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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Generalized multifractality at spin quantum Hall transition

Generalized multifractality characterizes scaling of eigenstate observables at Anderson-localization critical points. We explore generalized multifractality in 2D systems, with the main focus on the spin quantum Hall (SQH) transition in superconductors of symmetry class C. Relations and differences with the conventional integer quantum Hall (IQH) transition are also studied. Using the field-theoretical formalism of non-linear sigma-model, we derive the pure-scaling operators representing generalizing multifractality and then “translate” them to the language of eigenstate observables. Performing numerical simulations on network models for SQH and IQH transitions, we confirm the analytical predictions for scaling observables and determine the corresponding exponents. Remarkably, the generalized-multifractality exponents at the SQH critical point strongly violate the generalized parabolicity of the spectrum, which implies violation of the local conformal invariance at this critical point.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Hierarchical-embedding autoencoder with a predictor as efficient architecture for learning time-evolution in multi-scale turbulent flows

We introduce a scale-aware, data-driven deep learning modeling framework for accurately predicting the time evolution of multi-scale turbulent plasma and liquid flows. The approach is motivated by the idea of scale separation. Structures of vastly different length scales emerge in these systems, and interactions between these structures occur only locally. To exploit this structure, the flow state is transformed by a hierarchical, fully convolutional autoencoder, not into a single embedding layer as in conventional convolutional surrogate models, but into a series of embedding layers. A stepwise training strategy ensures that fine-scale features are encoded on a high-resolution grid, while larger structures are represented on progressively coarser layers. The time evolution predictor advances all embedding layers in sync, capturing local interactions between features at the same scale as well as between all scales. This approach enables efficient modeling of multi-scale systems since negligible interactions between distant, small-scale structures do not need to be directly modeled. Our hierarchical-embedding autoencoder with a predictor framework is evaluated on canonical examples of multi-scale turbulence: two-dimensional Kolmogorov flow and Hasegawa–Wakatani plasma turbulence. In both cases, the proposed framework significantly improves predictive accuracy relative to conventional convolutional network architectures. A significant improvement in prediction accuracy was observed for crucial statistical characteristics of the Hasegawa–Wakatani plasma as well as for individual trajectories of the Kolmogorov flow turbulence. Importantly, the model's rollout for the Hasegawa–Wakatani problem demonstrates a four-order-of-magnitude speedup compared to traditional numerical solvers.

Khrabry, Alexander I. [Princeton Univ., NJ (United↗
DOE OSTI2024

An Approach to Realize Generalized Optimal Motion Primitives Using Physics Informed Neural Networks

Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. Here, this work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

SGR 1900+14

SGR 1900+14 soft gamma-ray repeater has entered an extraordinary new phase of activity with a previously unobserved frequency and intensity of bursts. Activity began on May 26 and has continued at least through May 30. Triangulation with Ulysses and BATSE gives a preliminary annulus whose center is at R.A. = 2lh58ml8s, Decl. = -11ol8'.4 (equinox 2000.0), and whose radius is 47.269 +/- 0.019 deg. This annulus includes the network synthesis error box (Hurley et al. 1994, Ap.J. 431, L31), the ROSAT source proposed as a possible quiescent x-ray counterpart to the soft repeater, and the peculiar double M star system coincident with the ROSAT source. As all these locations are mutually consistent, we conclude that the ROSAT source and M star system are indeed the counterparts to the soft gamma-ray repeater, and urge that observations at other wavelengths be undertaken immediately, while the source remains active. An image may be found at http://ssl.berkeley.edu/ipn3/sgrl9OO+l4/. On May 30, BATSE triggered on an extremely intense outburst from this source that comprised at least 38 bursts of varying intensity and duration; the series of bursts started at May 30.37759 UT and lasted for about 350 s. The peak flux of each of the three major bursts in the series is 1.1 x lOE-4 erg cmE-2 sE-1 (between 25 and 300 keV, integrated for 128 ms). Assuming a distance of about 12 kpc for SGR 1900+14, this corresponds to a luminosity of about 2 X 10(exp 42) ergs / second for each burst. This bunching of emission is very similar to the behavior of SGR 1806-20 as detected with the Rossi X-ray Timing explorer in 1996. Overall, between May 26 and June 1, we have detected over 50 bursts from SGR 1900+4. The frequency and intensity of emissions are unlike any previously seen behavior from this source. Follow-up observations at infrared and radio wavelengths, while the source is still active are encouraged.

