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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

L-BFGS Class Implementation in C++

This report presents a header-only C++ class implementation of the Limited-memory BroydenFletcher-Goldfarb-Shanno (L-BFGS) algorithm. The L-BFGS method is a general purpose quasi-Netwon optimization method that builds an approximation of the descent direction from consecutive iterate and gradient vectors. The limited-memory aspect stems from the replacement of the N × N approximation matrix of the original BFGS method with M vectors of length N. An example usage of the class is included along with the reference source code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Z Line VISAR Analysis with the LineVISAR SMASH Class

The Z line VISAR system (ZLV) is a spatially-resolved velocimeter that measures surface velocities in high-energy density experiments on the Z Pulsed Power Facility to facilitate the investigation of fusion, power flow, and dynamic material physics. The data measured in these experiments are analyzed with the LineVISAR SMASH class, a MATLAB software suite that provides tools for data importation, streak image correction, spatiotemporal registration, wrapped phase computation, phase unwrapping, shock handling, and velocity calculation. This report overviews the LineVISAR class, discusses its use, provides example implementations, and supplies analysis specifics not typically recorded in journal publications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

MW-Class SOFC Pilot System Development

The overall objective of this project is to develop a low-cost, efficient, and reliable MWe-class Solid Oxide Fuel Cell (SOFC) power system towards commercial deployment in natural-gas fueled distributed generation applications. This report details the successful completion of all project objectives. The main goals of the project were to develop the conceptual design of a MWe-class SOFC power system, and to complete a techno-economic analysis (TEA) to determine system costs at low-volume production levels of >100MW per year. The combination of the BoP equipment cost and SOFC stack module factory cost provided the net fabrication cost of the selected 1MW SOFC system configuration. The results of the study showed that the cost target of less than $6000/kW is achievable for a FOAK 1MW SOFC system, exclusive of the first-time engineering cost. Also, the project included an exploratory analysis of the factory cost of next generation stack technology based on the Compact Solid-oxide Architecture (CSA) design at a high annual manufacturing rate of one-GW produced in a giga-factory. The BoP capital cost for one giga-watt power production was estimated using learning-curve analysis based on the historical manufacturing cost data related to similar high temperature Molten Carbonate fuel cell systems built by FuelCell Energy (FCE). The giga-factory manufactured SOFC cost estimate combined with the BoP cost at high production rates led to the estimation of entire system capital cost. Ultimately, a Cost of Electricity (COE) analysis was performed including natural gas fuel prices, installation and O&M costs, and stack replacement cost. The results of the techno-economic analysis were summarized by parametric estimation of COE for 1MW SOFC NOAK (Nth-of-a-Kind) system with natural gas prices as a variable parameter.

08 HYDROGEN↗
DOE OSTI2026

Illinois Storage Corridor, CarbonSAFE Phase III: UIC Class VI Permitting Plan

The Illinois Storage Corridor (ISC) project evaluated two distinct sites to determine the feasibility of commercial-scale CO₂ storage at each. The project leveraged the region's exceptional geological characteristics, particularly the well-characterized Cambro-Ordovician Storage Complex, to enable permanent geological storage of more than 50 million tonnes of CO₂ over 30 years. The two storage sites are located at One Earth Energy (OEE) facility in northcentral Illinois and Prairie State Generating Company (PSGC) in southcentral Illinois. Once operational, these facilities will combine to capture and store more than 6.5 million tonnes of CO₂ per year, positioning the ISC among the largest carbon storage regions globally. Preliminary homogeneous dynamic modeling based on regional and site-specific reservoir characteristics indicates promising injection capabilities at both locations. For the OEE site, modeling predicts a maximum allowable injection rate of 3.7 MTPA, with a baseline scenario of 1.7 MTPA over 30 years producing a CO₂ plume radius of 1.4 miles at end of injection. For the PSGC site, incorporating recent well data, modeling indicates a single-well maximum injection rate of 2.1 MTPA, with a plume radius of 4.2 miles at end of injection for the 60 MT over 30 years scenario. The primary objective of this CarbonSAFE Phase III project is to develop and submit Class VI Underground Injection Control (UIC) permit applications to the U.S. Environmental Protection Agency Region 5. The permitting plan outlines comprehensive site characterization, Area of Review delineation, monitoring programs, well construction designs, financial responsibility provisions, and post-injection site care procedures necessary to demonstrate safe, permanent CO₂ storage protective of underground sources of drinking water. Three UIC Class VI permit applications for the OEE site were submitted to EPA in October 2022 and are progressing through technical review, with final permit decision projected by June 2026. Through five rounds of Requests for Additional Information and responses, the applications have been refined to address computational modeling, area of review delineation, well integrity, monitoring protocols, and financial assurance requirements. For the PSGC site, finalized characterization and permitting documentation was delivered directly to the facility in July 2023 due to business constraints precluding formal federal regulatory submission. This comprehensive permitting effort builds upon extensive prior subsurface evaluations and demonstration projects that have confirmed the feasibility of widespread commercial-scale carbon storage in the region.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Diversity in the Common Fold: Structural Insights into Class D β-Lactamases from Gram-Negative Pathogens

