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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sensitivity of geophysical techniques for monitoring secondary CO 2 storage plumes

For geologic carbon storage, the ability to detect secondary CO 2 plumes—defined as those CO 2 plumes accumulating outside the intended storage reservoir—is fundamental to preventing unexpected CO 2 migration into groundwater resources and for risk and liability management. Understanding the sensitivity of various geophysical methods to secondary plumes is crucial for designing cost-effective monitoring schemes. We use several modeling scenarios to demonstrate the process of assessing sensitivities and detection thresholds of three primary geophysical techniques—surface seismic, borehole-to-surface electromagnetic (EM), and surface and borehole gravity—for early detection of secondary CO 2 plumes in the post-injection phase. While seismic reflection methods are often considered in monitoring strategies to track the evolution of CO 2 plumes, they are also the most expensive. Due to cost considerations, especially for long-term post-injection monitoring, other techniques complement seismic monitoring when designing an adaptive monitoring network. Borehole-to-surface EM or surface gravity surveys are feasible for time-lapse monitoring of deep secondary CO 2 plumes. Furthermore, these surveys could be carried at intervals defined by site-specific conditions. If time-lapse EM and/or gravity surveys detect any signal responses beyond the expected change, it would trigger a need for the higher resolution seismic survey.

58 GEOSCIENCES↗
DOE OSTI2025

Active Learning of Microgrid Frequency Dynamics Using Neural Ordinary Differential Equations

Accurate frequency modelling of inverter‐based resource (IBR)‐dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low‐inertia characteristics and fast dynamics that traditional swing equation‐based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data‐driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python‐based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the 10 -6 p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real‐time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.

Aryal, Tara [South Dakota State Univ., Brookings, ↗
DOE OSTI2026

Application of machine learning techniques for fast MeV x-ray spectra unfolding from filter stack spectrometer data

Recovery of MeV x-ray spectra from detector signals is difficult because the response matrix inversion is ill-conditioned and current methods are too slow for high-repetition-rate experiments. In this work, we make use of neural networks to unfold MeV x-ray spectra from measurements obtained with a filter stack spectrometer at rates of near 40 Hz. The neural network was trained on synthetic data and tested on both synthetic and experimental data, the latter obtained in two separate experiments performed at the Omega EP laser facility. We show here that this unfolding method has good performance on synthetic data and that it is a promising option for experimental data of up to 40 MeV. The accuracy on experimental data is verified by using a simple forward model to compare against measured values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Discrimination between individual dust and bioparticles using aerosol time-of-flight mass spectrometry

Ice nucleating particles (INPs) impact cloud properties and precipitation processes through their ability to trigger cloud glaciation. Dust and bioparticles are two important sources of INPs that have markedly different atmospheric loadings and ice nucleating efficiencies. In situ identification of the sources of INPs in clouds has been accomplished using single particle mass spectrometry (SPMS). However, external mixtures of dust and bioparticles present a unique challenge as they have overlapping mass spectral ion signatures, complicating their unambiguous identification. This study presents a detailed discussion of dust and bioparticle SPMS signatures, uniting data from a broad array of studies. As emphasized, the ion signals from both dust and bioparticles are highly sensitive to ionization conditions. To understand the observed variations, we characterize the mass spectral dependence of distinct dust and bioparticle samples using total positive ion intensity (TPII) as an indicator of the laser pulse energy each particle encountered. Through this analysis, a broad range of characteristic biogenic low intensity ion peaks that may be useful to distinguish bioparticles from dust became apparent and are highlighted. Insights informed by this analysis were utilized to identify bioparticles in ambient SPMS data. Ambient particles exhibiting both dust and characteristic biogenic spectral fingerprints were excluded from the bioparticle classification. Although bioparticles only made up 0.2% of all sampled particles, their abundance was moderately correlated with INP concentrations measured at 15 °C.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗
DOE OSTI2022

Online Power System Event Detection via Bidirectional Generative Adversarial Networks

Accurate and speedy detection of power system events is critical to enhancing the reliability and resiliency of power systems. Although supervised deep learning algorithms show great promise in identifying power system events, they require a large volume of high-quality event labels for training. This paper develops a bidirectional anomaly generative adversarial network (GAN)-based algorithm to detect power system events using streaming PMU data, which does not rely on a huge amount of event labels. By introducing conditional entropy constraint in the objective function of GAN and graph signal processing-based PMU sorting technique, our proposed algorithm significantly outperforms state-of-the-art event detection algorithms in terms of accuracy. To facilitate the adoption of the proposed algorithm, a prototype online platform is also developed using Apache Hadoop, Kafka, and Spark to enable real-time event detection. Here, the accuracy and computational efficiency of the proposed algorithm are validated using a large-scale real-world PMU dataset from the Eastern Interconnection of the United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Priority-Based Control Strategy and Performance Bound for Aggregated HVAC-Based Load Shaping