Hurley, K.↗
NASA NTRS2002

MODIS and AERONET Characterization of the Global Aerosol

Recently produced daily MODIS aerosol data for the whole year of 2001 are used to show the concentration and dynamics of aerosol over ocean and large parts of the continents. The data were validated against the Aerosol Robotic Network (AERONET) measurements over land and ocean. Monthly averages and a movie based on the daily data are produced and used to demonstrate the spatial and temporal evolution of aerosol. The MODIS wide spectral range is used to distinguish fine smoke and pollution aerosol from coarse dust and salt. The movie produced from the MODIS data provides a new dimension to aerosol observations by showing the dynamics of the system. For example in February smoke and dust emitted from the Sahel and West Africa is shown to travel to the North-East Atlantic. In April heavy dust and pollution from East Asia is shown to travel to North America. In May-June pollution and dust play a dynamical dance in the Arabian Sea and Bay of Bengal. In Aug-September smoke from South Africa and South America is shown to pulsate in tandem and to periodically to be transported to the otherwise pristine Southern part of the Southern Hemisphere.

Kaufman, Yoram↗
NASA NTRS2000

Robust detection of ionospheric irregularities

The approach outlined in this paper conservatively bounds the ionospheric errors even for the worst observed ionospheric conditions to date, using data sets taken from the operational receivers in the WAAS reference station network.

Wide Area Augmentation System (WAAS) ionosphere ir↗
NASA NTRS2008

The NASA Orbiting Carbon Observatory (OCO) Mission: Objectives, Approach, and Status

The Orbiting Carbon Observatory (OCO) is a NASA Earth System Science Pathfinder (ESSP) mission that is currently under development at the Jet Propulsion Laboratory (JPL). OCO will make global, space-based measurements of atmospheric carbon dioxide (CO2) with the precision, resolution, and coverage needed to characterize regional-scale sources and sinks of this important greenhouse gas. The observatory consists of a dedicated spacecraft bus that carries a single instrument. The bus employs single-string version of Orbital Sciences Corporation (OSC) LEOStar-2 architecture. This 3-axis stabilized bus includes a propulsion system for orbit insertion and maintenance, provides power, points the instrument, receives and processes commands from the ground, and records, stores, and downlinks science and engineering data. The OCO instrument incorporates 3 oboresighted, high resolution grating spectrometers that will make coincident measurements of reflected sunlight in near-infrared CO2 and molecular oxygen (O2) bands. The instrument was designed and manufactured by Hamilton Sundstrand (Pomona, CA), and then integrated, flight qualified, and calibrated by JPL. It is scheduled for delivery to OSC (Dulles, VA) for integration with the spacecraft bus in the spring of 2008. OCO will be launched from the Vandenberg Air Force Base on a dedicated OSC Taurus XL launch vehicle in December 2008. It will fly in formation with the Earth Observing System Afternoon Constellation, a group of satellites that files in a 98.8 minute, 705 km altitude, sun-synchronous orbit. This orbit provides coverage of the sunlit hemisphere with a 16-day ground track repeat cycle. OCO will fly approx.4 minutes ahead of the EOS Aqua platform, with an ascending nodal crossing time of approx.1:26 PM. The OCO science data will be transmitted to the NASA Ground Network Stations in Alaska and Virginia, and then transferred to the OCO Ground Data System at JPL. There, the CO2 and O2 spectra will be analyzed by the OCO Science Team to provide spatially resolved estimates of the column-averaged CO2 dry air mole fraction, XCO2. These measurements are expected to improve our understanding of the nature and processes that regulate atmospheric CO2 enabling more reliable forecasts of CO2 buildup and its impact on climate change.

global measurements↗
NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗
DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Renewable-battery hybrid power plants in congested electricity markets: Implications for plant configuration

