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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Electronic Probing and Manipulation of Low-Dimensional Magnets (Final Report)

We investigate the effects of reduced dimensionality of nanomagnets on the dynamical properties at low temperatures where quantum effects such as confinement, coherence, decoherence, and environmental coupling may be important. The experimental focus is on low-temperature transport measurements of such nanomagnets and cryogenic preamplification, followed by theoretical modeling of magnetic dynamics. In addition, we explore quantum effects in magnetic dynamics for their potential as quantum sensors of spin. We are particularly interested in the regime of strong environmental coupling, where novel phases of magnetic matter such as spin-boson may develop. The key new results are improved signal-to-noise ratio of magnetic tunneling junctions through low temperature preamplification that enables us to find the spin-dependent component of the current noise; a new powerful technique to simulate magnetic dynamics in the quantum coherent regime using the master equation formalism; and, finally, experimental hint of a novel state of matter in rare-earth doped permalloy ferromagnetic samples.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities

We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sensitivity to Dijet Resonances at Proton-Proton Colliders

A significant benchmark for discovery at a proton-proton collider is the sensitivity to a dijet resonance, X, the intermediate state of the s-channel process $pp \rightarrow X \rightarrow 2\mbox{ jets}$. To probe the highest resonance masses, hadron collider experiments have used the classic technique of searching for bumps in the mass spectrum of two individually resolved jets. In this Snowmass 2021 study, we explore the search sensitivity to multiple benchmark models of dijet resonances at current and future proton-proton colliders. We present the expected masses for $5\sigma$ discovery and 95% confidence level exclusion of diquarks, colorons, excited quarks, $W^{\prime}s$, $Z^{\prime}s$ and Randall-Sundrum gravitons, resulting from accumulation of integrated luminosities between 10 and $10^5$ fb$^{-1}$, at proton-proton colliders operating at energies $\sqrt{s}=$ 13, 14, 27, 75, 100, 150, 300 and 500 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Sensor Impact Evaluation at Different Sensor Locations in a Multi-zone Office Building

FY 2022 Q1 deliverables include the development of an emulator that can evaluate the sensor impacts at different sensor locations (i.e., thermostat locations) in a multizone office building. This report presents the detailed procedure of developing the emulator and using the emulator for preliminary sensor impact analysis. In designing new multizone buildings or retrofitting existing buildings, the room thermostat locations or subzoning design has been often determined by best practices without considering the effects of this design in terms of energy or thermal comfort. In subzoning design, the total number of thermostats is usually smaller than the total number of rooms. As a result, one thermostat in one room often controls the indoor temperature of several other adjacent rooms. For example, five zones might share one thermostat located in one of the zones. Because the demands for thermal load in different zones could be different for the multizone buildings, this subzoning design can compromise control performance and waste building energy consumption. Furthermore, for multizone buildings, subzoning could introduce thermal discomfort for zones. This issue has not be thoroughly investigated in simulation/field studies, and the US Department of Energy’s Oak Ridge National Laboratory explored the impacts of subzoning design through modeling and experimental study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Geometric Measures of Trustworthiness for Machine Learning Predictions

his report details the findings from the research and investigation of Geometric Measures of Trustworthiness for Machine Learning Predictions. We explored the trustworthiness of machine learning (ML) models’ predictions using geometric measures to quantify the similarity of a query point with the training data. Predictive uncertainty in ML can originate from at least three sources: (1) Model uncertainty, which represents the uncertainty in model form (e.g. decision tree, vs neural network) and estimating the model parameters from the training data, (2) Data uncertainty, which represents the natural complexities of the data such as class overlap and inherent noise, and (3) Distributional uncertainty, which represents the mismatch between the training and operational distributions. The proposed measures focus on measuring and explaining the data and distributional uncertainties by measuring the relationships of operational data with the training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

2023 Reftek Colt Seismometer Exploratory Evaluation

Sandia National Laboratories has tested and evaluated three Colt broadband seismometers designed and manufactured by Reftek. The purpose of this seismometer evaluation is to measure performance characteristics in areas such as power consumption, sensitivity, frequency response, full scale, self-noise, dynamic range, calibration system response, and passband. The Colt model of sensors are being evaluated to explore the potential for a future seismometer Type Approval process in the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT).