Class D β-lactamases (DBLs) represent a major threat to antibiotic efficacy by hydrolyzing β-lactam drugs, including last-resort carbapenems, thereby driving antimicrobial resistance in Gram-negative bacteria. The enzymes share a structurally conserved two-domain α/β architecture with seven active-site motifs and three flexible extended loops (the P-loop, Ω-loop, and newly designated B-loop) that surround the active site. While each of these loops is known to influence enzyme function, their coordinated roles have not been fully elucidated. To investigate the significance of their interplay, we compared the sequences and crystal structures of 40 DBLs from clinically relevant Gram-negative pathogens and performed molecular dynamics simulations on selected representatives. Combined structural and dynamical analyses revealed a strong correlation between B-loop architecture and carbapenemase activity in the pathogens Klebsiella and Acinetobacter, particularly regarding loop length and spatial organization. These findings emphasize the B-loop’s critical contribution, in concert with the P- and Ω-loops, in tuning active site versatility, substrate recognition, catalytic activity, and structural stability. A deeper understanding of how these motifs and loops govern DBL function may inform the development of novel antibiotics and inhibitors targeting this class of enzymes.

antibiotic resistance↗
DOE OSTI2024

Cosmology Large Angular Scale Surveyor (CLASS): 90 GHz Telescope Pointing, Beam Profile, Window Function, and Polarization Performance

The Cosmology Large Angular Scale Surveyor (CLASS) is a telescope array that observes the cosmic microwave background (CMB) over ∼75% of the sky from the Atacama Desert, Chile, at frequency bands centered near 40, 90, 150, and 220 GHz. CLASS measures the large angular scale CMB polarization to constrain the tensor-to-scalar ratio and the optical depth to last scattering. This paper presents the optical characterization of the 90 GHz telescope. Observations of the Moon establish the pointing while dedicated observations of Jupiter are used for beam calibration. The standard deviations of the pointing error in azimuth, elevation, and boresight angle are 1$^{'}_.$3, 2$^{'}_.$1, and 2$^{'}_.$0, respectively, over the first 3 yr of observations. This corresponds to a pointing uncertainty ∼7% of the beam’s full width at half-maximum (FWHM). The effective azimuthally symmetrized instrument 1D beam estimated at 90 GHz has an FWHM of 0$^°_.$620 ± 0$^°_.$003 and a solid angle of 138.7 ± 0.6(stats.) ± 1.1(sys.) μsr integrated to a radius of 4°. The corresponding beam window function drops to $b^2_ℓ$ = 0.93, 0.71, 0.14 at ℓ = 30, 100, 300, respectively. Far-sidelobes are studied using detector-centered intensity maps of the Moon and measured to be at a level of 10 −3 or below relative to the peak. The polarization angle of Tau A estimated from preliminary survey maps is 149°.6 ± 0°.2(stats.) in equatorial coordinates. The instrumental temperature-to-polarization (T → P) leakage fraction, inferred from per-detector demodulated Jupiter scan data, has a monopole component at the level of 1.7 × 10 −3 , a dipole component with an amplitude of 4.3 × 10 −3 , with no evidence of quadrupolar leakage.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

HLA Class I Supertype Classification Based on Structural Similarity

HLA class I proteins, a critical component in adaptive immunity, bind and present intracellular Ags to CD8+ T cells. The extreme polymorphism of HLA genes and associated peptide binding specificities leads to challenges in various endeavors, including neoantigen vaccine development, disease association studies, and HLA typing. Supertype classification, defined by clustering functionally similar HLA alleles, has proven helpful in reducing the complexity of distinguishing alleles. However, determining supertypes via experiments is impractical, and current in silico classification methods exhibit limitations in stability and functional relevance. In this study, by incorporating three-dimensional structures we present a method for classifying HLA class I molecules with improved breadth, accuracy, stability, and flexibility. Critical for these advances is our finding that structural similarity highly correlates with peptide binding specificity. The new classification should be broadly useful in peptide-based vaccine development and HLA–disease association studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Multi-class decision system for categorizing industrial asset attack and fault types