Examining coupled renewable-battery power plants (“hybrids”) in congested areas provides insights into a future of increased wind and solar penetration. Our study focuses on two types of congested regions, Variable Renewable Energy (VRE)-rich Areas and Load Centers, and explores likely plant configuration choices for developers and transmission network planners. Here, this paper examines how hybrid value, comprising energy and capacity value, varies by plant configuration and congested region type considering factors such as storage duration, battery degradation, and ability to charge from the grid. We select plant locations from across the seven main U.S. independent system operators (ISOs). Hybrid value for each configuration is computed based on profit-maximizing plant operation given perfect foresight, according to observed wholesale power market real time prices from 2018 to 2021. In VRE-rich Areas, the median increase in energy value from extending storage duration from one to 4h is 29.4% for solar and 26.8% for wind, assuming low battery degradation costs and storage sized to 100% of the plant's nameplate generation capacity. Increasing storage duration beyond 4h does not substantially increase its value from energy markets, even in VRE-rich Areas. We find that solar hybrids reach a 90% capacity credit with 4h of storage, while wind hybrids require 8h of storage, based on the capacity factor of each hybrid during the top 100 net load hours.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1988

Planetary submillimeter spectroscopy

The aim is to develop a comprehensive observational and analytical program to study solar system physics and meterology by measuring molecular lines in the millimeter and submillimeter spectra of planets and comets. A primary objective is to conduct observations with new JPL and Caltech submillimeter receivers at the Caltech Submillimeter Observatory (CSO) on Mauna Kea, Hawaii. A secondary objective is to continue to monitor the time variable planetary phenomena (e.g., Jupiter and Uranus) at centimeter wavelength using the NASA antennas of the Deep Space Network (DSN).

Klein, M. J.↗
NASA NTRS2014

The International DORIS Service (IDS) - Recent Developments in Preparation for ITRF2013

The International DORIS Service (IDS) was created in 2003 under the umbrella of the International Association of Geodesy (IAG) to foster scientific research related to the French DORIS tracking system and to deliver scientific products, mostly related to the International Earth rotation and Reference systems Service (IERS). We first present some general background related to the DORIS system (current and planned satellites, current tracking network and expected evolution) and to the general IDS organization (from Data Centers, Analysis Centers and Combination Center). Then, we discuss some of the steps recently taken to prepare the IDS submission to ITRF2013 (combined weekly time series based on individual solutions from several Analysis Centers). In particular, recent results obtained from the Analysis Centers and the Combination Center show that improvements can still be made when updating physical models of some DORIS satellites, such as Envisat, Cryosat-2 or Jason-2. The DORIS contribution to ITRF2013 should also benefit from the larger number of ground observations collected by the last generation of DGXX receivers (first instrument being onboard Jason-2 satellite). In particular for polar motion, sub-millarcsecond accuracy seems now to be achievable. Weekly station positioning internal consistency also seems to be improved with a larger DORIS constellation.

tracking↗
DOE OSTI2020

Interfacial Electromechanics Predicts Phase Behavior of 2D Hybrid Halide Perovskites

Quasi-two-dimensional (2D) mixed-cation hybrid halide perovskites (A' 2 A N–1 M N X 3N+1 ; A' = large organic molecule with cationic group, A = [Cs + , CH 3 NH 3 + , HC(NH 2 ) 2 + ], M = [Pb, Sn, Ge], X = [I – , Br – , Cl – ]) have rapidly emerged as candidates to improve the structural stability and device lifetime of 3D perovskite semiconductor devices under operating conditions. The addition of the large A' cation to the traditional AMX 3 structure introduces several synthetic degrees of freedom and breaks M–X bonds, giving rise to peculiar critical phase behavior in the phase space of these complex materials. In this work, we propose a thermodynamic model parametrized by first-principles calculations to generate the phase diagram of 2D and quasi-2D perovskites (q-2DPKs) based on the mechanics and electrostatics of the interface between the A' cations and the metal halide octahedral network. Focusing on the most commonly studied methylammonium lead iodide system where A' is n-butylammonium (BA; CH 3 (CH 2 ) 3 NH 3 + ), we find that the apparent difficulty in synthesizing phase-pure samples with a stoichiometric index N > 5 can be attributed to the energetic competition between repulsion of opposing interfacial dipole layers and mechanical relaxation induced by interfacial stress. Our model shows quantitative agreement with experimental observations of the maximum phase-pure stoichiometric index (N crit ) and explains the nonmonotonic evolution of the lattice parameters with increasing stoichiometric index (N). This model is generalizable to the entire family of q-2DPKs and can guide the design of photovoltaic and optical materials that combine the structural stability of the q-2DPKs while retaining the charge carrier properties of their 3D counterparts.

