdf. These experimental results can be used to validate how we currently model the cycling behavior of heat pumps and air conditioners. Additionally, many demand response programs implement heat pump and air conditioner control by changing the thermostat set point – these data may also be used to verify our models for heat pump and air conditioner demand response control are implemented correctly. The Test_Matrix file describes all the indoor and outdoor test conditions for heat pump and air conditioner and the file names of data sets include information about the test conditions. A wide range of outdoor air temperatures were chosen to accommodate summer and winter conditions. In addition to operating the HVAC equipment with different outdoor temperatures, we also operate the system with different indoor temperature set points to represent different grid signals or different operating conditions. For cooling conditions, the baseline set point is 72°F. To represent Load Up signals, the setpoint is changed to 68°F. The Load Shed set point is 76°F. For heating conditions, the baseline set point was assumed to be 68°F. The Load add set point is 72°F and the Load shed set point is 64°F. The starting indoor temperature for cooling conditions was set ~2°F above the indoor setpoint temperature so that the equipment turned on quickly. Similarly, the initial indoor temperature was set ~2°F lower than setpoint for heating mode tests to ensure that heating began quickly. The return air temperature was assumed to be equal to the indoor setpoint temperature in all cases. The experimental data are sampled at 1-second intervals. The data from ecobee thermostat at 5-minute interval are resampled and added to the corresponding file. The content of each data set is as follows: • T_Return (C): Measured return air temperature [C] • T_Return_SP (C): Return air temperature setpoint from E+ model, sent to HIL [C] • T_Supply (C): Measured supply air temperature at evaporator outlet [C] • T_Outdoor (C): Measured outdoor air temperature [C] • T_Outdoor_SP (C): Outdoor air temperature setpoint from weather file, sent to HIL [C] • T_Indoor (C): Measured indoor air temperature [C] • T_Indoor_SP (C): Indoor air temperature setpoint from E+ model, sent to HIL [C] • Outdoor Unit Power (W): Measured power of the outdoor unit [W] • Indoor Unit Power (W): Measured power of the indoor unit [W] • Evaporator Airflow Rate (CFM): Measured evaporator or indoor unit airflow rate sent to E+ model [CFM] • Cooling/Heating Capacity (kW): Calculated cooling/heating capacity sent to E+ model [kW] • T_SP_Thermostat (C): Thermostat cooling/heating setpoint temperature [C] • T_Indoor_Thermostat (C): Thermostat indoor air temperature [C]

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Outdoor Test Bed Performance of a Power Line Sensor Using a Real-Time Event Simulator

This report summarizes the design and application of an outdoor power line sensor testbed (OPLST) with a real-time simulator and power meter to compare a potential transformer (PT) and current transformer (CT) versus an advanced line-post sensor. The OPLST was created to validate advanced medium-voltage (20/34.5 kV) outdoor power line sensors used in electrical distribution systems. Electrical utilities have installed metering and relay-protection transformers like PTs and CTs for several decades. The PTs/CTs are iron core measurement transformers based on the electromagnetic induction principle and provide reliable data in normal grid operation. However, new outdoor power line sensors (OPLS) using other technologies like voltage divider, Rogowski coil and optical principles have become available and may have favorable performance and costs compared to PTs/CTs. Therefore, the importance of testing these technologies with the PT/CT, to compare the measured phase voltage/current at different power grid scenarios is crucial to understand the performance of these new OPLS. For this study a G&W Electric Model CVS-36-O power line sensor was chosen as the OPLS to test with voltage/current signals. An OPAL-RT Technologies Model OP4510 real-time simulator and Schweitzer Engineering Laboratories Model SEL-735 power meter were installed with the 20/34.5 kV OPLST to compare the measured transient events collected from an advanced OPLS and the PT/CT. This system is installed at the Distributed Energy Communications and Control (DECC) lab, Oak Ridge National Laboratory (ORNL). The simulator generated different power grid scenarios (electrical faults, capacitor bank operation, service restoration, etc.), and its analog-output signals were connected to the voltage/current amplifiers that feed the 20/34.5 kV aerial cable loop through the PT/CT devices. Additional PT/CT devices were also wired with the medium voltage aerial cable loop to measure the phase current/voltage signals and server as references. After each test, common format for transient data exchange (COMTRADE) files were collected from the SEL-735 power meter and used to compare the performance of the OPLS with the PT/CT. The behavior of analog signals, harmonic components, total harmonic distortion and crest factors were assessed and found favorable for the G&W sensor as compared to the reference PT/CT.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A surprising proliferation of detwinning in β -tin at extreme loading rates

Integrating data from dynamic compression experiments of condensed matter across three national laboratories has led to insight and quantitative calibration of materials strength over decades of loading rate. For many materials, a single strength model (such as PTW) is sufficient to capture the flow-stress strain rate relationship which is monotonic. Here, we show here that β -tin, a tetragonal metal, exhibits dramatic deviations from this behavior. Naive fitting to a single PTW model is insufficient to capture the behavior; indeed, such resulting inferred flow stress versus strain exhibits a non-monotonic behavior. We suggest a resolution to this by proposing that in β -tin there are important Bauschinger effects arising from favorable conditions for twinning and detwinning. A simple yield surface model when paired with PTW hardening captures the experimental data.

36 MATERIALS SCIENCE↗