According to some embodiments, a plurality of monitoring nodes may each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. A node classifier computer, coupled to the plurality of monitoring nodes, may receive the series of current monitoring node values and generate a set of current feature vectors. The node classifier computer may also access at least one multi-class classifier model having at least one decision boundary. The at least one multi-class classifier model may be executed and the system may transmit a classification result based on the set of current feature vectors and the at least one decision boundary. The classification result may indicate, for example, whether a monitoring node status is normal, attacked, or faulty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Age class based arbitration

A system, includes a plurality of sub-queues. Each sub-queue is assigned to an age class of a sequence of age classes. A set of age thresholds divides the sub-queues. A queue manager places a received transaction into a sub-queue based on a comparison of an age of the received transaction to the set of age thresholds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Class 2 CRISPR/Cas compositions and methods of use

Provided are compositions and methods that include one or more of: (1) a Class 2 CRISPR/Cas effector protein, a nucleic acid encoding the effector protein, and/or a modified host cell comprising the effector protein (and/or a nucleic acid encoding the same); (2) a CRISPR/Cas guide RNA that binds to and provides sequence specificity to the Class 2 CRISPR/Cas effector protein, a nucleic acid encoding the CRISPR/Cas guide RNA, and/or a modified host cell comprising the CRISPR/Cas guide RNA (and/or a nucleic acid encoding the same); and (3) a CRISPR/Cas transactivating noncoding RNA (trancRNA), a nucleic acid encoding the CRISPR/Cas trancRNA, and/or a modified host cell comprising the CRISPR/Cas trancRNA (and/or a nucleic acid encoding the same).

Doudna, Jennifer A.↗
DOE OSTI2022

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗
DOE OSTI2023

A pulsed current-mode class-D low-voltage high-bandwidth power amplifier for portable NMR systems

Low-field NMR has seen growing interest in recent years, especially for portable applications. The lower homogeneity magnets used for portable applications require short RF pulses to ensure enough transmit bandwidth to excite the sample volume and also support short echo periods. Furthermore, the preferred use of a high-Q coil to improve signal-to-noise ratio (SNR) prolongs the pulse transients. Thus, at such low Larmor frequencies, the excitation pulse transients become comparable or longer than the pulse length, such that the transmit bandwidth begins to limit measurement SNR. This paper describes the design of a pulsed current-mode class-D power (PCMCD) transmitter that addresses this issue by generating high power in a tuned sample coil while maintaining short transients, thus resulting in high output bandwidth. The transmitter also uses a charge recycling mechanism to maximize power efficiency for RF train excitation, which also results in faster pulse repetition rate and reduces allowable echo time. In this work, experimental results from a small form-factor PCMCD transmitter are presented. This design generates a peak RF power of 240 W into a 9.16 µH coil at 4 MHz while operating off a single 12 V power supply. NMR measurement results using the transmitter are also described, showing minimum achievable echo time of 70 µs and 25 µs depending on the transmitter mode of operation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

1 × 1 km maps of abundances of eight enzyme functional classes for soil C, N, and P cycling across the CONUS

This dataset includes eight 1 × 1 km maps of the abundances of eight enzyme functional classes (EFC) for soil C, N, and P cycling across the CONUS. These mappings are predicted by the machine learning model trained using metagenomics and the corresponding environmental data. This item corresponds to our article: Fan, C., Song, Y., Mishra, U., Gautam, S., & Mayes, M. A. (2025). Harnessing the Power of Machine Learning and Omics to Identify Environmental Regulation on Microbial Functional Composition for Soil C, N, and P Cycling. Journal of Geophysical Research: Biogeosciences, 130(10).

1 × 1 km↗
DOE OSTI2022

8-Plate Multi-Resonant Coupling Using a Class-E 2 Power Converter for Misalignments in Capacitive Wireless Power Transfer

Misalignment is a common issue in wireless power transfer systems. It shifts the resonant frequency away from the operating frequency that affects the power flow and efficiency from the charging station to the load. This work proposes a novel capacitive wireless power transfer (CPT) using an 8-plate multi-resonant capacitive coupling to minimize the effect of misalignments. A single-active switch class-E 2 power converter is utilized to achieve multi-resonance through the selection of different resonant inductors. Simulations show a widening of the resonant frequency band which offers better performance than a regular 4-plate capacitive coupling for misalignments. The hardware results of the 8-plate multi-resonant coupling show an efficiency of 88.5% for the 20.8 W test, which is 18.3% higher than that of the regular 4-plate coupling. Because of the wider resonant frequency band {455–485 kHz}, compared with the regular 4-plate coupling, the proposed design minimized the output voltage drop by 15% for a 10% misalignment. Even for large misalignments, the 8-plate performance improved by 40% compared with the 4-plate coupling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Unsupervised Detection of SOC Spoofing in OCPP 2.0.1 EV Charging Communication Protocol Using One-Class SVM