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

2023 T120 Horizon and Slim Posthole Seismometer Exploratory Evaluation

Sandia National Laboratories has tested and evaluated a suite of four T120 broadband seismometers designed and manufactured by Nanometrics. Specifically, two T120 Horizon V2 sensors, one T120 Horizon V1 sensor and one T120 Slim Posthole (PH) sensor were evaluated. The purpose of this seismometer evaluation is to measure performance characteristics in areas such as power consumption, sensitivity, frequency response, full scale, self-noise, dynamic range, calibration system response, and passband. The T120 model of sensors are being evaluated to explore the potential for a future seismometer Type Approval process in the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT).

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Performance Optimization of the IOTA Duoplasmatron Proton Source

We present results from online optimization studies of a duoplasmatron ion source designed to produce 50~keV protons for acceleration to 2.5~MeV and subsequent injection into the Integrable Optics Test Accelerator (IOTA) at Fermilab. Using a Bayesian exploration technique, we developed multi-parameter models of the source s proton current and employed these models to optimize its performance. Depending on the spectrometer configuration used to isolate the proton beam and the chosen optimization objective, we identified three candidate operating points, achieving normalized 50~\% emittances between 0.57 and 1.3~\textmu m and a maximum proton current of $14.5 \pm 0.6$~mA.

Banerjee, Nilanjan [Fermilab] (ORCID:0000000344660↗
DOE OSTI2022

On a Unified Core Characterization Methodology to Support the Systematic Assessment of Rare Earth Elements and Critical Minerals Bearing Unconventional Carbon Ores and Sedimentary Strata

A significant gap exists in our understanding and ability to predict the spatial occurrence and extent of rare earth elements (REE) and certain critical minerals (CM) in sedimentary strata. This is largely due to a lack of existing, systematic, and well-distributed REE and CM samples and analyses in United States sedimentary basins. In addition, the type of sampling and characterization performed to date has generally lacked the resolution and approach required to constrain geologic and geographic heterogeneities typical of subsurface, mineral resources. Here, we describe a robust and systematic method for collecting core scale characterization data that can be applied to studies on the contextual and spatial attributes, the geologic history, and lithostratigraphy of sedimentary basins. The methods were developed using drilled cores from coal bearing sedimentary strata in the Powder River Basin, Wyoming (PRB). The goal of this effort is to create a unified core characterization methodology to guide systematic collection of key data to achieve a foundation of spatially and geologically constrained REEs and CMs. This guidance covers a range of measurement types and methods that are each useful either individually or in combination to support characterization and delineation of REE and CM occurrences. The methods herein, whether used in part or in full, establish a framework to guide consistent acquisition of geological, geochemical, and geospatial datasets that are key to assessing and validating REE and CM occurrences from geologic sources to support future exploration, assessment, and techno-economic related models and analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

VERITAS Observations Contemporaneous with the LHAASO Detection of NGC 4278

Significant gamma-ray emission between 1 TeV and 20 TeV from a point source, 1LHAASO J1219+2915, consistent with the location of the LINER/LLAGN galaxy NGC 4278 was recently reported by the Large High Altitude Air Shower Observatory (LHAASO) collaboration. These data were later split into active and quasi-quiet states, with most of the LHAASO significance coming from the active state (MJD 59449-59589). Subsequent analysis of Fermi-LAT and Swift-XRT observations have been used to explore the double-peaked broadband emission. Models of the spectral energy distribution (SED) are currently unconstrained due to the lack of contemporaneous multiwavelength data at either peak. Here, we report serendipitous observations of NGC 4278 with the Very Energetic Radiation Imaging Telescope Array System (VERITAS), made possible by the contemporaneous observations of the nearby blazars 1ES 1218+304, 1ES 1215+303, and W Comae, each of which are located within 2° of NGC 4278. VERITAS did not detect any gamma-ray emission, and a flux upper limit was calculated. The flux upper limits constrain the photon spectrum of the quasi-quiet period, and together with Fermi-LAT, indicate a peak in the SED between 100 GeV and 2 TeV. We present an interpretation of the broadband SED that is based on acceleration of protons in the corona of the active galactic nuclei, followed by p–γ interactions and optically thin γ-ray emission. Within this framework, the implied neutrino signal is slightly below the current sensitivity of IceCube.