Air conditioning systems have been recognized as a potential, cost-effective resource for shaping electrical load in a power system. This work presents a new priority-based control strategy that explicitly addresses three requirements for using a collection of air conditioning units in this capacity. These are 1) tracking a regulation signal, 2) maintaining the building temperature near its set point, and 3) satisfying a cycling constraint of the air conditioners. After introducing the control strategy, we derive a bound for the regulation signal within which tracking is guaranteed while also satisfying the temperature requirement. These results are illustrated with simulations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

OsWRKY21 and OsWRKY108 function redundantly to promote phosphate accumulation through maintaining the constitutive expression of OsPHT1;1 under phosphate‐replete conditions

Summary Plant Phosphate Transporter 1 (PHT1) proteins, probably the only influx transporters for phosphate (Pi) uptake, are partially degraded on sufficient Pi levels to prevent excessive Pi accumulation. Therefore, the basal/constitutive expression level of PHT1 genes is vital for maintaining Pi uptake under Pi‐replete conditions. Rice ( Oryza sativa ) OsPHT1;1 is a unique gene as it is highly expressed and not responsive to Pi, however the mechanism for maintaining its basal/constitutive expression remains unknown. Using biochemical and genetic approaches, we identified and functionally characterised the transcription factors maintaining the basal/constitutive expression of OsPHT1;1 . OsWRKY21 and OsWRKY108 interact within the nucleus and both bind to the W‐box in the OsPHT1;1 promoter. Overexpression of OsWRKY21 or OsWRKY108 led to increased Pi accumulation, resulting from elevated expression of OsPHT1;1 . By contrast, oswrky21 oswrky108 double mutants showed decreased Pi accumulation and OsPHT1;1 expression in a Pi‐dependent manner. Moreover, similar to ospht1;1 mutants, plants expressing the OsWRKY21–SRDX fusion protein (a chimeric dominant suppressor) were impaired in Pi accumulation in Pi‐replete roots, accompanied by downregulation of OsPHT1;1 expression. Our findings demonstrated that rice WRKY transcription factors function redundantly to promote Pi uptake by activating OsPHT1;1 expression under Pi‐replete conditions, and represent a novel pathway independent of the central Pi signalling system.

Zhang, Jun↗
DOE OSTI2020

Controlled removal of hydrogen atoms from H-terminated silicon surfaces

The controlled formation of dangling bond structures on a H-terminated silicon surface is the first step in an atomically precise method of fabrication of silicon quantum electronic devices. An ultrahigh vacuum scanning tunneling microscope (STM) tip is used to selectively desorb hydrogen atoms from a Si(100)-2x1:H surface by injecting electrons with the sample held at a positive bias voltage. The authors propose a lithography method that allows the STM to operate under negative bias imaging conditions and simultaneously desorb H atoms as required. A high frequency signal is added to the negative bias voltage to deliver the required energy for hydrogen removal. The resulting current at this frequency and its harmonics are filtered to minimize their effect on the operation of the STM’s feedback control loop. The authors show that the chance of tip-sample crash during the lithography process is reduced by employing this method. They also demonstrate that this approach offers a significant potential for controlled and precise removal of H atoms from a H-terminated silicon surface and thus may be used for the fabrication of practical silicon-based atomic-scale devices.

42 ENGINEERING↗
DOE OSTI2021

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Mitogen-activated protein kinase 6 negatively regulates secondary wall biosynthesis by modulating MYB46 protein stability in Arabidopsis thaliana

The R2R3-MYB transcription factor MYB46 functions as a master switch for secondary cell wall biosynthesis, ensuring the exquisite expression of the secondary wall biosynthetic genes in the tissues where secondary walls are critical for growth and development. At the same time, suppression of its function is needed when/where formation of secondary walls is not desirable. Little is known about how this opposing control of secondary cell wall formation is achieved. We used both transient and transgenic expression of MYB46 and mitogen-activated protein kinase 6 (MPK6) to investigate the molecular mechanism of the post-translational regulation of MYB46. We show that MYB46 is phosphorylated by MPK6, leading to site specific phosphorylation-dependent degradation of MYB46 by the ubiquitin-mediated proteasome pathway. In addition, the MPK6-mediated MYB46 phosphorylation was found to regulate in planta secondary wall forming function of MYB46. Furthermore, we provide experimental evidences that MYB83, a paralog of MYB46, is not regulated by MPK6. The coupling of MPK signaling to MYB46 function provides insights into the tissue- and/or condition-specific activity of MYB46 for secondary wall biosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Clean Grid Vision: A U.S. Perspective - Chapter 5. Global Power Market Trends

The evolution of the electricity sector toward competitive modern markets is occurring over a long period and responding to rapid changes in technologies, business models, and other market factors. No reformed power market anywhere in the world looks the same today as it did a decade ago. Technology has enabled some of the evolution, but perhaps it is more important that people and institutions learn from experience how to make markets better over time. Governments still have an important role—not as the authority controlling prices, but as the authority safeguarding the integrity of the economic processes that create prices in response to supply and demand and ensure reliability and other objectives are met. More VRE resources will change market conditions, but they will not change market fundamentals. Prices provide important signals for new investments and how the grid operates—including new technologies and processes discussed elsewhere in this report. If poor market design or the exercise of market power by a large entity distort these signals, outcomes will cease to be efficient and the public will bear the additional cost. Fundamentally, prices reflect supply, demand, and the cost of technological options.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