The electric vehicles (EVs) market keeps growing globally; thus, it is critical to secure the EV charging communication protocols in order to guarantee reliable and fair charging operations among the customers. The Open Charge Point Protocol (OCPP) 2.0.1 supports the communication between the Electric Vehicle Supply Equipment (EVSE) and Charging Station Management Systems (CSMSs); therefore, it becomes vulnerable to several types of attacks, which aim to jeopardize smart charging, billing, and energy management. Specifically, OCPP 2.0.1 allows the self-reporting of the State of Charge (SOC) values, which makes it vulnerable to spoofing-based cyberattacks, which target manipulating the scheduling priorities, distorting the load forecasts, and extending the charging sessions in an unfair manner. In this paper, we try to address this type of attack by providing a comprehensive analysis of the SOC spoofing attacks and introducing a novel unsupervised detection framework based on the One-Class Support Vector Machine (OCSVM) algorithm. Specifically, two types of attack scenarios are analyzed (i.e., priority manipulation and session extension) by deriving engineered features that capture the nonlinear relationships under normal charging behavior. Detailed simulation-based results are derived by utilizing the DESL-EPFL Level 3 EV charging dataset. Our results demonstrate high F1-score and recall in identifying spoofed SOC values and that the proposed OCSVM model demonstrates superior performance compared to alternative clustering and deep-learning based detectors.

EV charging↗
DOE OSTI2021

A Novel Glutathione S-Transferase Gtt2 Class (VpGSTT2) Is Found in the Genome of the AHPND/EMS Vibrio parahaemolyticus Shrimp Pathogen

Glutathione S-transferases are a family of detoxifying enzymes that catalyze the conjugation of reduced glutathione (GSH) with different xenobiotic compounds using either Ser, Tyr, or Cys as a primary catalytic residue. We identified a novel GST in the genome of the shrimp pathogen V. parahaemolyticus FIM- S1708+, a bacterial strain associated with Acute Hepatopancreatic Necrosis Disease (AHPND)/Early Mortality Syndrome (EMS) in cultured shrimp. This new GST class was named Gtt2. It has an atypical catalytic mechanism in which a water molecule instead of Ser, Tyr, or Cys activates the sulfhydryl group of GSH. The biochemical properties of Gtt2 from Vibrio parahaemolyticus (VpGSTT2) were characterized using kinetic and crystallographic methods. Recombinant VpGSTT2 was enzymatically active using GSH and CDNB as substrates, with a specific activity of 5.7 units/mg. Low affinity for substrates was demonstrated using both Michaelis–Menten kinetics and isothermal titration calorimetry. The crystal structure showed a canonical two-domain structure comprising a glutathione binding G-domain and a hydrophobic ligand H domain. A water molecule was hydrogen-bonded to residues Thr9 and Ser 11, as reported for the yeast Gtt2, suggesting a primary role in the reaction. Molecular docking showed that GSH could bind at the G-site in the vicinity of Ser11. G-site mutationsT9A and S11A were analyzed. S11A retained 30% activity, while T9A/S11A showed no detectable activity. VpGSTT2 was the first bacterial Gtt2 characterized, in which residues Ser11 and Thr9 coordinated a water molecule as part of a catalytic mechanism that was characteristic of yeast GTT2. The GTT2 family has been shown to provide protection against metal toxicity; in some cases, excess heavy metals appear in shrimp ponds presenting AHPND/EMS. Further studies may address whether GTT2 in V. parahaemolyticus pathogenic strains may provide a competitive advantage as a novel detoxification mechanism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Decision Tree Regression to Identify Representative Road Sections for Evaluating Performance of Connected and Automated Class 8 Tractors

Currently, connected and autonomous vehicle (CAV) technology is being developed for Class 8 tractor trucks aimed at improved safety and fuel economy and reduced CO2 emissions. Despite extensive efforts conducted across the world, the reported efficiency gains were varied from different research groups, raising concerns about the fidelity of models, the performance of control, and the effectiveness of the experimental validation. One root cause for this variation stems from the fact that the efficiency gain obtained from the CAV is sensitive to real-world conditions, including surrounding traffic and road grade. This study presents an approach aimed at identifying representative public road sections and facilitating CAV research from this perspective. By employing the decision tree regression (DTR) method to the Fleet DNA database, the most representative road sections can be identified. High-level metrics and detailed information of the derived road sections are also illustrated and discussed, which demonstrate their representativeness and the effectiveness of the approach. Meanwhile, the capability of this approach can be easily extended by integrating specific constraints into the DTR algorithm. As an example, a specific representative road section with an aggressive road grade profile was also provided via this approach.

27 ARPA - Advanced Research Projects Agency-Energy↗