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At least 487 records · Page 27

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DOE OSTI2024

Sentinel protist taxa of the McMurdo Dry Valley lakes, Antarctica: a review

High-latitude meromictic lakes such as those in the Antarctic McMurdo Dry Valleys (MDV) harbor aquatic ecosystems dominated by the microbial loop. Within this habitat, which is limited year-round by light and nutrients, protists, or single celled eukaryotes, play outsized roles in the food web as the dominant primary producers and the apex predators. Thus, the MDV lake ecosystem represents an ideal system to study the role of sentinel protist taxa in carbon and nutrient cycling. The perennially ice-covered lakes are part of the McMurdo Long Term Ecological Research (McM LTER; mcmlter.org) established in 1993. In this review we will highlight the diversity and trophic roles of the MDV lake protist community and compare environmental factors driving spatiotemporal patterns in key protist taxa in two lakes within the McM LTER, Lakes Bonney and Fryxell. We will then discuss lessons learned from manipulated experiments on the impact of current and future climate-driven environmental change on sensitive protist taxa. Last, we will integrate knowledge gained from 25 years of lab-controlled experiments on key photosynthetic protists to extend our understanding of the function of these extremophiles within the MDV aquatic food webs. Our research group has studied the distribution and function of the MDV microbial community for nearly two decades, training the next generation of scientists to tackle future problems of these globally significant microbes. This review article will also highlight early career scientists who have contributed to this body of work and represent the future of scientific understanding in the Anthropocene.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

A case study of gravity waves-convective storms interaction 9 May 1979

An analysis is presented of a series of severe storms which occurred in the north central United States on 9 May 1979 and whose spatial distribution and movement correlate well with observed gravity waves. Two gravity wave trains of 2.1-3 mb amplitude, 2.5-3.3 h period and 240-265 km horizontal wavelength were isolated through power spectra analysis and cross-correlation techniques applied to National Weather Service barograph traces. The wave trains propagated in the 200 deg direction, which coincided with the jet axis, with a phase velocity of 20-30 m/s and within a 300 km wide band. The storms were identified on enhanced infrared GOES satellite pictures with the help of radar summaries. These convective systems initially developed in Nebraska and propagated north-northeast at 25 m/s, revealing wave-like characteristics with a separation of 300-400 km. The convective systems were closely linked to the observed wave trains with cell intensity, height and associated rainfall maximized at the wave ridge. One of the two wave trains developed in regions of weak or no convection and appeared to initiate more intense convective clusters downstream from the point of origin. It is shown that the characteristics of the wave trains are consistent with those of gravity waves generated in a region of strong vertical shear associated with the jet. It is suggested that the wave trains continue to extract energy from the basic state all along their track through critical level interaction.

Stobie, J. G.↗

NASA NTRS1990

Network modelling for the Space Station Training Facility

Simulation models for LANs to be used in the proposed Space Station Training Facility, including models developed for Ethernet, Starlan, token ring, and Fiber Distributed Data Interface LANs, are discussed. Results of model runs for the basic configuration, for burst traffic, and for a growth scenario are presented. The PAWS and Network II.5 modeling tools are compared.

Hajare, Ankur R.↗

NASA NTRS1993

Origin of orbital debris impacts on LDEF's trailing surfaces

A model was developed to determine the origin of orbital impacts measured on the training surfaces of LDEF. The model calculates the expected debris impact crater distribution around LDEF as a function of debris orbital parameters. The results show that only highly elliptical, low inclination orbits could be responsible for these impacts. The most common objects left in this type of orbit are orbital transfer stages used by the U.S. and ESA to place payloads into geosynchronous orbit. Objects in this type of orbit are difficult to catalog by the U.S. Space Command; consequently there are independent reasons to believe that the catalog does not adequately represent this population. This analysis concludes that the relative number of cataloged objects with highly elliptical, low inclination orbits must be increased by a factor of 20 to be consistent with the LDEF data.

Kessler, Donald J.↗

NASA NTRS1960

A Summary of Operating Conditions Experienced by Three Military Helicopters and a Mountain-Based Commercial Helicopter

The results of a survey of the flight conditions experienced by three military helicopters engaged in simulated and actual military missions, and a commercial helicopter operated in the mountainous terrain surrounding Denver, CO, are presented. The data, obtained with NASA helicopter VGHN recorders, represent 813 flights or 359 flying hours, and are compared where applicable to previous survey results. The current survey results show that none of the helicopters exceeded the maximum design airspeed. One military helicopter, used for instrument flight training, never exceeded 70 percent of its maximum design airspeed. The rates of climb and descent utilized by the IFR training helicopter and of the mountain-based helicopter were generally narrowly distributed within all the airspeed ranges. The number of landings per hour for all four of the helicopters ranged from 1.6 to 3.3. The turbine-engine helicopter experienced more frequent normal-acceleration increments above a threshold of +/-0.4g (where g is acceleration due to gravity) than the mountain-based helicopter, but the mountain-based helicopter experienced acceleration increments of greater magnitude. Limited rotor rotational speed time histories showed that all the helicopters were operated at normal rotor speeds during all flight conditions.