Archer, A. [DePauw University, Greencastle, IN (Un↗
DOE OSTI2021

Utilizing Additive Manufacturing to Create Prototype and Functional Beamline Instrumentation and Support Components

The world of beamline science is often fast-paced and dynamic. One of the major challenges in this environment is to be able to design, manufacture and then implement new items for use on the beamlines in a fast and accurate manner. Many times, this involves iterating the design to address unknown or new variables which were not pre- sent at the beginning of the project planning task. Through the use of additive manufacturing, I have been able to support the user programs of various (APS) Ad- vanced Photon Source beamlines* across multiple scien- tific disciplines. I will provide a few detailed examples of Items that were created for specific beamline applications and discuss what benefits they provided to the pertinent project. I will also talk about why choosing consumer- level printer options to produce the parts has been the direction I went and the pros and cons of this decision. Pri marily, this choice allowed for quicker turnaround times and the ability to make more frequent changes in an efficient manner. Currently, we are utilizing only the fused deposition modelling (FDM) type printers but I am exploring the addition of UV-activated resin printing, exotic materials that can be utilized using the current toolset, and the possibility of commercial metal printing systems. This technology has been a game-changer for the implementation of new support items and instrumentation over the last couple of years for the different disciplines I am sup- porting. I will discuss how the roadmap ahead and what the evolving technologies could potentially allow us to do.

Jensen, D.↗
DOE OSTI2025

Automating Bug Report Classification with Few Shot Learning

Orthogonal defect classification (ODC) is a method used to categorize software defects, providing valuable insights into the development process. This study focuses on automating the classification of software bug reports into different ODC defect types using few shot learning, a machine learning approach that requires minimal labeled data. Previous research has manually classified bug reports or used traditional machine learning algorithms like linear support vector machine, achieving limited success. Our approach uses few shot learning to improve classification accuracy and efficiency. The results show a harmonic mean of recall and precision (i.e., the F1 score) of around 0.6 which is a performance improvement over previous methods. The results highlight the potential benefit of few shot learning techniques and their application in enhancing the safety and reliability of nuclear digital instrumentation and control (DI&C) systems. Future work will explore incorporating advanced techniques to supplement the model's training data and achieve better results.

42 - ENGINEERING↗
DOE OSTI2024

A Model for Hybrid Systems for Production Cost Modeling Studies Considering Ancillary Services: Preprint

This paper introduces a model for simulating hybrid plants participating in energy and ancillary services for bulk power system studies. The model considers a hybrid plant comprised of a renewable energy source, a thermal power unit, a storage unit, fixed power loads, or any combination of these technologies. The model focuses on Production Cost Modeling (PCM) studies under the assumption of centralized dispatch. We present an example case study to illustrate the use of the model in a single-stage production cost model similar to those conducted by planning agencies. We explore the allocation of behind-themeter ancillary services products and total energy participation to hybrid plant sub-assets and the resulting impacts on the system's ancillary service allocation. The model is implemented and simulated in a unit commitment problem in the RTS test system, which was modified to include a hybrid plant asset.

ancillary services↗
DOE OSTI2024

Carbon management technology pathways for reaching a U.S. Economy-Wide net-Zero emissions goal

The Carbon Management Study Group of the 37 th Energy Modeling Forum (EMF 37) designed seven scenarios to explore the role of three potentially key technology suites – point source carbon dioxide capture and storage (PSCCS), direct air capture of carbon dioxide (DACCS), and hydrogen systems (H 2 ) – in shaping the broader technology pathways to reaching net-zero carbon dioxide (CO 2 ) emissions in United States by 2050. Each scenario was run by up to 13 models participating in the EMF 37 study. Results show that carbon dioxide removal technologies were consistently a major part of successful pathways to net-zero U.S. CO 2 emissions in 2050. Achieving this net-zero CO 2 goal without any form of carbon dioxide capture and storage was found to be impossible for most models; some models also found it impossible to reach net-zero without DACCS. The marginal cost of achieving net-zero CO 2 emissions in 2050 was between two and 10 times higher without PSCCS and/or DACCS available. The carbon price at which DACCS was deployed as a backstop technology depended upon the assumed cost at which DACCS was available at scale. Carbon prices were between $\$$250 and $\$$500 per ton CO 2 when DACCS deployed as a backstop. The average CO 2 capture rate across all models in 2050 in the central net-zero scenario was 1.3 GtCO 2 /year, which implies a substantial upscaling of capacity to move and store CO 2 . Finally, hydrogen sensitivity scenarios showed that H 2 typically constituted a relatively small share of the overall U.S. energy system; however, H 2 deployed in applications that are considered hard to decarbonize, facilitating transition towards net-zero emissions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model

