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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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NASA NTRS2009

Antimatter and Dark Matter Search in Space: BESS-Polar Results

The apex of the Balloon-borne Experiment with a Superconducting Spectrometer program was reached with the Antarctic flight of BESS-Polar II, during the 2007-2008 Austral Summer, that obtained 24.5 days of data on over 4.7 billion cosmic-ray events. The US-Japan BESS Collaboration uses elementary particle measurements to study the early Universe and provides fundamental data on the spectra of light cosmic-ray elements and isotopes. BESS measures the energy spectra of cosmic-ray antiprotons to investigate signatures of possible exotic sources, such as dark-matter candidates, and searches for heavier anti-nuclei that might reach Earth from antimatter domains formed during symmetry breaking processes in the early Universe. Since 1993, BESS has carried out eleven high-latitude balloon flights, two of long duration, that together have defined the study of antiprotons below about 4 GeV, provided standard references for light element and isotope spectra, and set the most sensitive limits on the existence of anti-deuterons and anti-helium, The BESS-Polar II flight took place at Solar Minimum, when the sensitivity of the low-energy antiproton measurements to a primary source is greatest. The rich BESS-Polar II dataset more than doubles the combined data from all earlier BESS flights and has 10-20 times the statistics of BESS data from the previous Solar Minimum. Here, we summarize the scientific results of BESS program, focusing on the results obtained using data from the long-duration flights of BESS-Polar I (2004) and BESS-Polar II.

Mitchell, John W.↗

NASA NTRS2014

Investigation of Martian Aqueous Processes Using Multiple Alpha Particle X-ray Spectrometer (APXS) Datasets

The APXS instruments flown on the Mars Exploration Rovers (MER) Spirit and Opportunity and the Mars Science Laboratory (MSL) Curiosity were based on the same fundamental design. The calibration effort of the MSL APXS used the same reference standards analyzed in the MER calibration which ensures that data produced by all three instruments provide the same compositional results for the same sample. This cross-calibration effort is unprecedented and allows direct comparisons and contrasts of samples analyzed at Gusev Crater by Spirit, Meridiani Planum by Opportunity, and Gale Crater by Curiosity.

Yen, A. S.↗

DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗

DOE OSTI2020

Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review

Gas chromatography (GC) is undoubtedly the analytical technique of choice for analyzing the composition of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC×GC) has evolved, fuel analysis has often been highlighted in scientific reports, as their complexity allows for illustration of the impressive peak capacity gains afforded by GC×GC. Indeed, several research groups in recent years have applied GC×GC and chemometrics to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC×GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC×GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC×GC analyses of fuels for compliance and forensic applications, in an effort to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC×GC technology (column stationary phases and modulators) and several chemometric techniques will be provided. More specifically, chemometric techniques will be broken down into (1) signal pre-processing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed, with particular emphasis on the demonstrated success of the employed chemometric techniques. Overall, this review will hopefully make 1D-GC and GC×GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Factors That Influence Variability in Stress-Drop Measurements Using Spectral Decomposition and Spectral-Ratio Methods for the 2019 Ridgecrest Earthquake Sequence

Stress drop is a fundamental parameter related to earthquake source physics, but is hard to measure accurately. To better understand how different factors influence stress-drop measurements, we compare two different methods using the Ridgecrest stress-drop validation data set: spectral decomposition (SD) and spectral ratio (SR), each with different processing options. Here, we also examine the influence of spectral complexity on source parameter measurement. Applying the SD method, we find that frequency bandwidth and time-window length could influence spectral magnitude calibration, while depth-dependent attenuation is important to correctly map stress-drop variations. For the SR method, we find that the selected source model has limited influence on the measurements; however, the Boatwright model tends to produce smaller standard deviation and larger magnitude dependence than the Brune model. Variance reduction threshold, frequency bandwidth, and time-window length, if chosen within an appropriate parameter range, have limited influence on source parameter measurement. For both methods, wave type, attenuation correction, and spectral complexity strongly influence the result. The scale factor that quantifies the magnitude dependence of stress drop show large variations with different processing options, and earthquakes with complex source spectra deviating from the Brune-type source models tend to have larger scale factor than earthquakes without complexity. Based on these detailed comparisons, we make a few specific suggestions for data processing workflows that could help future studies of source parameters and interpretations.