Connor, Andrew B.↗

DOE OSTI2022

Need for research and training reactors for advanced reactor designs

Full text of publication follows. Research and training reactors have served a valuable role in helping train workforce for currently operating fleet of light water reactors. These research and training reactors have been used in reactor laboratory classes to familiarize the students with such vital concepts as approach to criticality, reactor period, neutron moderation, reactivity, flux distribution and leakage, etc. As the industry moves toward advanced non-light-water reactor designs, it is critical that research and training reactors be developed and deployed at university campuses to help train the new generation of nuclear and non-nuclear engineers who are likely to design, build, and operate these advanced reactors. Among the designs currently being pursued for nuclear power generation include molten salt, sodium cooled, and gas cooled designs, with options for various fuel forms. Thus, industry and DOE in collaboration with academic institutions should devise plans on how to familiarize the next generation of nuclear workforce with hands-on experience necessary for such designs. These research and training reactors will play a vital role in familiarizing the future workforce with hands-on experience with concepts associated with fast spectrum reactors, gas cooled reactors, and other features not associated with light water reactors. In addition to classical nuclear engineering concepts, these advanced research and training reactors can also be used for hands-on training as well as for research on features being considered in the design of GEN-IV reactors: cyber security for digital control room operations, hybrid energy system, hydrogen generation, district heating, autonomous control... (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

High–Resolution Maps of Near–Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter–scale maps of near–surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near–surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k–fold cross validation, and the modeled extents of near–surface permafrost were compared to the observed extents at each site. At–a–site near–surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at–a–site prediction (70%–83%), yet they had greater accuracy when transferred to the non–training site (62%–78%). These models demonstrate the potential for integrating high–resolution spatial data and machine learning models to develop maps of near–surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Solar Dynamic Power System Fault Diagnosis

The objective of this research is to conduct various fault simulation studies for diagnosing the type and location of faults in the power distribution system. Different types of faults are simulated at different locations within the distribution system and the faulted waveforms are monitored at measurable nodes such as at the output of the DDCU's. These fault signatures are processed using feature extractors such as FFT and wavelet transforms. The extracted features are fed to a clustering based neural network for training and subsequent testing using previously unseen data. Different load models consisting of constant impedance and constant power are used for the loads. Open circuit faults and short circuit faults are studied. It is concluded from present studies that using features extracted from wavelet transforms give better success rates during ANN testing. The trained ANN's are capable of diagnosing fault types and approximate locations in the solar dynamic power distribution system.

Momoh, James A.↗

DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗

DOE OSTI2022

Discovering faint and high apparent motion rate near-Earth asteroids using a deep learning program

ABSTRACT Although many near-Earth objects have been found by ground-based telescopes, some fast-moving ones, especially those near detection limits, have been missed by observatories. We developed a convolutional neural network for detecting faint fast-moving near-Earth objects. It was trained with artificial streaks generated from simulations and was able to find these asteroid streaks with an accuracy of 98.7 per cent and a false positive rate of 0.02 per cent on simulated data. This program was used to search image data from the Zwicky Transient Facility (ZTF) in four nights in 2019, and it identified six previously undiscovered asteroids. The visual magnitudes of our detections range from ∼19.0 to 20.3 and motion rates range from ∼6.8 to 24 deg d−1, which is very faint compared to other ZTF detections moving at similar motion rates. Our asteroids are also ∼1–51 m diameter in size and ∼5–60 lunar distances away at close approach, assuming their albedo values follow the albedo distribution function of known asteroids. The use of a purely simulated data set to train our model enables the program to gain sensitivity in detecting faint and fast-moving objects while still being able to recover nearly all discoveries made by previously designed neural networks which used real detections to train neural networks. Our approach can be adopted by any observatory for detecting fast-moving asteroid streaks.