Accurate early season predictions of crop yield at the within-field scale can be used to address a range of crop production, management, and precision agricultural challenges. While the remote sensing of within-field insights has been a research goal for many years, it is only recently that observations with the required spatio-temporal resolutions, together with efficient assimilation methods to integrate these into modeling frameworks, have become available to advance yield prediction efforts. Here we explore a yield prediction approach that combines daily high-resolution CubeSat imagery with the APSIM crop model. The approach employs APSIM to train a linear regression that relates simulated yield to simulated leaf area index (LAI). That relationship is then used to identify the optimal regression date at which the LAI provides the best prediction of yield: in this case, approximately 14 weeks prior to harvest. Instead of applying the regression on satellite imagery that is coincident, or closest to, the regression date, our method implements a particle filter that integrates CubeSat-based LAI into APSIM to provide end-of-season high-resolution (3 m) yield maps weeks before the optimal regression date. The approach is demonstrated on a rainfed maize field located in Nebraska, USA, where suitable collections of both imagery and in-situ data were available for assessment. The procedure does not require in-field data to calibrate the regression model, with results showing that even with a single assimilation step, it is possible to provide yield estimates with good accuracy up to 21 days before the optimal regression date. Yield spatial variability was reproduced reasonably well, with a strong correlation to independently collected measurements (R 2 = 0.73 and rRMSE = 12%). When the field averaged yield was compared, our approach reduced yield prediction error from 1 Mg/ha (control case based on a calibrated APSIM model), to 0.5 Mg/ha (using satellite imagery alone), and then to 0.2 Mg/ha (results with assimilation up to three weeks prior to the optimal regression date). Such a capacity to provide spatially explicit yield predictions early in the season has considerable potential to enhance digital agricultural goals and improve end-of-season yield predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Robustness of deep learning algorithms in astronomy -- galaxy morphology studies

Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Temperature distribution in a laser-heated diamond anvil cell as described by finite element analysis

Finite element analysis (FEA) is a powerful tool for numerically solving partial differential equations over complex geometries and is thus useful for analyzing heat transport in laser-heated diamond anvil cell (LHDAC) experiments. Our models expand on previously published simulations by calculating the volume-averaged temperatures of both the sample and insulation/pressure media under steady-state heating to determine the thermal pressure of the hot sample. Our goal is to produce an accurate relationship between the measured surface temperature of the absorbing sample and the temperature of the transparent insulating media, which is used to determine thermal pressure but susceptible to steep temperature gradients. We find that in doing so, our FEA models of temperature within the pressure/insulation media can differ from simplified estimates of temperature gradients by more than a factor of 2. We also explore temperature-dependent and temperature-independent thermal conductivity models and find that the volume-averaged temperatures differ by up to a factor of 1.3, forcing the predicted thermal pressures determined to also differ by up to a factor of 1.5 at a temperature of 2000 K at 50 GPa for neon. Higher temperatures exacerbate this difference. We also find that unintentional asymmetric sample insertion and sample heating, which are common in LHDAC experiments, do not have a first-order effect on volume-averaged temperatures. The FEA models, available in both Python and FlexPDE, are versatile across different sample geometries, materials, and heat source laser shapes.

Farah, Frederick↗
DOE OSTI2021

Singling out modified gravity parameters and data sets reveals a dichotomy between Planck and lensing

ABSTRACT An important route to testing general relativity (GR) at cosmological scales is usually done by constraining modified gravity (MG) parameters added to the Einstein perturbed equations. Most studies have analysed so far constraints on pairs of MG parameters, but here, we explore constraints on one parameter at a time while fixing the other at its GR value. This allows us to analyse various models while benefiting from a stronger constraining power from the data. We also explore which specific data sets are in tension with GR. We find that models with (μ = 1, η) and (μ, η = 1) exhibit a 3.9σ and 3.8σ departure from GR when using Planck18 + Supernovae type Ia (SNe) + Baryon acoustic oscillation (BAO), while (μ, η) shows a tension of 3.4σ. We find no tension with GR for models with the MG parameter Σ fixed to its GR value. Using a Bayesian model selection analysis, we find that some one-parameter MG models are moderately favoured over ΛCDM when using all data set combinations except Planck cosmic microwave background lensing and dark energy survey data. Namely, Planck18 shows a moderate tension with GR that only increases when adding any combination of redshift space distortion, SNe, or BAO. However, adding lensing diminishes or removes these tensions, which can be attributed to the ability of lensing in constraining the MG parameter Σ. The two overall groups of data sets are found to have a dichotomy when performing consistency tests with GR, which may be due to systematic effects, lack of constraining power, or modelling. These findings warrant further investigation using more precise data from ongoing and future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