58 GEOSCIENCES↗

DOE OSTI2022

Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

Here, we use Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create multiple models with various combinations of sampled weights, which can be used to obtain ensemble predictions. The average of such an ensemble can be regarded as the 'mean estimation', whereas its standard deviation can be used to construct 'confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks. We utilize representative neural-network-based function approximation tasks for the following cases: (i) a two-dimensional circular-cylinder wake; (ii) the DayMET dataset (maximum daily temperature in North America); (iii) a three-dimensional square-cylinder wake; and (iv) urban flow, to assess the generalizability of the present idea for a wide range of complex datasets. SWAG-based UQ can be applied regardless of the network architecture, and therefore, we demonstrate the applicability of the method for two types of neural networks: (i) global field reconstruction from sparse sensors by combining convolutional neural network (CNN) and multi-layer perceptron (MLP); and (ii) far-field state estimation from sectional data with two-dimensional CNN. We find that SWAG can obtain physically-interpretable confidence-interval estimates from the perspective of epistemic uncertainty. This capability supports its use for a wide range of problems in science and engineering.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Search for supersymmetry in proton-proton collisions at $\sqrt{s} =$ 13 TeV in events with high-momentum Z bosons and missing transverse momentum

A search for new physics in events with two highly Lorentz-boosted Z bosons and large missing transverse momentum is presented. The analyzed proton-proton collision data, corresponding to an integrated luminosity of 137 fb$^{−1}$, were recorded at $ \sqrt{s} $ = 13 TeV by the CMS experiment at the CERN LHC. The search utilizes the substructure of jets with large radius to identify quark pairs from Z boson decays. Backgrounds from standard model processes are suppressed by requirements on the jet mass and the missing transverse momentum. No significant excess in the event yield is observed beyond the number of background events expected from the standard model. For a simplified supersymmetric model in which the Z bosons arise from the decay of gluinos, an exclusion limit of 1920 GeV on the gluino mass is set at 95% confidence level. This is the first search for beyond-standard-model production of pairs of boosted Z bosons plus large missing transverse momentum.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Search for a new heavy resonance decaying to a top quark and a neutral scalar boson in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A first search at the LHC for a new heavy resonance decaying to a top quark and a neutral scalar boson ϕ in the fully hadronic final state is presented, where the ϕ boson is identified by its decay into a bottom quark-antiquark pair. The search is focused on final states in which the decay products of the highly Lorentz boosted top quark and ϕ boson are each reconstructed as a single, large-radius jet with distinct substructure. The analysis is performed using proton-proton collision data at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb −1 , recorded with the CMS detector at the LHC in 2016–2018. The single production of a vector-like top quark, T′, is used as a benchmark model for the signal process. The results of this search are combined with those of a previous CMS search in which semileptonic decays of the top quark were used. No significant excess of data is observed with respect to the background prediction. For the case where the neutral scalar is a standard model Higgs boson and the T′ quark width is 5% of its mass, T′ quark masses between 0.85 and 1.3 TeV are excluded at 95% confidence level and the most stringent limits to date are set for masses above 2 TeV . For other ϕ boson masses, upper limits as low as 0.1 fb are set on the product of the T′ quark production cross section and branching fraction for its decay to a top quark and a ϕ boson.

Boosted↗

DOE OSTI2024

National-scale remotely sensed lake trophic state from 1984 through 2020

Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Hadronic structure in high-energy collisions

Here, Parton distribution functions (PDFs) describe the structure of hadrons as composed of quarks and gluons. They are needed to make predictions for short-distance processes in high-energy collisions and are determined by fitting to cross-section data. Definitions of the PDFs and their relations to high-energy cross sections are reviewed. The focus is on the PDFs in protons, but PDFs in nuclei are also discussed. The standard statistical treatment needed to fit the PDFs to data using the Hessian method is reviewed in some detail. Tests are discussed that critically examine whether the needed assumptions are indeed valid. Also presented are some ideas of what one can do in case tests indicate that the assumptions fail.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Search for a Neutral Gauge Boson with Nonuniversal Fermion Couplings in Vector Boson Fusion Processes in Proton-Proton Collisions at $\sqrt{𝑠}$ = 13 TeV

The first search for a heavy neutral spin-1 gauge boson (𝑍′) with nonuniversal fermion couplings produced via vector boson fusion processes and decaying to tau leptons or 𝑊 bosons is presented. The analysis is performed using LHC data at $\sqrt{𝑠}$ = 13 TeV, collected from 2016 to 2018 with the CMS experiment and corresponding to an integrated luminosity of 138 fb −1 . The data are consistent with the standard model predictions. Upper limits are set on the product of the cross section for production of the 𝑍′ boson and its branching fraction to 𝜏⁢𝜏 or 𝑊⁢𝑊. The presence of a 𝑍′ boson decaying to 𝜏 + ⁢𝜏 − (𝑊 + ⁢𝑊 − ) is excluded for masses up to 2.45(1.60) TeV, depending on the 𝑍′ boson coupling to standard model weak bosons, and assuming a 𝑍′→𝜏 + ⁢𝜏 − (𝑊 + ⁢𝑊 − ) branching fraction of 50%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

14 SOLAR ENERGY↗

DOE OSTI2021

Development of a Lower Cost, High Volume, Commercially Available, Precursor for Lower Cost Carbon Fiber for Automotive and Wind Blade Applications