CLimate Impact: Determining Etiology thRough pAthways (CLDERA)

Climate impacts have broad economic, health, political, and national security ramifications. Societally relevant impacts are typically farther downstream, are the product of multiple interacting processes, and can arise over small regions and timeframes because their sources are short-term and localized. Short-term forcings (as can be seen in volcanic eruptions, climatic tipping points (e.g., the collapse of rainforests or the disappearance of sea ice), or in increasingly plausible climate interventions) fundamentally possess low signal-to-noise and could benefit from accounting for the multiple conditional processes through which a downstream impact arises. Under the Grand Challenge LDRD CLDERA (CLimate impacts: Discovering Etiology thRough pAthways), we have developed tools to enable downstream impact attribution from geographically and temporally localized source forcings in the climate. CLDERA developed methods that can distinguish how a localized source drives the climate system to respond with particular impacts. The how is embodied in pathways – the spatio-temporally evolving chain of physical processes that connects a source to a series of increasingly distant impacts. Novel analytic methods in pursuit of downstream impact attribution were developed and demonstrated on simulations and observations of the 1991 eruption of Mt. Pinatubo in the Philippines. As described within this report we have • developed stratospheric expertise and aerosol modeling capabilities in E3SM, • created original methods to detect and model pathways from source-to-impact, and • advanced climate attribution through novel methods, cases, and approaches. Further, CLDERA developed a tiered verification process consisting of controlled datasets to prototype, verify, and refine the original method development. CLDERA increased Sandia’s footprint in the climate analytics community and developed new climate collaborations whilst also creating a cadre of climate analysts at Sandia. The products from CLDERA have been extensive with a total of 9 journal articles published, 12 articles submitted and under review, and an additional 8 articles in preparation. We have produced 1750 simulated years and developed 9 code-bases. This report details these accomplishments and serves as a summary of the work completed during the CLDERA Grand Challenge.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1968

Approach guidance planet tracker.

Working breadboard model of planet tracker under various illuminating conditions, discussing hardware and closed loop tracking error analysis accounting for signal to noise effects

Cohen, M. K.↗
NASA NTRS1973

Note on the error signal of block quantizers.

Demonstration that the error signal incurred by block quantizing stationary data is nonstationary under some conditions. Examples are presented which indicate that the mean square error is largest at the block edges. When block quantizers are used to encode pictures at low bit rates, this effect tends to make the block edges visible in the reconstructed picture.

Tasto, M.↗
NASA NTRS1975

Flight investigation of rotor/vehicle state feedback

The feasibility of using control feedback or rotor tip-path-plane motion or body state as a means of altering rotor and fuselage response in a prescribed manner was investigated to determine the practical limitations of in-flight utilization of a digital computer which conditions and shapes rotor flapping and fuselage state information as feedback signals, before routing these signals to the differential servo actuators. The analysis and test of various feedback schemes are discussed. Test results show that a Kalman estimator routine which is based on only the first harmonic contributions of blade flapping yields tip-path-plane coefficients which are adequate for use in feedback systems, at speeds up to 150 kts.

Briczinski, S. J.↗
NASA NTRS1975

Pyrotechnic shock measurement and data analysis requirements

A study of laboratory measurement and analysis of pyrotechnic shock prompted by a discrepancy in preliminary Mariner Jupiter/Saturn shock test data is reported. It is shown that before generating shock response plots from any recorded pyrotechnic event, a complete review of each instrumentation and analysis system must be made. In addition, the frequency response capability of the tape recorder used should be as high as possible; the discrepancies in the above data were due to inadequate frequency response in the FM tape recorders. The slew rate of all conditioning amplifiers and input converters must be high enough to prevent signal distortion at maximum input voltage; amplifier ranges should be selected so that the input pulse is approximately 50% of full scale; the Bessel response type should be chosen for digital shock analysis if antialiasing filters are employed; and transducer selection must consider maximum acceleration limit, mounted resonance frequency, flat clean mounting surfaces, base bending sensitivity, and proper torque.

Albers, L.↗
NASA NTRS1982

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

An examination of limitations, requirements, and precision of the linear multiple-regression technique for quantification of marine environmental parameters is conducted. Both environmental and optical physics conditions have been defined for which an exact solution to the signal response equations is of the same form as the multiple regression equation. Various statistical parameters are examined to define a criteria for selection of an unbiased fit when upwelled radiance values contain error and are correlated with each other. Field experimental data are examined to define data smoothing requirements in order to satisfy the criteria of Daniel and Wood (1971). Recommendations are made concerning improved selection of ground-truth locations to maximize variance and to minimize physical errors associated with the remote sensing experiment.

Whitlock, C. H.↗