Wang, Franklin (ORCID:0000000344887355)↗

DOE OSTI2023

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

This paper proposes a novel data-driven method for the reliable prediction of the power grid’s post-fault trajectories, i.e., the power grid’s dynamic response after a disturbance or fault. Here, the proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods’ predictive power and uncertainty quantification capability using the New York-New England power grid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Stakeholder Training for IEEE 1547-2018 (Abbreviated Final Technical Report)

This project identifies and makes publicly available educational materials to assist stakeholders in adopting the revised IEEE Std 1547-2018, Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces. The new standard is significantly different than the previous 2003 version, contains new concepts and new technical requirements, and requires educational material on the application of these changes. NREL had a principal role in accelerating the revision process though its chairmanship of the IEEE Std 1547 working group to revise the standard and through technical support provided to the working group. Under this project, NREL and partners compiled and developed publicly available educational materials for IEEE Std 1547-2018, including webinars, white papers, and other resources on related topics.

14 SOLAR ENERGY↗

NASA NTRS2023

A Global Land Cover Training Dataset From 1984 to 2020

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Radost Stanimirova↗

DOE OSTI2022

Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials

Abstract Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and can therefore yield catastrophically incorrect predictions. Established a-posteriori UQ methods, including ensemble methods, the dropout method, the delta method, and various heuristic distance metrics, have limitations such as being computationally challenging for large models due to model re-training. In addition, the uncertainty estimates are often not rigorously calibrated. In this work, we propose combining the distribution-free UQ method, known as conformal prediction (CP), with the distances in the neural network’s latent space to estimate the uncertainty of energies predicted by neural network force fields. We evaluate this method (CP+latent) along with other UQ methods on two essential aspects, calibration, and sharpness, and find this method to be both calibrated and sharp under the assumption of independent and identically-distributed (i.i.d.) data. We show that the method is relatively insensitive to hyperparameters selected, and test the limitations of the method when the i.i.d. assumption is violated. Finally, we demonstrate that this method can be readily applied to trained neural network force fields with traditional and graph neural network architectures to obtain estimates of uncertainty with low computational costs on a training dataset of 1 million images to showcase its scalability and portability. Incorporating the CP method with latent distances offers a calibrated, sharp and efficient strategy to estimate the uncertainty of neural network force fields. In addition, the CP approach can also function as a promising strategy for calibrating uncertainty estimated by other approaches.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗

NASA NTRSDate not supplied

Joint Spectrum Access and Power Control in Air-Air Communications - A Deep Reinforcement Learning Based Approach

This paper considers the dynamic spectrum access and power control problem in a single-hop point-to-point Air-Air Communication Network (AACN). Due to spectrum scarcity, we assume the number of Aircraft-to-Aircraft (A2A) communication links is greater than that of the available channels, such that some communication links need to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the Weighted Sum Spectral Efficiency (WSSE). A distributed and dynamic deep Q learning-based algorithm is proposed to find the optimal solution. Specifically, we design two different policies that are trained by conducting a trial-and-error scheme. Each communication link can achieve the optimal policy by exploiting the local information from its neighbors, and this distributive approach make it scalable to large networks. Finally, our experimental results demonstrate the effectiveness of the proposed solution in various AACN scenarios.

Zhe Wang↗

NASA NTRSDate not supplied

Joint Spectrum Access and Power Control in Air-Air Communications - A Deep Reinforcement Learning Based Approach

Zhe Wang↗

DOE OSTI2024

Training custom light curve models of SN Ia subpopulations selected according to host galaxy properties

ABSTRACT Type Ia supernova (SN Ia) cosmology analyses include a luminosity step function in their distance standardization process to account for an observed yet unexplained difference in the post-standardization luminosities of SNe Ia originating from different host galaxy populations [e.g. high-mass ($M \gtrsim 10^{10} \, {\rm M}_{\odot }$) versus low-mass galaxies]. We present a novel method for including host-mass correlations in the SALT3 (Spectral Adaptive Light curve Template 3) light curve model used for standardizing SN Ia distances. We split the SALT3 training sample according to host-mass, training independent models for the low- and high-host-mass samples. Our models indicate that there are different average Si ii spectral feature strengths between the two populations, and that the average spectral energy distribution of SNe from low-mass galaxies is bluer than the high-mass counterpart. We then use our trained models to perform an SN cosmology analysis on the 3-yr spectroscopically confirmed Dark Energy Survey SN sample, treating SNe from low- and high-mass host galaxies as separate populations throughout. We find that our mass-split models reduce the Hubble residual scatter in the sample, albeit at a low statistical significance. We do find a reduction in the mass-correlated luminosity step but conclude that this arises from the model-dependent re-definition of the fiducial SN absolute magnitude rather than the models themselves. Our results stress the importance of adopting a standard definition of the SN parameters (x0, x1, c) in order to extract the most value out of the light curve modelling tools that are currently available and to correctly interpret results that are fit with different models.

Taylor, G. (ORCID:0000000157563259)↗