Essentially all carbon fibers used in structural applications are presently manufactured from solution spun polyacrylonitrile (PAN) precursor. Solution spinning is accomplished at relatively low temperatures using very large amounts of environmentally unfriendly solvents that impart major impact to line speed and costs involved with handling and reclaiming the solvents and associated “waste streams” required in spinning and extracting those solvents. Dralon and others have demonstrated the dry spinning of PAN in the production of some acrylic fibers for textile applications, but to this point it has not been demonstrated in the production of carbon fibers. In dry spinning, the PAN polymer is also dissolved into similar solvents, although typically less solvent is utilized to maintain a somewhat higher solids content of 20-50%. The use of lower overall solvent quantities and elimination of the need for solvent/water separation simplifies and reduces the cost of solvent recovery operations. Other portions of the precursor production and carbon fiber manufacturing processes are quite similar. The work described in this report demonstrated that dry spun acrylic fiber can be an attractive candidate for carbon fiber precursor of potential interest to applications looking to capture the specific stiffness (stiffness per unit mass) and specific strength (strength per unit mass) of carbon fiber when produced at a discount relative to commercial carbon fibers in the market today. Dralon has determined that the dry spinning process utilized for producing fibers used as precursors in this project requires 27% less energy versus producing comparable fibers via the industry standard wet spinning process. Identification of carbon fiber manufacturers, as well as applications development and targeted marketing, will be required to achieve significant in-roads into mission areas of interest to DOE, such as automotive applications and wind turbine blades. In support of that activity, initial data produced in this project indicate the innovative approach as described in this report can meet at least the baseline property requirements identified for these applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

The REDTOP experiment: Rare $\eta/\eta^{\prime}$ Decays To Probe New Physics

The $\eta$ and $\eta^{\prime}$ mesons are nearly unique in the particle universe since they are almost Goldstone bosons and the dynamics of their decays are strongly constrained. The integrated $\eta$-meson samples collected in earlier experiments amount to $\sim10^{9}$ events. A new experiment, REDTOP (Rare Eta Decays To Probe New Physics), is being proposed, with the intent of collecting a data sample of order 10$^{14}$ $\eta$ (10$^{12}$ $\eta^{\prime}$) for studying very rare decays. Such statistics are sufficient for investigating several symmetry violations, and for searching for particles and fields beyond the Standard Model. In this work we present several studies evaluating REDTOP sensitivity to processes that couple the Standard Model to New Physics through all four of the so-called \emph{portals}: the Vector, the Scalar, the Axion and the Heavy Lepton portal. The sensitivity of the experiment is also adequate for probing several conservation laws, in particular $CP$, $T$ and Lepton Universality, and for the determination of the $\eta$ form factors, which is crucial for the interpretation of the recent measurement of muon $g-2$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

A Guide for Improved Resource Adequacy Assessments in Evolving Power Systems: Institutional and Technical Dimensions

This paper identifies and evaluates issues in traditional resource adequacy (RA) assessment practices, and how adjusting these practices may affect and depend on existing institutional arrangements for planning and procurement. The paper proposes a technical-institutional roadmap that would allow regulators in vertically-integrated jurisdictions and system planners and operators in restructured jurisdictions to revise RA practices across a range of components. First, we compile a critical review of current RA assessment practices based on (1) interviews with RA practitioners and (2) a review of recent technical literature. We find that (i) RA may need to expand beyond capacity adequacy to ensure energy adequacy – relevant for energy-limited resources such as storage – and potentially some form of ancillary service adequacy (e.g. enough ramping-up and ramping-down capability in the system); (ii) chronological hourly simulations for all hours in the year are the current best practice; (iii) metrics and models used do not reflect economic criteria in system operation and loss of load; and (iv) there is a need to improve representation of weather dependencies and weather data. Second, we review planning and RA reports for several private and public entities that plan generation and/or transmission infrastructure in the continental U.S. to look for existing practices involving resilience assessments. We find no systematic treatment of the costs of extreme weather and other hazards, the benefits of resilience, and resilience metrics in planning analyses and no systematic treatment of resilience metrics, methods, and outcomes for resource adequacy purposes. Third, we create a technical framework for probabilistic RA assessment and use it to study how key choices about how to model power system operations affect the values that are obtained for RA metrics. We find that (i) non-economic dispatch schemes that ignore economic objectives can lead to accurate RA assessments when coordinated with detailed operational strategies; (ii) multi-year data is critical to capture a wide variety of system conditions; (iii) not incorporating transmission limits into RA assessment could lead to substantial underestimation of traditional “expected value” RA metrics; and (iv) new RA metrics that capture event-specific shortfall characteristics should be used as supplements to traditional metrics. Finally, we examine RA assessments and use this information to propose a guide of evolving industry standards for resource adequacy assessments in resource planning and transmission planning. We report minimum, best, and frontier practices for temporal resolution of assessments, metrics and targets, weather data, load forecasting, characterization of variable renewable resources, characterization of transmission and market transactions, RA modeling and integration with planning processes, and capacity accreditation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Bridging Cloud and Edge Computing at NREL Using CONNECT: Cloud Optimized Networking for Next-Gen Edge Computing Technologies [Slides]

CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

PyPop: a mature open-source software pipeline for population genomics

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations.

59 BASIC BIOLOGICAL SCIENCES↗