14 SOLAR ENERGY↗
DOE OSTI2020

The Spatiotemporal Evolution of Granular Microslip Precursors to Laboratory Earthquakes

Laboratory earthquake experiments provide important observational constraints for our understanding of earthquake physics. Here we leverage continuous waveform data from a network of piezoceramic sensors to study the spatial and temporal evolution of microslip activity during a shear experiment with synthetic fault gouge. We combine machine learning techniques with ray theoretical seismology to detect, associate, and locate tens of thousands of microslip events within the gouge layer. Microslip activity is concentrated near the center of the system but is highly variable in space and time. While microslip activity rate increases as failure approaches, the spatiotemporal evolution can differ substantially between stick-slip cycles. These results illustrate that even within a single, well-constrained laboratory experiment, the dynamics of earthquake nucleation can be highly complex.

58 GEOSCIENCES↗
DOE OSTI2021

Generating synthetic occupants for use in building performance simulation

Occupant behaviour simulation frameworks can employ synthetic populations to characterize occupancy and behavioural patterns in buildings based on observed demographic data at a certain geographical location. For buildings, very few synthetic occupant populations have been generated. This paper uses a Bayesian Networks (BN) structural learning approach to synthesize populations of occupants in a multi-family housing case study. Two additional cases of office occupants and senior housing residents are considered as a cross-case comparison. Furthermore, we draw upon the extended version of drivers-needs-actions-systems (DNAS) framework to guide the selection of variables and data imputation. Our results show that the BN approach is powerful in learning the structure of data sets. The synthetic data sets successfully match the joint distributions of the underlying combined data sets. Experiments on the multi-family housing particularly show better performance than the office and senior housing cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1976

Monitoring of crustal movements in the San Andreas fault zone by a satellite-borne ranging system

The Close Grid Geodynamic Measurement System is conceived as an orbiting ranging device with a ground base grid of reflectors or transponders (spacing 1.0 to 30 km), which are projected to be of low cost (maintenance free and unattended), and which will permit the saturation of a local area to obtain data useful to monitor crustal movements in the San Andreas fault zone. The system includes a station network of 75 stations covering an area between 36 deg N and 38 deg N latitudes, and 237 deg E and 239 deg E longitudes, with roughly half of the stations on either side of the faults. In addition, the simulation of crustal movements through the introduction of changes in the relative positions between grid stations, weather effect for intervisibility between satellite and station and loss of observations thereof, and comparative evaluation of various observational scheme-patterns have been critically studied.

Kumar, M.↗
NASA NTRS2002

Biomass-burning Aerosols in South East-Asia: Smoke Impact Assessment(BASE-ASIA)

Biomass burning has been a regular practice for land clearing and land conversion in many countries, especially in Africa, South America, and South East Asia. Significant global sources of greenhouse gases (e.g., CO2, CH4), chemically active gases (e.g., NO, CO, HC, CH3Br), and atmospheric aerosols are produced by biomass-burning processes, which influence the Earth-atmosphere energetics and hence impact both global climate and tropospheric chemistry. Some gases and aerosols can serve as active cloud condensation nuclei, which play important role in determining the net radiation budget, precipitation rate, and cloud lifetime. Biomass burning also affects the biogeochemical cycling of nitrogen and carbon compounds from the soil to the atmosphere; the hydrological cycle (i.e., run off and evaporation); the reflectivity and emissivity of the land; and the stability of ecosystems and ecosystem biodiversity. Compared to Africa and South America, the climatology in South East Asia reveals quite different characteristics, showing distinct large-scale smoke and cloud sources and interaction regimes. The fresh water distribution in this region is highly dependent on monsoon rainfall; in fact, the predictability of the tropical climate system is much reduced during the boreal spring, which is associated with the peak season of biomass burning activities. Estimating the burning fuel (e.g., bark, branches, and wood), an important part of studying regional carbon cycle, may rely on utilizing a wide range of distinctive spectral features in the shortwave and longwave regions. Therefore, to accurately assess the impact of smoke aerosols in this region requires continuous observations from satellites, aircraft, networks of ground-based instruments and dedicated field experiments. A new initiative will be proposed and discussed.

Tsay, Si-Chee↗